Diagnostic Applications of Nuclear Medicine: Breast Cancer

  • Marsha Camilla Lynch
  • Jean H. Lee
  • David A. MankoffEmail author
Living reference work entry


This chapter details the use of radiotracer methods in application to breast cancer. We provide a background on breast cancer incidence and clinical features, followed by an overview of current treatment approaches. We then review the use of radionuclide methods applied to major diagnostic tasks for breast cancer that include detection, staging (locoregional and distant), response to therapy, and surveillance.


Breast cancer Diagnosis Staging Restaging Nuclear medicine [18F]FDG PET/CT Prognosis 





16 Alpha-18F-fluoro-17 beta-estradiol





99mTc-HDP or 99mTc-HMDP

99mTc-hydroxymethylene diphosphonate


99mTc-methylene diphosphonate


American College of Obstetricians and Gynecologists


American College of Radiology


American Cancer Society


Aromatase inhibitor


American Joint Committee on Cancer


Breast-conserving surgery


Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm


Breast cancer type 1 susceptibility protein


Breast cancer type 2 susceptibility protein


BRCA mutation carrier prediction model


Bone scintigraphy


Breast-specific gamma imaging, also known as molecular breast imaging


Checkpoint kinase 2


Complete response


X-ray computed tomography


Ductal carcinoma in situ


Disease-free survival


Extranodal extension


Estrogen receptor


Gene for the HER2 receptor


Fluorescent in situ hybridization


Human epidermal growth factor receptor 2, also known as receptor tyrosine-protein kinase erbB-2, or HER2/neu


Internal mammary


Locally advanced breast cancer


Metastatic breast cancer


Menopausal hormone therapy


Magnetic resonance imaging


Mammalian target of rapamycin


Sodium [18F]fluoride


Neo-adjuvant therapy


National Comprehensive Cancer Network


Negative predictive value


Overall survival


Tumor protein p53, also known as cellular tumor antigen p53, phosphoprotein p53, tumor suppressor p53, antigen NY-CO-13, or transformation-related protein 53 (TRP53)


Partner and localizer of BRCA2, also known as FANCN


Pathologic complete response


Positron emission mammography


Positron emission tomography


Positron emission tomography/computed tomography


Progesterone receptor


Phosphatase and tensin homolog


Response evaluation criteria in solid tumors


Society of Breast Imaging


Selective estrogen receptor modulator


Sentinel lymph node


Single nucleotide polymorphism


Single-photon emission computed tomography


Single-photon emission computed tomography/computed tomography


Standardized uptake value


Triple-negative breast cancer


AJCC staging system based on parameters “T” (tumor status), “N” (lymph node status) and “M” (distant metastasis status)



Epidemiology of the Tumor

Breast cancer is the most common non-skin cancer and the second leading cause of cancer mortality in women [1, 2]. Breast cancer is largely a disease of women (one in eight lifetime risk) with an estimated 231,840 new cases expected to be diagnosed in the USA in 2015 [1]. The incidence of breast cancer in men is ~100 times less than in women, with approximately 2,350 new cases expected in 2015. A sharp 7% decline in incidence of breast cancer occurred among white women between 2002 and 2003 attributable to a decline in the use of menopausal hormone therapy (MHT) after a 2002 report linked the use of combined estrogen plus progestin MHT with an increased risk of breast cancer and coronary heart disease [3]. More recently (data from 2007 to 2011), the incidence rates have been stable in white women and increased slightly to 0.3% in black women [2].

There has been a steady decrease in mortality from breast cancer since 1989, attributed to both improvements in early detection, screening programs initiated in the USA in the early 1980s, and treatment [4, 5]. Despite several randomized control trials demonstrating the mortality benefit of screening, there remains controversy on screening guidelines for average-risk women. New 2015 recommendations from the American Cancer Society (ACS) now suggest yearly screening beginning from 45 through 54 and biennial screening from age 55 to age 74 or expected lifetime of less than 10 years [6]. Other organizations including the Society of Breast Imaging (SBI), American College of Obstetricians and Gynecologists (ACOG), and American College of Radiology (ACR) continue to recommend annual screening beginning at age 40. Just over 40,000 women and 400 men are expected to succumb to the disease in 2015. Mortality rates from the most recent data have slightly favored younger more than older women and white more than black women in recent times [2].

Risk Factors

Risk factors for development of breast cancer are due to one or a combination of genetic/familial, reproductive/hormonal, lifestyle, and environmental factors. Further, some of these factors may be classified as modifiable/external or non-modifiable/internal. The most well-established risk factors for female breast cancer include age, family history and genetics, late first pregnancy, and obesity [7]. Besides obesity, other modifiable factors that contribute to breast cancer risk include physical inactivity, use of MHT, long-term heavy smoking, and alcohol consumption [8, 9]. Other well-established non-modifiable risk factors include long menstrual history (early menarche, late menopause), mantle radiation for Hodgkin’s lymphoma, and inherited gene mutations (e.g., BRCA1/BRCA 2). Factors shown to decrease the risk of breast cancer include breastfeeding for at least a year, increased physical activity, and, in high-risk women, decreasing endogenous estrogens by either inhibition of the estrogen receptor using selective estrogen receptor modulators (SERMs) or blocking estrogen synthesis using aromatase inhibitors (AIs). It is estimated that in some populations of average-risk women, lifestyle risk reduction measures may contribute to up to a 30% decrease in overall risk and chemopreventive measures using SERMS and AIs may reduce breast cancer risk by up to 50% [9, 10, 11, 12].

Age is an important risk factor. The highest risk of developing invasive breast cancer by age group occurs in women 70 and over (1 in 15), while the risk in women 39 years and younger is only 1 in 208. The majority of women who develop breast cancer are postmenopausal; however, breast cancer is not uncommon in premenopausal women. Breast cancer in younger women tends to be more aggressive than breast cancer diagnosed for older women and is an important cause of mortality in younger patients. Breast cancer is the leading cause of cancer death in women age 20–59, accounting for over 12,000 deaths annually [2].

Genetic Predisposition

Approximately 30% of breast cancer cases are familial and associated with mutations in breast cancer susceptibility genes. Of these, up to 30% are due to high-penetrance mutations in the BRCA1, BRCA2, PTEN, and p53 genes, and these patients face substantially increased lifetime risk of breast cancer, between 40% and 85% over the average-risk women (~12%). Mutations in genes related to DNA repair, such as PALB2 and CHEK2, have also been shown to be associated with moderate risk [13, 14, 15]. Several risk assessment models have been developed to estimate the breast cancer risk or probability of carrying a susceptibility genetic mutation, most notably the Gail, Claus, BRCAPRO, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BODAICEA), and Tyrer-Cuzick models [16, 17, 18, 19, 20]. These models take into account various sets of risk factors to estimate an individual’s risk, including genetic mutations, family history, history of chest irradiation and personal history of ductal carcinoma in situ, ovarian cancer, lobular neoplasia, or atypical ductal hyperplasia, which are used to inform provider and patient decision-making regarding an appropriate screening timetable. The ACR/SBI recommends annual screening with mammography and breast MRI for women with a lifetime risk of greater than 20% for developing breast cancer beginning by age 30, for known high-penetrance gene mutations, 10 years earlier than the youngest affected relative or 8 years after chest radiation [21].

There are other common genetic variants, single nucleotide polymorphisms (SNPs), that confer a much smaller increased risk, but may in combination account for a more substantially increased risk of development of breast cancer in the remainder of familial breast cancer patients who do not have an identifiable high-risk or moderate-risk genetic mutation. Currently, there are more than 90 risk loci identified through genome-wide-associated studies, a technique used to identify genetic variations associated with breast cancer, with estimated hundreds more yet to be discovered that might explain significant proportion of familial and sporadic breast cancer risk [22, 23].

Underlying Breast Cancer Biology Changes

Breast cancers arise from the epithelial cells in the breast cancer ducts [24]. The most important histologic feature in the primary tumor is the presence or absence of tumor invasion beyond the ductal architecture. Tumors that have not broken through the outer boundaries of ductal structures are termed noninvasive (ductal carcinoma in situ, DCIS) and rarely metastasize [24]. Invasive cancers are those that have broken through tissue boundaries at the time of diagnosis and have the potential to metastasize both through lymphatic and hematologic spread. Other histologic features of note include the architectural pattern (ductal, lobular, or mixed), nuclear grade (low, intermediate, or high), and the presence or absence of lymphovascular invasion [24]. In addition to morphological analyses, biopsy material is routinely assayed for phenotypic features that include the expression of hormone receptors (estrogen receptor [ER] and progesterone receptor [PR]) and HER2/neu (also known as cerbB2), typically by immunohistochemistry or fluorescent in situ hybridization (FISH).

The use of gene expression profiling methods has yielded further insights into breast cancer phenotype [25] establishing the current understanding of breast cancer, not as a single entity but as a heterogeneous disease with a number of subtypes defined by distinct molecular signatures [26, 27, 28, 29, 30, 31, 32]. Perou et al. proposed four breast cancer subtypes: (1) luminal A, typically hormone receptor positive and histologically low grade; (2) luminal B, typically hormone receptor positive and histologically medium to high grade; (3) HER2 enriched, which show increased expression of the ERBB2 gene; and (4) basal-like, which express neither the ER/PR receptors nor HER2 (“triple negative”) and typically express high levels of genes favoring cellular proliferation. Basal cancers represent the most aggressive tumors (see Table 1).
Table 1

Four major subtypes of invasive breast cance r used clinically (Adapted from [27])


Standard immunochemical results and cancer grade

Frequency (%)


Luminal A

ER+ PR+ HER+, usually low grade


Best prognosis, low Ki-67 level

Luminal B

ER+ PR+ HER, usually intermediate to high grade


Generally more proliferative (high Ki-67 levels) with less marked hormonal receptor expression than luminal A tumors, approximately 30% are HER2 positive

HER2 enriched

ER PR HER+, usually midgrade to high grade


Prognosis much improved with directed therapy (e.g., trastuzumab); 30–40% of tumors also express ERs and PRs


ER PR HER, high grade


Often synonymous with triple negative

Measuring levels of certain cellular biomarkers within tumors, e.g., Ki-67, a marker of cellular proliferation, can clarify the molecular portrait of a particular tumor and also aid in prediction of tumor aggressiveness and behavior. Molecules and receptors may also serve as targets for specific therapy, e.g., the HER2 receptor is targeted with the drug trastuzumab [33]. Some predictive expression panels are now approved and used in clinical practice [34]. For example, the 21-gene assay used in the Oncotype DX (Genomic Health Inc.) panel is now routinely used to determine which patients with early-stage ER-positive breast cancer may benefit from adjuvant chemotherapy in addition to endocrine therapy [27, 35, 36].

Staging Categories and Prognostic Stratification

Breast cancer stage at presentation is an important factor in prognosis and treatment. Stages are defined in Table 2. The most widely accepted (AJCC) system uses a TNM approach [37]. There is also a stage classification, which includes groups of TNM categories ranging from early-stage disease (stage I) to late-stage, metastatic disease (stage IV). Improved diagnosis, staging, and especially sentinel lymph node (SLN) biopsy resulted in significant changes in breast cancer staging definitions in the 6th edition of the AJCC staging manual, reviewed in Singletary et al. [37] with no major changes [38] to the breast cancer staging definitions in the currently used 7th edition of the AJCC guidelines [39] published in 2010.
Table 2

Summary of the AJCC staging system for breast cancer (Adapted from [37, 38, 39])

Tumor (T)

T0 – no primary tumor found

Tis – carcinoma in situ

T1 – invasive cancer <2 cm in greatest dimension

 T1mic – microinvasion ≦0.1 cm in greatest dimension

 T1a – tumor >0.1 cm but ≦0.5 cm in greatest dimension

 T1b – tumor >0.5 cm but ≦1 cm in greatest dimension

 T1c – tumor >1 cm but ≦2 cm in greatest dimension

T2 – tumor >2 cm but ≦5 cm in greatest dimension

T3 – tumor >5 cm in greatest dimension

T4 – tumor of any size with direct extension to chest wall or skin

Regional lymph nodes (N) (clinical staging)a

N0 – no regional nodal metastases

N1 – metastasis in mobile ipsilateral axillary nodes

N2 – metastasis to fixed ipsilateral axillary nodes (N2a) or ipsilateral internal mammary nodes in the absence of ipsilateral axillary lymph node metastasis (N2b)

N3 – metastases in ipsilateral infraclavicular nodes and axillary nodes (N3a) or ipsilateral internal mammary nodes and axillary nodes (N3b) or metastases to ipsilateral supraclavicular nodes (N3c)

Regional lymph nodes (N) (pathologic staging)b

pNX – regional lymph nodes cannot be assessed (e.g., previously removed or not removed for pathologic study)

pN0 – no regional lymph node metastasis histologically, no additional examination for isolated tumor cells

 pN0(i−) – no regional lymph node metastasis histologically, negative immunohistochemical staining

 pN0(i+) – isolated tumor cells identified histologically or by positive immunohistochemical staining, no cluster >0.2 mm

 pN0(mol−) – no regional lymph node metastasis histologically, negative molecular findings

 pN0(mol+) – no regional lymph node metastasis histologically, positive molecular findings

pN1 – metastasis in one to three axillary lymph nodes and/or in internal mammary nodes with microscopic disease detected by sentinel lymph node dissection but not clinically apparenta

 pN1mi – micrometastasis (>0.2 mm, none >2.0 mm)

 pN1a – metastasis in one to three axillary lymph nodes

 pN1b – metastasis in internal mammary nodes with microscopic disease detected by sentinel lymph node dissection but not clinically apparenta

 pN1c – metastasis in one to three axillary lymph nodes and in internal mammary lymph nodesc with microscopic disease detected by sentinel lymph node dissection but not clinically apparenta

pN2 – metastasis in four to nine axillary lymph nodes or in clinically apparenta internal mammary lymph nodes in the absence of axillary lymph node metastasis

 pN2a – metastasis in four to nine axillary lymph nodes (at least one tumor deposit >2.0 mm)

 pN2b – metastasis in clinically apparenta internal mammary lymph nodes in the absence of axillary lymph node metastasis

pN3 – metastasis in ten or more axillary lymph nodes or in infraclavicular lymph nodes or in clinically apparenta ipsilateral internal mammary lymph nodes in the presence of one or more positive axillary lymph nodes or in more than three axillary lymph nodes with clinically negative microscopic metastasis in internal mammary lymph nodes or in ipsilateral supraclavicular lymph nodes

 pN3a – metastasis in ten or more axillary lymph nodes (at least one tumor deposit >2.0 mm) or metastasis to the infraclavicular lymph nodes

 pN3b – metastasis in clinically apparenta ipsilateral internal mammary lymph nodes in the presence of one or more positive axillary lymph nodes or in more than three axillary lymph nodes and in internal mammary lymph nodes with microscopic disease detected by sentinel lymph node dissection but not clinically apparenta

 pN3c – metastasis in ipsilateral supraclavicular lymph nodes

Distant metastases (M)

MX – distant metastases cannot be assessed

M0 – no distant metastases

M1 – distant metastases

Alternate staging system for invasive breast cancer

Stage I

Stage II

Stage III

Stage IV


Stage IIA

Stage IIIA

Any T, any N, M1






Stage IIIB

Stage IIB

 T4N0-2 M0


Stage IIIC


 Any T, N3M0

aThis system is for clinical staging based upon imaging and physical examination

bPathologic staging for nodal metastases is further classified for sentinel lymph nodes, for detection on the basis of immunohistochemistry assays, and for detection on the basis of molecular assays. Classification is based on axillary lymph node dissection with or without sentinel lymph node dissection. Classification based solely on sentinel lymph node dissection without subsequent axillary lymph node dissection is designated (sn) for “sentinel node,” such as pN0(i_)(sn)

cIf associated with more than three positive axillary lymph nodes, the internal mammary nodes are classified as pN3b to reflect increased tumor burden

Breast Cancer Detection and Diagnosis, Modalities Other Than Nuclear Medicine for the Tumor

Mammography is the primary imaging modality for breast cancer screening, detection, and diagnosis [40]. The average earlier stage and smaller size of lesions detected by mammographic screening, versus symptomatic and/or palpable lesions, reflects the success of mammographic screening, which has been shown to reduce breast cancer mortality [40]. Full-field digital mammography has largely replaced screen-film mammography. And the recent addition of digital breast tomosynthesis has been shown to reduce false-positive rates and increase cancer detection [41, 42]. Both ultrasound and MRI are important adjuncts to x-ray mammography for detection, diagnosis, characterization, and determination of the extent of breast cancer and are routinely utilized in these roles [43, 44].

MRI has been shown to be useful for screening in high-risk patients, i.e., women with greater than 20% lifetime risk of breast cancer. In a meta-analysis of 14 studies of high-risk women conducted between 2000 and 2011, MRI was shown to have higher sensitivity for malignancy (84.6%) than mammography (38.6%) or ultrasound (39.6%) [45].

The American Cancer Society recommendations have incorporated MRI for screening in high-risk patients [46]. Mammography, ultrasound, and breast MRI can all be used to direct tissue sampling by needle biopsy for breast cancer diagnosis.

Randomized control trials have shown no survival or quality of life benefit when annual mammography has been compared with intensive imaging or laboratory surveillance in asymptomatic women with early-stage breast cancer [47].

With regard to staging and metastatic workup for asymptomatic women with a history of early-stage breast cancer, stage I, IIA, and IIB, no further imaging is indicated beyond diagnostic mammography (with adjunct use of US and MRI) given the low chance of distant metastases and the higher chance of false-positive findings [48]. For later stage breast cancer patients, stage III and above current guidelines recommend chest CT, abdomen +/− pelvis CT or MR, bone imaging (bone scintigraphy (BS) or Na18F PET/CT), and optional [18F]FDG PET/CT. Bone scan and [18F]FDG PET/CT are the most common nuclear imaging methods used in staging and metastatic workup. These will be discussed later. Again, anatomic imaging modalities are considered the gold standard with chest and abdominopelvic CT, breast MR, and brain MR performed as directed by physical examination and clinical assessment.

Nuclear Imaging for Primary Detection and Diagnosis

Radiotracer methods for primary breast cancer detection and diagnosis have not as yet found widespread use despite some important advantages over anatomic imaging, namely, the potential for more specific targeting of breast cancer tissue, good tumor to background contrast, and uptake independent of breast density [49, 50, 51, 52, 53]. 99mTc-sestamibi is the most common single-photon radiopharmaceutical used in both scintimammography, imaging of the breast using traditional nuclear medicine gamma cameras, and breast-specific gamma imaging (BSGI, also called molecular breast imaging ), imaging with dedicated breast small field-of-view gamma cameras. The radiopharmaceutical is taken up and retained differentially in breast tumors from normal breast tissue. The specific mechanism is unknown, but appears to depend on factors relating to regional blood flow, plasma and mitochondrial membrane potential, angiogenesis, and tissue metabolism [54, 55, 56]. Scintimammography, however, has a limited ability to detect lesions less than 1 cm and also has lower sensitivity for non-palpable lesions. BSGI can overcome some of these limitations using dedicated devices that provide high spatial resolution using dedicated breast imaging devices [49, 51, 57]. The disadvantage of both techniques is that they require relatively long imaging times and subject the patient to a substantially increased radiation dose (15–30 times) compared with mammography [57, 58]. At present, they are not indicated for routine screening/primary detection.

PET has also been tested in application to breast cancer detection and diagnosis, spurred by the finding of increased 18F-fluorodeoxyglucose ([18F]FDG) uptake in cancer compared to normal breast tissue. [18F]FDG uptake in tumors reflects the rate of glucose metabolism and correlates somewhat with histologic type (higher uptake in ductal vs. lobular), tumor histologic grade, and indices of cellular proliferation (higher uptake with higher levels of proliferation) [59, 60]. Overall, the sensitivity of whole-body [18F]FDG PET in the detection of primary breast cancer ranges 64–96%, specificity of 73–100%, positive predictive value of 81–100%, and negative predictive value of 52–89% [61]. Whole-body [18F]FDG PET however has low sensitivity for small, well-differentiated, and in situ cancers [62]. As with 99mTc-sestamibi scintimammography, this has severely limited its use for primary tumor diagnosis.

Dedicated breast positron emission mammography (PEM) units have been developed [63], and advantages include higher spatial resolution, shorter imaging time, and reduced attenuation compared with whole-body imaging. PEM units allow a co-registration of anatomic and molecular information similar to PET/CT and have biopsy capability [64]. In a large trial of patients with newly diagnosed early breast cancer, PEM showed improvement in specificity over MRI, and the performance of PEM plus conventional imaging is similar to MRI with regard to cancer detection [65].

However, PEM limitations include difficulty in imaging posterior lesions, variable [18F]FDG uptake in small tumors, and false-positive findings from prior biopsy [66, 67]. In addition, similar to single-photon techniques, radiation doses are significantly higher than mammography [57].

Nuclear Imaging for Regional Nodal Staging

Staging is typically divided into regional lymph nodes, especially axillary nodes, and distant or systemic staging of sites beyond regional lymph nodes, especially to visceral organs (lung and liver) and bone. Almost all patients with invasive breast cancer will undergo axillary nodal staging, since the presence or absence of axillary lymph nodes is an important consideration for further treatment after surgery [68].

The traditional approach to axillary nodal staging was the axillary nodal dissection, where axillary contents were removed and examined; however, the morbidity of this process, including a significant incidence of lymphedema, led to the search for a less morbid but equally accurate approach [68]. This consideration motivated sentinel lymph node (SLN) methods , designed to identify and sample the node(s) that are the first draining nodes, i.e., “sentinels” in the axillary drainage basin for the breast lesion. SLN biopsy is a shorter surgical procedure than full axillary nodal dissection with much less morbidity [68]. If sentinel nodes are negative, then the chance of “downstream” nodal metastases is quite small, and the patient can be spared the full dissection. If the sentinel node shows evidence of metastasis, then an axillary dissection may be needed to determine the extent of axillary spread. SLN biopsy has been shown to be highly accurate compared to axillary dissection [69, 70, 71] and has emerged as the standard of care for axillary nodal sampling in patients with clinically negative axillae [72]. SLN biopsy has been thoroughly tested in large clinical trials and shown to be safe and accurate, with comparable relapse and survival compared to axillary nodal dissection [73, 74]. Further, a large randomized clinical trial showed that completion axillary dissection is not useful for improved locoregional control or survival in women with smaller (T1/T2) tumors, no palpable adenopathy, and one or two positive nodes positive by SLNB without gross ENE [75].

Radionuclide methods play a key role in SLN biopsy (see also the chapter “Radioguided Surgery for Breast Cancer”). Typically, for SLN mapping, a colloidal radiopharmaceutical (99mTc-sulfur colloid in the USA, 99mTc-nanocolloudal albumin or 99mTc-antimony nanocolloid in Europe) is injected, and sentinel nodes are identified through imaging (lymphoscintigraphy, Fig. 1), gamma probes, or both [76]. Other, more specific agents have been developed and are undergoing testing [77]. Some centers use both radiocolloid and visible blue dye to guide SLN sampling, while some centers use one of the methods without the other [68].
Fig. 1

Lymphoscintigraphy for breast cancer sentinel lymph node mapping. Images (oblique, left, and anterior, right), with the body contour outline drawn using a radioactive marker, show a peri-lesional injection (solid arrows), an axillary sentinel lymph node (arrowhead), and drainage to an internal mammary lymph node (dashed arrow)

The importance of sampling internal mammary (IM) nodes has been a point of considerable debate [78]. Studies using SLN mapping and lymphoscintigraphy to sample IM lymph have demonstrated the feasibility of this approach [79, 80, 81]; however, the overall percentage of positive IM nodes is on the order of 5% or less. And although a significant proportion of breast cancers have primary drainage to the IM nodes, approximately 30% of medial tumors and 15% of lateral tumors, it is rare to have IM lymph node metastasis in the absence of axillary nodal metastases [82]. As a result, most practices in the USA do not routinely sample IM nodes. Yao et al. [83] found that patients with positive axillary nodes and IM nodal drainage, even in the absence of IM nodal sampling, were significantly more likely to relapse and die of breast cancer than those without drainage. Another study found a higher rate of nodal relapse outside the axilla, versus in the axilla, after SLN biopsy [84]. Such studies prompt careful consideration of the approach to SLN mapping in breast cancer and the potential benefit of peri-lesional injection, which have a higher likelihood of drainage demonstrating IM nodal drainage, and lymphoscintigraphy in selected patients to identify extra-axillary drainage [85].

There was considerable interest in [18F]FDG PET as a noninvasive imaging method to detect axillary nodal spread in newly diagnosed breast cancer and avoid the need for invasive biopsy. However, although early studies showed high sensitivity of increased uptake in axillary nodes in breast cancer, multiple subsequent studies including larger multicenter trials have failed to show sufficient sensitivity of PET in the staging of the axilla in patients with early-stage breast cancer [86, 87] to obviate the need for tissue sampling with SLN mapping and biopsy [88, 89, 90]. Early-stage breast cancer patients, i.e., those with small tumors less than 2–3 cm with clinically negative axillary nodes, represent the majority of breast cancer patients, many clinically silent and diagnosed by screening mammography. Given the low probability of metastatic disease in this population and the risk of false positives with whole-body PET/CT, there is no recommended role for [18F]FDG PET in axillary or clinical staging in patients with early-stage disease [21, 91].

Nuclear Methods for Staging of Distant Metastases

Systemic evaluation for metastatic disease is recommended in the initial workup for high-risk and later clinical stage (≥stage III) breast cancer as well as for patients with recurrent breast cancer and known metastatic disease [48]. High-risk disease is commonly defined as locally advanced breast cancer (LABC) [48], including primary tumor larger than 5 cm, skin or chest wall involvement, fixed axillary nodes, positive supraclavicular/infraclavicular/IM nodes, and inflammatory cancer. Patients with LABC have a poor prognosis because of the high incidence of distant metastases during follow-up [92, 93]. In these patients detection of distant metastases is crucial to determine the initial goals of therapy, i.e., curative versus palliative and/or local control, and thus systemic staging is recommended as part of initial evaluation [48]. The bone scan and PET/CT play significant roles in systemic staging in breast cancer patients. Important areas of more distant spread for breast cancer include nodal beds outside the axillary and IM chain, for example, in the mediastinum, the lungs, the liver, and the bones [94].

The skeleton is the most common site for breast cancer metastases, affecting approximately 50–70% of patients, and is the sole site of metastatic disease in almost one half of patients [95, 96, 97, 98, 99]. For decades, the bone scan (BS) has been the primary method for staging breast cancer bone metastases despite well-known limitations with respect to sensitivity and specificity (Fig. 2). The most commonly used radiopharmaceuticals are 99mTc-hydroxymethylene diphosphonate (99mTc-HMDP or 99mTc-HDP) and 99mTc-methylene diphosphonate (99mTc-MDP) with accumulation within bone metastases related to changes in local blood flow and abnormally increased activity of osteoclasts.
Fig. 2

Bone metastases identified by bone on two subsequent occasions during follow-up. 99mTc-methylene diphosphonate bone scan shows progression of osseous metastatic disease, for example, radiotracer uptake in the right femoral lesion (arrowhead) and new uptake in the right femoral head (thick arrow) and right scapula (thin arrow)

The reported sensitivity ranges from 62% to 100% and specificity ranges from 78% to 100% [100, 101]. False positives can be caused by trauma, degenerative changes, and other benign conditions, while false negatives can occur in the presence of predominantly osteolytic metastases, low bone turnover, or in avascular regions (such as necrosis) [101]. Single-photon emission computed tomography (SPECT) has been shown to improve sensitivity and specificity [102, 103, 104], with further improvement in diagnostic confidence shown with SPECT/CT [105, 106, 107, 108], likely due to the reduction of false positives through the morphological appearance CT. SPECT is particularly helpful to characterize solitary lesions in the spine and/or to differentiate bone metastases from degenerative lesions. Salvelli et al. reported the sensitivity and specificity of SPECT to be over 90% [102].

Sodium-[18F]fluoride (Na18F) is a bone-seeking positron-emitting agent with a similar mechanism of skeletal uptake as 99mTc-labeled bone scan agents. Both have uptake related to blood flow and osteoblastic activity; however, Na18F has practical advantages over the single-photon bone scan agents that include faster clearance and higher contrast [109]. The now widespread availability of hybrid PET/CT systems has renewed interest in the relatively higher spatial resolution of Na18F PET/CT for bone imaging, as there has been demonstrated higher diagnostic accuracy over BS (with or without SPECT) and CT for breast cancer and number of other malignancies [110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121] (Fig. 3). Several prospective trials and registries are ongoing which seek to more robustly address issues regarding time needed for interpretation, radiation dose, cost, and clinical impact compared to the BS [115, 122, 123, 124, 125, 126].
Fig. 3

Bone metastases identified by Na18F PET. Fluoride PET images (sagittal low-dose CT, emission, and fused images (top); low-dose CT and fused images (bottom)) of a patient with multiple bone metastases demonstrate the increased detail and tomographic information by PET. Sagittal images demonstrate radiotracer uptake in the cervical and thoracic spine compatible with osseous metastases, for example, at the T8 (fat arrows) and T10 (thin arrows). Axial images at the T8 level demonstrate increased tracer uptake associated with an osseous sclerosis (fat arrow). Note linear tracer uptake at the L4/L5 (arrowhead) level involving the inferior endplate of L4 and superior endplate of L5 corresponds to degenerate uptake

[18F]FDG PET has also been shown to be complimentary, and in some studies superior to BS in the detection of skeletal metastases in most studies [127, 128, 129, 130, 131] (Fig. 4). There is however an important difference compared to bone scans and fluoride imaging. The increased uptake detected on [18F]FDG PET is thought to be within the breast cancer tumor cells thus reflecting tumor activity within the bone rather the osteogenic (blastic) response of the bone architecture to the metastases, as is the case with BS and Na18F uptake. Several studies demonstrated that [18F]FDG PET is superior to bone scanning in the detection of lytic and intramedullary metastases, but inferior for detecting primarily osteoblastic lesions. Cook and colleagues showed that [18F]FDG PET detected the lytic metastases often missed by bone scan, while [18F]FDG PET often missed osteoblastic metastases, for which bone scan is superior [132]. Subsequent studies confirmed these findings (reviewed in Fogelman et al. [63, 109]), showing that [18F]FDG PET is complementary to bone scan and Na18F imaging. Some of the increased sensitivity over BS is likely due to earlier detection of active tumor in bone prior to osteoblastic response [133]. Again, as with hybrid imaging with SPECT/CT and Na18F PET/CT, specificity is gained through reduction of false positives with correlative CT aiding in improved differentiation between benign and malignant lesions. And though [18F]FDG PET has been shown to have lower sensitivity for purely osteoblastic or sclerotic lesions [132, 134, 135], with many demonstrating no uptake, these may be detected by the changes on the accompanying CT [135]. Given reported high concordance between BS and [18F]FDG PET/CT [136], and the known advantages over BS, the question of the necessity of the BS in the era of hybrid PET imaging is being raised. NCCN guidelines still recommend BS for staging in symptomatic patients with early-stage/operable breast cancer, and Na18F PET/CT is now included in current NCCN guidelines as an alternative to BS [48]. In addition, a recent amendment suggests PET/CT may be helpful for problem solving in equivocal cases and that bone scanning/Na18F PET/CT may not be necessary in cases where [18F]FDG PET clearly demonstrate bone metastases [48].
Fig. 4

Breast cancer distant metastases identified by [18F]FDG PET/CT in a male patient presenting with back pain. Axial PET and low-dose CT images (left and middle columns) demonstrate increased [18F]FDG uptake associated with destructive osseous metastatic lesion involving the sacrum. Sagittal PET (right) demonstrates multifocal osseous involvement of the spine and sternum. Axial PET and CT images on the bottom row demonstrate intense radiotracer uptake in a left breast nodule, corresponding to the primary invasive ductal carcinoma tumor

Besides bony metastatic disease, [18F]FDG PET has proven useful in the general detection of distant metastases for initial staging and restaging of breast cancer patients [95, 137, 138, 139, 140, 141]. A 48-patient pilot study by van der Hoeven et al. suggested that PET was more sensitive than conventional imaging in detecting distant metastases [138]. Recently, a prospective cohort study by Groheux et al. compared conventional imaging staging (bone scan, chest radiography or dedicated CT, liver ultrasound, enhanced CT abdomen and pelvis) with a single session of staging with [18F]FDG PET [95] in 117 patients with LABC. The study found that not only did PET change the clinical stage in 61 patients (52%), PET identified more distant metastases compared to the conventional imaging (43 vs. 28 patients). In addition, PET/CT was found to outperform conventional imaging in detecting bone, distant lymph node, and liver metastases. Diagnostic chest CT outperformed PET/CT, owing to respiratory motion and partial volume effect reducing the sensitivity of PET for sub-centimeter lung nodules, and relatively more difficult visualization of sub-centimeter lung nodules on the lower dose acquisition CT obtained as part of the PET/CT.

There is ongoing study of the utility of PET/CT in staging intermediate risk breast cancer patients, i.e., patients with large breast cancers (T2) or clinical N1 disease [142, 143]. In a recent prospective study of 254 patients with clinical stage IIA through IIIC (as determined by clinical examination, mammography, breast MRI, and locoregional ultrasound), Groheux et al. found that imaging with [18F]FDG PET/CT revealed distant metastatic disease in 2.3% of patients with clinical stage IIA disease (1/44), 10.7% of patients with stage IIB disease (6/56), 17.5% of patients with stage IIIA disease (11/63), 36.5% of patients with stage IIIB disease (27/74), and 47.1% of patients with stage IIIC disease (8/17) [143]. As expected the yield of metastatic disease increases with increasing clinical stage. Further studies are needed to determine clinical impact and cost-effectiveness of staging evaluation with PET/CT of patients in the intermediate risk category.

Approaches to Breast Cancer Therapy

With exception of very early- and late-stage disease, almost all breast cancer care involves a combination of locoregional and systemic treatments. Locoregional therapy includes surgery and radiotherapy, and systemic therapy includes chemotherapy (e.g., taxanes), endocrine (hormonal, e.g., aromatase inhibitors) therapy, and/or targeted (biologic, e.g., anti-HER2 drugs) therapy.

Primary tumor excision remains a key component of the treatment of newly diagnosed breast cancer [144]. This includes mastectomy, removal of the entire breast, and breast-conserving surgery (BCS) , also known as “lumpectomy” of the cancer [144]. Lumpectomy followed by adjuvant radiotherapy provides low rates of local recurrence and survival similar to mastectomy, but with preservation of the breast [145]. For invasive disease, axillary nodal staging is routinely performed at the time of surgery. The purpose of axillary sampling is largely for diagnosis; the therapeutic value of lymph node removal remains largely unproven [146]. For this purpose, sentinel lymph node has emerged as the preferred method for patients with clinically negative axillae [68], sparing the patient most of the morbidity, principally lymphedema, of full axillary dissection [147].

Breast cancer is one of the solid tumors that is often responsive to systemic therapy. Systemic therapy can be cytotoxic chemotherapy and can also involve more targeted and selective treatments. In all but the earliest cancers, adjuvant systemic therapy is used in addition to locoregional therapy to treat residual microscopic disease [145] and has contributed significantly to reduced breast cancer mortality. Systemic cytotoxic chemotherapy is used in more advanced or aggressive tumors, typically ER- or PR-negative tumors [148], triple-negative tumors, HER2-positive tumors, and large tumors [41, 48].

In ER-expressing tumors, adjuvant endocrine therapy is used, typically an aromatase inhibitor or tamoxifen [149]. In higher-risk ER-expressing tumors, both chemotherapy and endocrine therapy can be used [148]. RNA-based genomic testing can be used to aid in estimating the risk of distant recurrence and identify patients in this group who may benefit from chemotherapy [35]. Both types of treatment are also primary therapy for metastatic disease [150]. For HER2-positive breast cancer, trastuzumab, a HER2-specific monoclonal antibody has been shown to substantially improve survival [151]. Directed therapy has become a key component of both adjuvant and metastatic systemic therapy for patients with HER2-overexpressing tumors [152, 153]. Pertuzumab, a monoclonal antibody directed against a different epitope than trastuzumab on the HER2 receptor, and lapatinib, a tyrosine kinase inhibitor targeting the HER2 pathway, are both routinely used in therapy regimes for higher-risk HER2-positive disease [48].

New breast cancer targets and therapy combinations continue to be investigated [154, 155]. Improvement in progression-free survival in women with hormone receptor-positive metastatic breast cancer has been demonstrated with the addition of the mTOR inhibitor everolimus [156] and more recently with the addition of the drug palbociclib, an inhibitor of cyclin-dependent kinases 4 and 6 [157].

In ER-negative tumors, chemotherapy is typically an important component of systemic therapy. This is typically combined with HER2-directed therapy in HER2-overexpressing tumors. On the other hand, tumors that are ER/PR/HER2 negative (triple negative, TNBC) are treated primarily using chemotherapy. The ongoing study is particularly important in triple-negative patients where therapies effective in other breast cancer subtypes (endocrine and HER2-targeted therapies) are unlikely to work [158, 159].

For patients with LABC, or those with large tumors who have smaller breasts and/or desire breast conservation, neoadjuvant therapy is considered. The primary purpose of this therapy is to reduce the size to the tumor to better facilitate BCS and local control. However, response to neoadjuvant therapy also provides valuable assessment of the tumor’s response to therapy prior to definitive treatment.

Radiotherapy is commonly used as an adjunct to surgery in locoregional breast cancer treatment [160]. Lumpectomy in the absence of breast radiotherapy leads to unacceptably high rates of local recurrence [145]; therefore breast radiotherapy is an integral part of breast conservation treatment. In addition to radiation of the breast in patients undergoing breast conservation, adjuvant irradiation can also be used for the chest wall in mastectomy patients, but it is usually reserved for patients with larger tumors or documented chest wall invasion [160]. Adjuvant radiotherapy is also used in the treatment of involved nodal basins and has been shown to enhance survival in those patients with multiple axillary nodal metastases [161].

Assessing the Efficacy of Treatment(s)

Evaluating the efficacy of systemic treatment is an important diagnostic need for breast cancer. The largest population of patients undergoing systemic therapy for breast cancer receives treatment as adjuvant therapy, after completion of primary surgery. Since there should be no residual macroscopic disease after definitive surgery, imaging is not helpful for response monitoring for adjuvant therapy, except for re-staging upon suspicion of recurrence. Imaging however plays an important role in response evaluation of systemic neoadjuvant therapy (NAT), given prior to definitive surgery primarily in LABC, as well as systemic therapy given as the primary treatment for metastatic, stage IV disease.

In the neoadjuvant setting, studies have demonstrated that the extent of residual breast and axillary disease after treatment is prognostic for both disease-free survival (DFS) and overall survival (OS) [162, 163, 164]. Patients demonstrating complete pathologic response (pCR), defined as no residual invasive tumor at post-therapy surgery histopathology, have improved long-term outcome compared to patients without pCR [162, 164]. NAT offers both the opportunity to assess tumor responsiveness while improving surgical options for patients with locally advanced disease by down-staging the primary tumor.

Sized-based approaches such as physical exam and mammography that rely on changes in size for response evaluation (e.g., RECIST criteria [165]) do not reflect early changes at the molecular level that may occur prior to changes in tumor size. Functional imaging therefore may be particularly helpful in determining treatment efficacy earlier in the course, allowing for earlier change of therapeutic strategy, thus avoiding unnecessary toxic systemic side effects of ineffective treatments.

One of the earliest studies showing the use of [18F]FDG PET in the assessment of treatment response was performed by Wahl and colleagues in 1993 [166]. The investigators observed significant quantitative differences in the [18F]FDG uptake measured before and after 2 months of therapy (mid-therapy) for responders versus nonresponders. While nonresponders had no significant change, patients who went on to a good response had declines in [18F]FDG SUV of 50% or more. Most subsequent studies evaluating response at mid-therapy found similar quantitative findings (reviewed in [63, 167, 168]. A meta-analysis of 19 studies including over 900 patients by Wang et al. [169] underscored that imaging earlier in the course of treatment (after the first or second cycle) with cutoffs between 55% and 65% declines from baseline as response criteria offered better correlation with pathologic response of primary tumors.

The knowledge that breast cancer is a molecularly heterogeneous disease has prompted response evaluation studies within the various molecular subsets of breast cancer [170]. Groheux et al. [171] and Humbert et al. [172], in prospective studies including women with HER2-positive breast cancer treated with trastuzumab-containing regimens showed that low residual metabolism post-NAT (SUV <=3.0 after two cycles and <2.1 after one cycle, respectively) strongly correlated with pCR. Similar studies in patients with ER-positive/HER2-negative tumors and triple-negative tumors have also been conducted [173, 174, 175]. ER-positive/HER2-negative tumor type is somewhat less [18F]FDG avid at baseline and less sensitive to chemotherapy than more aggressive subtypes (e.g., TNBC) [170], and pCR is rarely achieved. Studies have found that for this subtype baseline SUV and changes after one or two cycles of NAT may offer prognostic value in the form of survival rather than pCR [174, 175]. For triple-negative tumors, change in SUV from baseline has been shown to correlate with pCR, which is more closely associated with survival [173]. These studies indicate that assessment of serial PET data to infer breast cancer response to therapy should be considered in light of breast cancer subtype, supported by studies showing that [18F]FDG kinetics may vary by breast cancer subtype [170].

Although some researchers have shown advantages of obtaining specific kinetic parameters of the tumor over the static SUV measurements [176], measuring percentage change in SUV from baseline and measuring absolute SUVs at baseline and follow-up time points are the most commonly used parameters in response assessment studies, likely due to ease of calculation and reproducibility. These studies highlight the need for standardization of the timing of interim [18F]FDG PET evaluation, cutoff points, and criteria used in assessing response to therapy for different subtypes of breast cancer [177, 178]. Further, study outcomes may also be influenced by the specific treatment regimes.

Studies of [18F]FDG PET performed after the completion of chemotherapy have shown reduced sensitivity for residual disease – i.e., that the absence of [18F]FDG uptake is not a reliable indicator of complete pathologic response [167, 179, 180, 181]. This is especially true for axillary nodal disease since the sensitivity for residual microscopic disease post-therapy is low. However, recent studies have shown that the presence of [18F]FDG uptake post-therapy is specific and highly predictive of relapse [182]. Therefore, even though [18F]FDG PET may miss small-volume disease post-therapy, the presence or absence of [18F]FDG uptake carries prognostic significance that may be important in directing follow-up.

In the metastatic setting, timely determination of the efficacy of systemic and/or targeted therapy is critical to the overall goal of maximizing survival and minimizing symptoms and side effects. Achieving and correlating with pCR is no longer the aim in this setting, and clinicians rely on clinical evaluation as well as anatomic and functional imaging in their assessment. Again, molecular imaging with [18F]FDG PET can offer more and earlier insight into treatment response, particularly in cases where the regimen may produce an overall cytostatic (reflected in the tumor metabolism) rather than cytotoxic (reflected in tumor size) effect (Figs. 5 and 6). A number of small studies showed, similar to neoadjuvant therapy, that response of metastatic breast cancer (MBC) to therapy is accompanied by substantial drops in [18F]FDG uptake, typically 40–50% or more from the pre-therapy baselines [183, 184, 185]. Mortazavi-Jehanno et al. [186] showed that [18F]FDG PET was predictive of progression-free survival in patients with ER-positive MBC tumors undergoing endocrine therapy, and more recently Lin et al. [187] found in a prospective studying including 82 patients with HER2-positive MBC undergoing early-line trastuzumab and lapatinib therapy that [18F]FDG PET was useful for predicting response to therapy by RECIST.
Fig. 5

Evolution of metabolic involvement in the pelvic bones of patient with bone-dominant metastatic at baseline, 4 weeks and 12 weeks post-endocrine therapy. Grossly unchanged lytic osseous change on CT images within the left iliac bone over the interval, however variable metabolic activity, with overall progression. Posterior left iliac bone involvement (thick arrows) demonstrates mild decline in metabolic activity at 4 weeks post initiation of therapy (SUV from 7.4 to 4.1) but however increased above baseline (7.9) at 12 weeks. Left iliac wing (thin arrows) demonstrates increase in [18F]FDG uptake at 4 weeks, from 5.7 to 8.6, and persistent increase, to 10.8, at 12 weeks, with much more extensive changes. Later clinical follow-up revealed progression of osseous and new visceral metastases, indicating the ability of the serial [18F]FDG PET/CT to indicate early disease progression

Fig. 6

Breast cancer response to treatment by serial [18F]FDG PET. Sagittal PET and fused images from a patient with ≥30% decline in average lesion SUVmax after 4 weeks of endocrine therapy. For example, sternal involvement (black and white arrows) decreased from SUVmax of 16.0 at baseline to 7.2 after 4 weeks. The patient sustained a response with decline in average SUV at 12 weeks and was without progression for 15 months from initiation of this line of endocrine therapy. This was the third-line endocrine therapy for this patient. This case illustrates the ability of serial [18F]FDG PET/CT to predict response of metastatic disease to systemic therapy in disease sites like bone that are challenging to follow by standard imaging such as CT and bone scan

A particularly difficult clinical problem is the evaluation of response for bone metastases [101]. Given that many bone lesions do not demonstrate a measurable soft tissue component, anatomic imaging using RECIST 1.1 is limited particularly with respect to evaluation of osseous metastases. Just as with detection, PET/CT has shown superiority to bone scan in assessment of treatment efficacy with regard to osseous metastases. Though excellent for detection, BS is problematic for response evaluation as changes will significantly lag behind the bone metastasis response and may even transiently worsen or “flare” in response to successful therapy [188, 189]. This flare phenomenon has also been shown with Na18F PET/CT given the similarity in mechanism [190, 191] and thus has implications for the timing of Na18F PET in response evaluations. In addition, given that [18F]FDG PET is not as sensitive for sclerotic lesions and breast cancer bone metastases may be mixed lytic/sclerotic or sclerotic, Na18F PET may provide a complementary role in assessment of response to therapy and has been shown to impact decision-making in treatment monitoring [192].

The observed differences between bone scintigraphy and [18F]FDG PET for visualizing bone metastases [132] led some investigators to hypothesize that [18F]FDG PET might provide a useful and accurate means for assessing bone metastasis response. Stafford and colleagues [193] showed that changes in [18F]FDG uptake correlated with the clinical assessment of response in bone-dominant MBC as well as with changes in breast cancer tumor markers, which are used clinically to assess bone metastasis response. A follow-up study [194] by Specht et al. showed that changes in [18F]FDG uptake predicted time to progression, and Tateishi et al. [195] showed that both metabolic and structural changes by [18F]FDG PET/CT predicted bone metastasis time to progression.

These studies show the significant role [18F]FDG PET may play in assessing treatment response for bone-dominant MBC.

Future Directions

As breast cancer treatment becomes increasingly targeted and individualized, there are increasing demands upon breast cancer diagnosis to help direct the therapeutic approach. The imaging approaches in current clinical use for diagnosing and staging breast cancer – mammography, ultrasound, contrast-enhanced MRI, CT, bone scan, and [18F]FDG PET/CT – provide important information on disease extent and response to treatment, but are relatively nonspecific. Advances are more specific and quantitative, and molecular imaging methods are poised to help provide the kind of diagnostic data that are needed to help take advantage of new therapeutic options for breast cancer. We highlight some selected examples below. These include imaging tumor receptor expression and imaging tumor proliferation to assess early response to treatment.

The expression of tumor receptors and other proteins involved in cellular signaling is particularly relevant to breast cancer treatment. Targets of interest include the estrogen and progesterone receptors (ER, PR), targets for endocrine therapy, and HER2, the target for HER2-directed therapy. These biomarkers have been traditionally measured by in vitro assay of biopsy material [196]; however, the development of vivo molecular imaging of these targets is setting the stage for more specific guidance on treatment and management of breast cancer. Complementary advantages of imaging to measure receptor expression include its non-invasiveness, the ability to measure receptor expression in the entire disease burden and thus the ability to avoid sampling error that can occur with heterogeneous receptor expression, and the potential for serial studies of in vivo drug effects on the target [197, 198].

PET imaging of the ER using the radiopharmaceutical 16 alpha-18F-fluoro-17 beta-estradiol (18F-FES) [199] has been validated as a measure of ER expression in breast tumors against ER expression assays of tissue samples by radioligand binding [200] and immunohistochemistry [201]. Higher 18F-FES SUV has been correlated with response to endocrine therapy [197, 202, 203]. A majority of breast cancers express ER (approximately 70%) and ER status is an initial key determinant in therapeutic strategy [204]. Given that there may be substantial heterogeneity of ER expression across tumor sites [205, 206], functional imaging of the ER receptor with PET/CT appears positioned to provide valuable insights into tumor burden characterization and pharmacodynamic response to targeted therapy at different stages of the disease [198]. Interest in imaging of the progesterone receptor (PR) in vivo using 21-18F-fluoro-16α,17α-[(R)-(1′-α-furylmethylidene)dioxy]-19-norpregn-4-ene-3,20-dione (18F-FFNP) has been increasing [207] as tumors with ER/PR co-positivity are shown to be more responsive to endocrine therapy [208].

HER2 expression in breast cancer has become an important indicator of prognosis and an increasingly important target for therapy, and imaging probes using both single-photon radionuclides (e.g., 111In-labeled-trastuzumab and 99mTc-ICR12) and positron-emitting radionuclides (e.g., 64Cu-trastuzumab, 64Cu-DOTA-ZHER2:477, 68Ga-trastuzumab F(ab′)2 fragments, 68Ga-ABY-002, and 89Zr-trastuzumab) have been developed and validated [209, 210]. 89Zr-trastuzumab PET/CT has emerged as one of the more favorable HER2 imaging methods due to advantageous radionuclide properties, biodistribution characteristics, and straightforward quantification capabilities of PET compared to single-photon techniques [210, 211]. A recent multicenter trial (ZEPHIR study) including patients with HER2-positive MBC who had progressed after prior trastuzumab therapy and were due to receive trastuzumab emtansine – a recently approved antibody-drug conjugate [212] – has shown that pretreatment HER2 and metabolic imaging with 89Zr-trastuzumab PET/CT and [18F]FDG PET, followed by metabolic assessment with [18F]FDG PET after one cycle of therapy, had a 100% NPV for therapeutic response after three cycles of therapy and also predicted time to treatment failure [213]. This study paves the way for other studies to assess the utility of combination imaging using PET and other modalities not only in determination of treatment efficacy in a number of clinical settings, such as the neoadjuvant and first-line metastatic settings, but also as a predictive tool in the pre-therapy setting [214].

Although studies of [18F]FDG PET have shown that tumor glycolysis declines early in the course of treatment [215, 216, 217], it is likely that other pathways more closely tied to cellular growth and death may provide even earlier and more specific indications of therapeutic response. Imaging cellular proliferation in breast tumors is becoming increasingly enticing, especially given the recent approval of a new drug palbociclib, which inhibits cell cycle progression into S phase [218].

A number of PET tracers are being developed and studied [219] with much attention focused on thymidine analogs, since thymidine is a substrate for activity of the thymidine salvage pathway for DNA synthesis. 18F has largely replaced the short half-life 11C [220] for radiolabeling proliferation analogs (new ref see comments), with the most promise shown with 18F-fluorothymidine (FLT). A recent phase II study showed some promise for its use in predicting pCR in the neoadjuvant setting [221]. Work on other proliferation targets such as the sigma-2 receptor ligand is ongoing [222, 223].


  1. 1.
    Siegel RL. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29.PubMedCrossRefGoogle Scholar
  2. 2.
    Society, A.C. American Cancer Society. Cancer facts & figures 2015. Atlanta: American Cancer Society; 2015.Google Scholar
  3. 3.
    Writing Group for the Women’s Health Initiative, I. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the women’s health initiative randomized controlled trial. JAMA. 2002;288(3):321–33.CrossRefGoogle Scholar
  4. 4.
    Berry DA, et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med. 2005;353(17):1784–92.PubMedCrossRefGoogle Scholar
  5. 5.
    DeVita VTJ, Rosenberg SA. Two hundred years of cancer research. N Engl J Med. 2012;366(23):2207–14.PubMedCrossRefGoogle Scholar
  6. 6.
    Oeffinger KC, et al. Breast cancer screening for women at average risk: 2015 guideline update from the american cancer society. JAMA. 2015;314(15):1599–614.PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Salehi F, et al. Review of the etiology of breast cancer with special attention to organochlorines as potential endocrine disruptors. J Toxicol Environ Health B Crit Rev. 2008;11(3–4):276–300.PubMedCrossRefGoogle Scholar
  8. 8.
    Howell A, et al. Risk determination and prevention of breast cancer. Breast Cancer Res: BCR. 2014;16(5):446.PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Colditz GA, Bohlke K. Priorities for the primary prevention of breast cancer. CA Cancer J Clin. 2014;64(3):186–94.PubMedCrossRefGoogle Scholar
  10. 10.
    Martino S, et al. Continuing outcomes relevant to evista: breast cancer incidence in postmenopausal osteoporotic women in a randomized trial of raloxifene. J Natl Cancer Inst. 2004;96(23):1751–61.PubMedCrossRefGoogle Scholar
  11. 11.
    Goss PE, et al. Exemestane for breast-cancer prevention in postmenopausal women. N Engl J Med. 2011;364(25):2381–91.PubMedCrossRefGoogle Scholar
  12. 12.
    Parkin DM, Boyd L, Walker LC. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010. Br J Cancer. 2011;105(S2):S77–81.PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Michailidou K, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353–61e2.PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Barnes DR, Antoniou AC. Unravelling modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers: update on genetic modifiers. J Intern Med. 2012;271(4):331–43.PubMedCrossRefGoogle Scholar
  15. 15.
    Pharoah PDP, et al. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008;358(26):2796–803.PubMedCrossRefGoogle Scholar
  16. 16.
    Gail MH, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81(24):1879–86.PubMedCrossRefGoogle Scholar
  17. 17.
    Claus EB, Risch N, Thompson WD. Autosomal dominant inheritance of early-onset breast cancer. Implications for risk prediction. Cancer. 1994;73(3):643–51.PubMedCrossRefGoogle Scholar
  18. 18.
    Berry DA, et al. BRCAPRO validation, sensitivity of genetic testing of BRCA1/BRCA2, and prevalence of other breast cancer susceptibility genes. J Clin Oncol. 2002;20(11):2701–12.PubMedCrossRefGoogle Scholar
  19. 19.
    Antoniou AC, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer. 2008;98(8):1457–66.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111–30.PubMedCrossRefGoogle Scholar
  21. 21.
    Moy L, et al. ACR appropriateness criteria stage I breast cancer: initial workup and surveillance for local recurrence and distant metastases in asymptomatic women. J Am Coll Radiol. 2014;11(12, Part A):1160–8.PubMedCrossRefGoogle Scholar
  22. 22.
    Fachal L, Dunning AM. From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. Curr Opin Genet Dev. 2015;30:32–41.PubMedCrossRefGoogle Scholar
  23. 23.
    Gail MH. Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model. J Natl Cancer Inst. 2009;101(13):959–63.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Schnitt SJ. Traditional and newer pathologic factors. J Natl Cancer Inst Monogr. 2001;30:22–6.CrossRefGoogle Scholar
  25. 25.
    Symmans WF. A pathologist’s perspective on emerging genomic tests for breast cancer. Semin Oncol. 2007;34(2 Suppl 3):S4–9.PubMedCrossRefGoogle Scholar
  26. 26.
    Sotiriou C, Pusztai Gene-Expression L. Signatures in breast cancer. N Engl J Med. 2009;360(8):790–800.PubMedCrossRefGoogle Scholar
  27. 27.
    Trop I, et al. Molecular classification of infiltrating breast cancer: toward personalized therapy. Radiographics. 2014;34(5):1178–95.PubMedCrossRefGoogle Scholar
  28. 28.
    Perou CM, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.PubMedCrossRefGoogle Scholar
  29. 29.
    Sorlie T, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98:10869–74.PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Sorlie T, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100:8418–23.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Sotiriou C, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A. 2003;100:10393–8.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Hu Z, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006;7(1):1–12.CrossRefGoogle Scholar
  33. 33.
    Walker RA. Immunohistochemical markers as predictive tools for breast cancer. J Clin Pathol. 2008;61(6):689–96.PubMedCrossRefGoogle Scholar
  34. 34.
    Ross JS, et al. Commercialized multigene predictors of clinical outcome for breast cancer. Oncologist. 2008;13(5):477–93.PubMedCrossRefGoogle Scholar
  35. 35.
    Paik S, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26.PubMedCrossRefGoogle Scholar
  36. 36.
    Paik S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor – positive breast cancer. J Clin Oncol. 2006;24(23):3726–34.PubMedCrossRefGoogle Scholar
  37. 37.
    Singletary SE, Connolly JL. Breast cancer staging: working with the sixth edition of the AJCC cancer staging manual. CA Cancer J Clin. 2006;56(1):37–47. quiz 50–1.PubMedCrossRefGoogle Scholar
  38. 38.
    Edge SB, Compton CC. The American Joint Committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17:1471–4. epub.PubMedCrossRefGoogle Scholar
  39. 39.
    Edge SB, et al. AJCC cancer staging manual. 7th ed. New York: Springer; 2010.Google Scholar
  40. 40.
    Elmore JG, et al. Screening for breast cancer. JAMA. 2005;293(10):1245–56.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    McDonald ES, et al. Baseline screening mammography: performance of full-field digital mammography versus digital breast tomosynthesis. Am J Roentgenol. 2015;205(5):1143–8.CrossRefGoogle Scholar
  42. 42.
    Friedewald SM, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014;311(24):2499–507.PubMedCrossRefGoogle Scholar
  43. 43.
    Stavros AT, et al. Solid breast nodules: use of sonography to distinguish between benign and malignant lesions. Radiology. 1995;196(1):123–34.PubMedCrossRefGoogle Scholar
  44. 44.
    Lehman CD, Schnall MD. Imaging in breast cancer: magnetic resonance imaging. Breast Cancer Res. 2005;7(5):215–9.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Lehman CD. Clinical indications: what is the evidence? Eur J Radiol. 2012;81 Suppl 1:S82–4.PubMedCrossRefGoogle Scholar
  46. 46.
    Saslow D, et al. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007;57(2):75–89.PubMedCrossRefGoogle Scholar
  47. 47.
    Keating NL, et al. Surveillance testing among survivors of early-stage breast cancer. J Clin Oncol. 2007;25(9):1074–81.PubMedCrossRefGoogle Scholar
  48. 48.
    Network NCC. NCCN clinical practice guidelines in oncology. Breast Cancer. Version 1. 2016. Available at
  49. 49.
    Rhodes DJ, et al. Dedicated dual-head gamma imaging for breast cancer screening in women with mammographically dense breasts. Radiology. 2011;258(1):106–18.PubMedCrossRefGoogle Scholar
  50. 50.
    Greene LR, Wilkinson D. The role of general nuclear medicine in breast cancer. J Med Radiat Sci. 2015;62(1):54–65.PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Brem RF, Rechtman LR. Nuclear medicine imaging of the breast: a novel, physiologic approach to breast cancer detection and diagnosis. Radiol Clin. 2010;48(5):1055–74.CrossRefGoogle Scholar
  52. 52.
    Mettler F, Guiberteau M. Essentials of nuclear medicine imaging. 6th ed. St. Louis: Mosby Elsevier; 2012.Google Scholar
  53. 53.
    Specht JM, Mankoff DA. Advances in molecular imaging for breast cancer detection and characterization. Breast Cancer Res: BCR. 2012;14(2):206.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Buscombe JR, et al. Uptake of Tc-99m MIBI related to tumour size and type. Anticancer Res. 1997;17(3B):1693–4.PubMedGoogle Scholar
  55. 55.
    Mankoff DA, et al. [Tc-99m]-sestamibi uptake and washout in locally advanced breast cancer are correlated with tumor blood flow. Nucl Med Biol. 2002;29(7):719–27.PubMedCrossRefGoogle Scholar
  56. 56.
    Scopinaro F, et al. Technetium-99m sestamibi: an indicator of breast cancer invasiveness. Eur J Nucl Med. 1994;21(9):984–7.PubMedCrossRefGoogle Scholar
  57. 57.
    O’Connor M, Rhodes D, Hruska C. Molecular breast imaging. Expert Rev Anticancer Ther. 2009;9(8):1073–80.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Hendrick RE. Radiation doses and cancer risks from breast imaging studies. Radiology. 2010;257(1):246–53.PubMedCrossRefGoogle Scholar
  59. 59.
    Avril N, et al. Glucose metabolism of breast cancer assessed by 18F-FDG PET: histologic and immunohistochemical tissue analysis. J Nucl Med. 2001;42(1):9–16.PubMedGoogle Scholar
  60. 60.
    Bos R, et al. Biologic correlates of 18fluorodeoxyglucose uptake in human breast cancer measured by positron emission tomography. J Clin Oncol. 2002;20(2):379–87.PubMedCrossRefGoogle Scholar
  61. 61.
    Scheidhauer K, Walter C, Seemann MD. FDG PET and other imaging modalities in the primary diagnosis of suspicious breast lesions. Eur J Nucl Med Mol Imaging. 2004;31 Suppl 1:S70–9.PubMedCrossRefGoogle Scholar
  62. 62.
    Kumar RS, et al. Clinicopathologic factors associated with false negative FDG-PET in primary breast cancer. Breast Cancer Res Treat. 2006;98:267–74.PubMedCrossRefGoogle Scholar
  63. 63.
    Rosen EL, Eubank WB, Mankoff DA. FDG PET, PET/CT, and breast cancer imaging. Radiographics. 2007;27 Suppl 1:S215–29.PubMedCrossRefGoogle Scholar
  64. 64.
    Raylman RR, et al. The positron emission mammography/tomography breast imaging and biopsy system (PEM/PET): design, construction and phantom-based measurements. Phys Med Biol. 2008;53(3):637–53.PubMedCrossRefGoogle Scholar
  65. 65.
    Berg WA, et al. Breast cancer: comparative effectiveness of positron emission mammography and MR imaging in presurgical planning for the ipsilateral breast. Radiology. 2011;258(1):59–72.PubMedPubMedCentralCrossRefGoogle Scholar
  66. 66.
    Rosen EL, et al. Detection of primary breast carcinoma with a dedicated, large-field-of-view FDG PET mammography device: initial experience. Radiology. 2005;234(2):527–34.PubMedCrossRefGoogle Scholar
  67. 67.
    Berg WA, et al. High-resolution fluorodeoxyglucose positron emission tomography with compression (“positron emission mammography”) is highly accurate in depicting primary breast cancer. Breast J. 2006;12(4):309–23.PubMedCrossRefGoogle Scholar
  68. 68.
    Newman EA, Newman LA. Lymphatic mapping techniques and sentinel lymph node biopsy in breast cancer. Surg Clin North Am. 2007;87(2):353–64. viii.PubMedCrossRefGoogle Scholar
  69. 69.
    Albertini J, et al. Lymphatic mapping and sentinel node biopsy in the patient with breast cancer. JAMA. 1996;276:1818–22.PubMedCrossRefGoogle Scholar
  70. 70.
    Giuliano AE, et al. Lymphatic mapping and sentinel lymphadenectomy for breast cancer. Ann Surg. 1994;220(3):391–8. discussion 398–401.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Krag DN, et al. Surgical resection and radiolocalization of the sentinel lymph node in breast cancer using a gamma probe. Surg Oncol. 1993;2(6):335–9. discussion 340.PubMedCrossRefGoogle Scholar
  72. 72.
    Lyman GH, et al. American Society of Clinical Oncology guideline recommendations for sentinel lymph node biopsy in early-stage breast cancer. J Clin Oncol. 2005;23(30):7703–20.PubMedCrossRefGoogle Scholar
  73. 73.
    Langer I, et al. Morbidity of sentinel lymph node biopsy (SLN) alone versus SLN and completion axillary lymph node dissection after breast cancer surgery: a prospective Swiss multicenter study on 659 patients. Ann Surg. 2007;245(3):452–61.PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Vanderveen KA, et al. Upstaging and improved survival of early breast cancer patients after implementation of sentinel node biopsy for axillary staging. Ann Surg Oncol. 2006;13(11):1450–6.PubMedCrossRefGoogle Scholar
  75. 75.
    Giuliano AE. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA. 2011;305(6):569–75.PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    Aarsvold JN, Alazraki NP. Update on detection of sentinel lymph nodes in patients with breast cancer. Semin Nucl Med. 2005;35(2):116–28.PubMedCrossRefGoogle Scholar
  77. 77.
    Wallace AM, et al. Lymphoseek: a molecular imaging agent for melanoma sentinel lymph node mapping. Ann Surg Oncol. 2007;14(2):913–21.PubMedCrossRefGoogle Scholar
  78. 78.
    Sugg SL, et al. Should internal mammary nodes be sampled in the sentinel lymph node era? Ann Surg Oncol. 2000;7:188–92.PubMedCrossRefGoogle Scholar
  79. 79.
    Estourgie SH, et al. Lymphatic drainage patterns from the breast. Ann Surg. 2004;239(2):232–7.PubMedPubMedCentralCrossRefGoogle Scholar
  80. 80.
    Farrus B, et al. Incidence of internal mammary node metastases after a sentinel lymph node technique in breast cancer and its implication in the radiotherapy plan. Int J Radiat Oncol Biol Phys. 2004;60(3):715–21.PubMedCrossRefGoogle Scholar
  81. 81.
    Paganelli G, et al. Internal mammary node lymphoscintigraphy and biopsy in breast cancer. Q J Nucl Med. 2002;46(2):138–44.PubMedGoogle Scholar
  82. 82.
    van Rijk MC, et al. Clinical implications of sentinel nodes outside the axilla and internal mammary chain in patients with breast cancer. J Surg Oncol. 2006;94(4):281–6.PubMedCrossRefGoogle Scholar
  83. 83.
    Yao MS, et al. Internal mammary nodal chain drainage is a prognostic indicator in axillary node-positive breast cancer. Ann Surg Oncol. 2007;14(10):2985–93.PubMedCrossRefGoogle Scholar
  84. 84.
    van der Ploeg IM, et al. Axillary and extra-axillary lymph node recurrences after a tumor-negative sentinel node biopsy for breast cancer using intralesional tracer administration. Ann Surg Oncol. 2008;15(4):1025–31.PubMedCrossRefGoogle Scholar
  85. 85.
    Manca G, et al. Sentinel lymph node mapping in breast cancer: a critical reappraisal of the internal mammary chain issue. Q J Nucl Med Mol Imaging. 2014;58(2):114–26.PubMedGoogle Scholar
  86. 86.
    Utech CI, Young CS, Winter PF. Prospective evaluation of fluorine-18 fluorodeoxyclucose positron emission tomography in breast cancer for staging of the axilla related to surgery and immunocytochemistry. Eur J Nucl Med. 1996;23(12):1588–93.PubMedCrossRefGoogle Scholar
  87. 87.
    Avril N, et al. Metabolic characterization of breast tumors with positron emission tomography using F-18 fluorodeoxyglucose. J Clin Oncol. 1996;14(6):1848–57.PubMedCrossRefGoogle Scholar
  88. 88.
    Veronesi P, Rodriguez-Fernandez J, Intra M. Controversies in the use of sentinel nodes: microinvasion, post surgery and after preoperative systemic treatment. Breast. 2007;16 Suppl 2:S67–70.PubMedCrossRefGoogle Scholar
  89. 89.
    Pritchard KI, et al. Prospective study of 2-[18F]fluorodeoxyglucose positron emission tomography in the assessment of regional nodal spread of disease in patients with breast cancer: an Ontario Clinical Oncology Group Study. J Clin Oncol. 2012;30(12):1274–9.PubMedCrossRefGoogle Scholar
  90. 90.
    Jeong YJ. Additional value of F-18 FDG PET/CT for initial staging in breast cancer with clinically negative axillary nodes. Breast Cancer Res Treat. 2014;145(1):137–42.PubMedCrossRefGoogle Scholar
  91. 91.
    Mankoff DA, et al. [18F]fluorodeoxyglucose positron emission tomography – computed tomography in breast cancer: when… when not? J Clin Oncol. 2012;30(12):1252–4.PubMedCrossRefGoogle Scholar
  92. 92.
    Perez EA, Foo ML, Fulmer JT. Management of locally advanced breast cancer. Oncology (Williston Park). 1997;11(9 Suppl 9):9–17.Google Scholar
  93. 93.
    Chia S, et al. Locally advanced and inflammatory breast cancer. J Clin Oncol. 2008;26(5):786–90.PubMedCrossRefGoogle Scholar
  94. 94.
    Carlson RW, McCormick B. Update: NCCN breast cancer clinical practice guidelines. J Natl Compr Canc Netw. 2005;3 Suppl 1:S7–11.PubMedGoogle Scholar
  95. 95.
    Groheux D, et al. 18F-FDG PET/CT in staging patients with locally advanced or inflammatory breast cancer: comparison to conventional staging. J Nucl Med. 2013;54(1):5–11.PubMedCrossRefGoogle Scholar
  96. 96.
    Jung SY, et al. Factors associated with mortality after breast cancer metastasis. Cancer Causes Control. 2011;23(1):103–12.PubMedCrossRefGoogle Scholar
  97. 97.
    Manders K, et al. Clinical management of women with metastatic breast cancer: a descriptive study according to age group. BMC Cancer. 2006;6:179.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Plunkett TA, Smith P, Rubens RD. Risk of complications from bone metastases in breast cancer. Eur J Cancer. 2000;36(4):476–82.PubMedCrossRefGoogle Scholar
  99. 99.
    Wei S, et al. Breast carcinomas with isolated bone metastases have different hormone receptor expression profiles than those with metastases to other sites or multiple organs. Ann Diagn Pathol. 2011;15(2):79–83.PubMedCrossRefGoogle Scholar
  100. 100.
    Crippa F, et al. Bone scintigraphy in breast cancer: a ten-year follow-up study. J Nucl Biol Med. 1993;37(2):57–61.PubMedGoogle Scholar
  101. 101.
    Hamaoka T, et al. Bone imaging in metastatic breast cancer. J Clin Oncol. 2004;22(14):2942–53.PubMedCrossRefGoogle Scholar
  102. 102.
    Savelli G, et al. Bone scintigraphy and the added value of SPECT (single photon emission tomography) in detecting skeletal lesions. Q J Nucl Med. 2001;45(1):27–37.PubMedGoogle Scholar
  103. 103.
    Even-Sapir E, et al. Role of SPECT in differentiating malignant from benign lesions in the lower thoracic and lumbar vertebrae. Radiology. 1993;187(1):193–8.PubMedCrossRefGoogle Scholar
  104. 104.
    Han LJ. Comparison of bone single-photon emission tomography and planar imaging in the detection of vertebral metastases in patients with back pain. Eur J Nucl Med. 1998;25(6):635–8.PubMedCrossRefGoogle Scholar
  105. 105.
    Sharma PP. Bone scintigraphy in breast cancer: added value of hybrid SPECT-CT and its impact on patient management. Nucl Med Commun. 2012;33(2):139–47.PubMedCrossRefGoogle Scholar
  106. 106.
    Utsunomiya D, et al. Added value of SPECT/CT fusion in assessing suspected bone metastasis: comparison with scintigraphy alone and nonfused scintigraphy and CT. Radiology. 2006;238(1):264–71.PubMedCrossRefGoogle Scholar
  107. 107.
    Palmedo HH. Whole-body SPECT/CT for bone scintigraphy: diagnostic value and effect on patient management in oncological patients. Eur J Nucl Med Mol Imaging. 2014;41(1):59–67.PubMedCrossRefGoogle Scholar
  108. 108.
    Uematsu T, et al. Comparison of FDG PET and SPECT for detection of bone metastases in breast cancer. AJR Am J Roentgenol. 2005;184(4):1266–73.PubMedCrossRefGoogle Scholar
  109. 109.
    Fogelman I, et al. Positron emission tomography and bone metastases. Semin Nucl Med. 2005;35(2):135–42.PubMedCrossRefGoogle Scholar
  110. 110.
    Even-Sapir E, Metser U, Flusser G, et al. Assessment of malignant skeletal disease: initial experience with F18-fluoride PET/CT and comparison between F18 fluoride PET and F18 fluoride PET/CT. J Nucl Med. 2004;45(2):272–8.PubMedGoogle Scholar
  111. 111.
    Schirrmeister H, et al. Early detection and accurate description of extent of metastatic bone disease in breast cancer with fluoride ion and positron emission tomography. J Clin Oncol. 1999;17(8):2381.PubMedCrossRefGoogle Scholar
  112. 112.
    Withofs NN. 18F-fluoride PET/CT for assessing bone involvement in prostate and breast cancers. Nucl Med Commun. 2011;32(3):168–76.PubMedCrossRefGoogle Scholar
  113. 113.
    Damle NA. The role of 18F-fluoride PET-CT in the detection of bone metastases in patients with breast, lung and prostate carcinoma: a comparison with FDG PET/CT and 99mTc-MDP bone scan. Jpn J Radiol. 2013;31(4):262–9.PubMedCrossRefGoogle Scholar
  114. 114.
    Yoon S-H, et al. Usefulness of 18F-fluoride PET/CT in breast cancer patients with osteosclerotic bone metastases. Nucl Med Mol Imaging. 2013;47(1):27–35.PubMedCrossRefGoogle Scholar
  115. 115.
    Even-Sapir E, et al. The detection of bone metastases in patients with high-risk prostate cancer: 99mTc-MDP planar bone scintigraphy, single- and multi-field-of-view SPECT, 18F-fluoride PET, and 18F-fluoride PET/CT. J Nucl Med. 2006;47(2):287–97.PubMedGoogle Scholar
  116. 116.
    Kjölhede H, et al. Combined 18F-fluorocholine and 18F-fluoride positron emission tomography/computed tomography imaging for staging of high-risk prostate cancer. BJU Int. 2012;110(10):1501–6.PubMedCrossRefGoogle Scholar
  117. 117.
    Langsteger WW. Fluorocholine (18F) and sodium fluoride (18F) PET/CT in the detection of prostate cancer: prospective comparison of diagnostic performance determined by masked reading. Q J Nucl Med Mol Imaging. 2011;55(4):448–57.PubMedGoogle Scholar
  118. 118.
    Hetzel M, et al. F-18 NaF PET for detection of bone metastases in lung cancer: accuracy, cost-effectiveness, and impact on patient management. J Bone Miner Res. 2003;18(12):2206–14.PubMedCrossRefGoogle Scholar
  119. 119.
    Sharma PP. 18F-Fluoride PET/CT for detection of bone metastasis in patients with renal cell carcinoma: a pilot study. Nucl Med Commun. 2014;35(12):1247–53.PubMedCrossRefGoogle Scholar
  120. 120.
    Chakraborty DD. Comparison of 18F fluoride PET/CT and 99mTc-MDP bone scan in the detection of skeletal metastases in urinary bladder carcinoma. Clin Nucl Med. 2013;38(8):616–21.PubMedCrossRefGoogle Scholar
  121. 121.
    Yen R-F. The diagnostic and prognostic effectiveness of F-18 sodium fluoride PET-CT in detecting bone metastases for hepatocellular carcinoma patients. Nucl Med Commun. 2010;31(7):637–45.PubMedGoogle Scholar
  122. 122.
    18F-fluoride PET/CT versus 99mTc-MDP scanning for detecting bone metastases: a randomized, multi-center trial to compare two bone imaging techniques. Available from:
  123. 123.
    18F – sodium fluoride pet imaging as a replacement for bone scintigraphy. Available at:
  124. 124.
    Bastawrous S, et al. Newer PET application with an old tracer: role of 18F-NaF skeletal PET/CT in oncologic practice. Radiographics. 2014;34(5):1295–316.PubMedCrossRefGoogle Scholar
  125. 125.
    Grant FD, et al. Skeletal PET with 18F-fluoride: applying new technology to an old tracer. J Nucl Med. 2008;49(1):68–78.PubMedCrossRefGoogle Scholar
  126. 126.
    Even-Sapir E. Imaging of malignant bone involvement by morphologic, scintigraphic, and hybrid modalities. J Nucl Med. 2005;46(8):1356–67.PubMedGoogle Scholar
  127. 127.
    Hahn S, et al. Comparison of FDG-PET/CT and bone scintigraphy for detection of bone metastases in breast cancer. Acta Radiol. 2011;52(9):1009–14.PubMedCrossRefGoogle Scholar
  128. 128.
    Ohta MM. Whole body PET for the evaluation of bony metastases in patients with breast cancer: comparison with 99Tcm-MDP bone scintigraphy. Nucl Med Commun. 2001;22(8):875–9.PubMedCrossRefGoogle Scholar
  129. 129.
    Shie PP. Meta-analysis: comparison of F-18 Fluorodeoxyglucose-positron emission tomography and bone scintigraphy in the detection of bone metastases in patients with breast cancer. Clin Nucl Med. 2008;33(2):97–101.PubMedCrossRefGoogle Scholar
  130. 130.
    Rong J, et al. Comparison of 18FDG PET-CT and bone scintigraphy for detection of bone metastases in breast cancer patients. A meta-analysis. Surg Oncol. 2013;22(2):86–91.PubMedCrossRefGoogle Scholar
  131. 131.
    Liu TT. A meta-analysis of 18FDG-PET, MRI and bone scintigraphy for diagnosis of bone metastases in patients with breast cancer. Skeletal Radiol. 2011;40(5):523–31.PubMedCrossRefGoogle Scholar
  132. 132.
    Cook GJ, et al. Detection of bone metastases in breast cancer by 18FDG PET: differing metabolic activity in osteoblastic and osteolytic lesions. J Clin Oncol. 1998;16(10):3375–9.PubMedCrossRefGoogle Scholar
  133. 133.
    Cook GJ, Azad GK, Goh V. Imaging bone metastases in breast cancer: staging and response assessment. J Nucl Med. 2016;57 Suppl 1:27S–33S.PubMedCrossRefGoogle Scholar
  134. 134.
    Abe KK. Comparison of 18FDG-PET with 99mTc-HMDP scintigraphy for the detection of bone metastases in patients with breast cancer. Ann Nucl Med. 2005;19(7):573–9.PubMedCrossRefGoogle Scholar
  135. 135.
    Nakai T, et al. Pitfalls of FDG-PET for the diagnosis of osteoblastic bone metastases in patients with breast cancer. Eur J Nucl Med Mol Imaging. 2005;32(11):1253–8.PubMedCrossRefGoogle Scholar
  136. 136.
    Morris PG, et al. Integrated positron emission tomography/computed tomography may render bone scintigraphy unnecessary to investigate suspected metastatic breast cancer. J Clin Oncol. 2010;28(19):3154–9.PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Groheux D, et al. 18F-FDG PET/CT for staging and restaging of breast cancer. J Nucl Med. 2016;57 Suppl 1:17S–26.PubMedCrossRefGoogle Scholar
  138. 138.
    van der Hoeven JJ, et al. 18F-2-fluoro-2-deoxy-d-glucose positron emission tomography in staging of locally advanced breast cancer. J Clin Oncol. 2004;22(7):1253–9.PubMedCrossRefGoogle Scholar
  139. 139.
    Carkaci S, et al. Retrospective study of 18F-FDG PET/CT in the diagnosis of inflammatory breast cancer: preliminary data. J Nucl Med. 2009;50(2):231–8.PubMedCrossRefGoogle Scholar
  140. 140.
    Alberini J-L, et al. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) imaging in the staging and prognosis of inflammatory breast cancer. Cancer. 2009;115(21):5038–47.PubMedCrossRefGoogle Scholar
  141. 141.
    Walker GV, et al. Pretreatment staging positron emission tomography/computed tomography in patients with inflammatory breast cancer influences radiation treatment field designs. Int J Radiat Oncol Biol Phys. 2012;83(5):1381–6.PubMedCrossRefGoogle Scholar
  142. 142.
    Cochet AA. 18F-FDG PET/CT provides powerful prognostic stratification in the primary staging of large breast cancer when compared with conventional explorations. Eur J Nucl Med Mol Imaging. 2014;41(3):428–37.PubMedCrossRefGoogle Scholar
  143. 143.
    Groheux D, et al. Prognostic impact of 18FDG-PET-CT findings in clinical stage III and IIB breast cancer. J Natl Cancer Inst. 2012;104(24):1879–87.PubMedPubMedCentralCrossRefGoogle Scholar
  144. 144.
    Vazquez B, Rousseau D, Hurd TC. Surgical management of breast cancer. Semin Oncol. 2007;34(3):234–40.PubMedCrossRefGoogle Scholar
  145. 145.
    Fisher B. From Halsted to prevention and beyond: advances in the management of breast cancer during the twentieth century. Eur J Cancer. 1999;35(14):1963–73.PubMedCrossRefGoogle Scholar
  146. 146.
    Gervasoni Jr JE, Sbayi S, Cady B. Role of lymphadenectomy in surgical treatment of solid tumors: an update on the clinical data. Ann Surg Oncol. 2007;14(9):2443–62.PubMedCrossRefGoogle Scholar
  147. 147.
    Lucci A, et al. Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group Trial Z0011. J Clin Oncol. 2007;25(24):3657–63.PubMedCrossRefGoogle Scholar
  148. 148.
    Carlson RW, et al. NCCN task force report: adjuvant therapy for breast cancer. J Natl Compr Canc Netw. 2006;4 Suppl 1:S1–26.Google Scholar
  149. 149.
    Come SE, et al. Endocrine and targeted manipulation of breast cancer: summary statement for the Sixth Cambridge Conference. Cancer. 2008;112 Suppl 3:673–8.PubMedCrossRefGoogle Scholar
  150. 150.
    Gralow JR. Optimizing the treatment of metastatic breast cancer. Breast Cancer Res Treat. 2005;89 Suppl 1:S9–15.PubMedCrossRefGoogle Scholar
  151. 151.
    Perez EA, et al. Trastuzumab plus adjuvant chemotherapy for human epidermal growth factor receptor 2 – positive breast cancer: planned joint analysis of overall survival from NSABP B-31 and NCCTG N9831. J Clin Oncol. 2014;32(33):3744–52.PubMedPubMedCentralCrossRefGoogle Scholar
  152. 152.
    Moasser MM. Targeting the function of the HER2 oncogene in human cancer therapeutics. Oncogene. 2007;26(46):6577–92.PubMedPubMedCentralCrossRefGoogle Scholar
  153. 153.
    Slamon DJ, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001;344(11):783–92.PubMedCrossRefGoogle Scholar
  154. 154.
    Clark O, et al. Targeted therapy in triple-negative metastatic breast cancer: a systematic review and meta-analysis. Core Evid. 2014;9:1–11.PubMedPubMedCentralCrossRefGoogle Scholar
  155. 155.
    Lord CJ, Tutt ANJ, Ashworth A. Synthetic lethality and cancer therapy: lessons learned from the development of PARP inhibitors. Annu Rev Med. 2015;66(1):455–70.PubMedCrossRefGoogle Scholar
  156. 156.
    Baselga J, et al. Everolimus in postmenopausal hormone-receptor – positive advanced breast cancer. N Engl J Med. 2012;366(6):520–9.PubMedCrossRefGoogle Scholar
  157. 157.
    Finn RS, et al. The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. Lancet Oncol. 2015;16(1):25–35.PubMedCrossRefGoogle Scholar
  158. 158.
    Ma CX, et al. A phase II study of UCN-01 in combination with irinotecan in patients with metastatic triple negative breast cancer. Breast Cancer Res Treat. 2013;137(2):483–92.PubMedCrossRefGoogle Scholar
  159. 159.
    Hu XC. Cisplatin plus gemcitabine versus paclitaxel plus gemcitabine as first-line therapy for metastatic triple-negative breast cancer (CBCSG006): a randomised, open-label, multicentre, phase 3 trial. Lancet Oncol. 2015;16(4):436–46.PubMedCrossRefGoogle Scholar
  160. 160.
    Bellon JR, Katz A, Taghian A. Radiation therapy for breast cancer. Hematol Oncol Clin North Am. 2006;20(2):239–57. vii.PubMedCrossRefGoogle Scholar
  161. 161.
    Ragaz J, et al. Adjuvant radiotherapy and chemotherapy in node-positive premenopausal women with breast cancer. N Engl J Med. 1997;337(14):956–62.PubMedCrossRefGoogle Scholar
  162. 162.
    Feldman LD, et al. Pathological assessment of response to induction chemotherapy in breast cancer. Cancer Res. 1986;46:2578–81.PubMedGoogle Scholar
  163. 163.
    McCready DR, et al. The prognostic significance of lymph node metastases after preoperative chemotherapy for locally advanced breast cancer. Arch Surg. 1989;124:21–5.PubMedCrossRefGoogle Scholar
  164. 164.
    Wolmark N, et al. Preoperative chemotherapy in patients with operable breast cancer: nine-year results from National Surgical Adjuvant Breast and Bowel Project B-18. J Natl Cancer Inst Monogr. 2001;30:96–102.CrossRefGoogle Scholar
  165. 165.
    Eisenhauer EA, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.PubMedCrossRefGoogle Scholar
  166. 166.
    Wahl RL, et al. Metabolic monitoring of breast cancer chemohormonotherapy using positron emission tomography: initial evaluation. J Clin Oncol. 1993;11(11):2101–11.PubMedCrossRefGoogle Scholar
  167. 167.
    Mankoff DA, Dunnwald LK. Changes in glucose metabolism and blood flow following chemotherapy for breast cancer. PET Clin. 2005;1:71–82.CrossRefGoogle Scholar
  168. 168.
    Avril N, Sassen S, Roylance R. Response to therapy in breast cancer. J Nucl Med. 2009;50 Suppl 1:55S–63.PubMedCrossRefGoogle Scholar
  169. 169.
    Wang YY. Is 18F-FDG PET accurate to predict neoadjuvant therapy response in breast cancer? A meta-analysis. Breast Cancer Res Treat. 2012;131(2):357–69.PubMedCrossRefGoogle Scholar
  170. 170.
    Specht JM, et al. Tumor metabolism and blood flow as assessed by positron emission tomography varies by tumor subtype in locally advanced breast cancer. Clin Cancer Res. 2010;16(10):2803–10.PubMedPubMedCentralCrossRefGoogle Scholar
  171. 171.
    Groheux D, et al. HER2-overexpressing breast cancer: FDG uptake after two cycles of chemotherapy predicts the outcome of neoadjuvant treatment. Br J Cancer. 2013;109(5):1157–64.PubMedPubMedCentralCrossRefGoogle Scholar
  172. 172.
    Humbert OO. HER2-positive breast cancer: 18F-FDG PET for early prediction of response to trastuzumab plus taxane-based neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2014;41(8):1525–33.PubMedCrossRefGoogle Scholar
  173. 173.
    Groheux D, et al. Triple-negative breast cancer: early assessment with 18F-FDG PET/CT during neoadjuvant chemotherapy identifies patients who are unlikely to achieve a pathologic complete response and are at a high risk of early relapse. J Nucl Med. 2012;53(2):249–54.PubMedCrossRefGoogle Scholar
  174. 174.
    Humbert OO. Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using 18F-FDG PET in luminal HER2-negative breast cancer. Eur J Nucl Med Mol Imaging. 2014;41(3):416–27.PubMedCrossRefGoogle Scholar
  175. 175.
    Groheux D, et al. Baseline tumor 18F-FDG uptake and modifications after 2 cycles of neoadjuvant chemotherapy are prognostic of outcome in ER+/HER2 − breast cancer. J Nucl Med. 2015;56(6):824–31.PubMedCrossRefGoogle Scholar
  176. 176.
    Dunnwald LK, et al. PET tumor metabolism in locally advanced breast cancer patients undergoing neoadjuvant chemotherapy: value of static versus kinetic measures of fluorodeoxyglucose uptake. Clin Cancer Res. 2011;17(8):2400–9.PubMedPubMedCentralCrossRefGoogle Scholar
  177. 177.
    Boellaard R. Need for standardization of 18F-FDG PET/CT for treatment response assessments. J Nucl Med. 2011;52 Suppl 2:93S–100.PubMedCrossRefGoogle Scholar
  178. 178.
    Groheux D, et al. Early metabolic response to neoadjuvant treatment: FDG PET/CT criteria according to breast cancer subtype. Radiology. 2015;277(2):358–71.PubMedCrossRefGoogle Scholar
  179. 179.
    Bassa P, et al. Evaluation of preoperative chemotherapy using PET with fluorine-18-fluorodeoxyglucose in breast cancer. J Nucl Med. 1996;37:931–8.PubMedGoogle Scholar
  180. 180.
    Burcombe RJ, et al. Evaluation of good clinical response to neoadjuvant chemotherapy in primary breast cancer using [18F]-fluorodeoxyglucose positron emission tomography. Eur J Cancer. 2002;38(3):375–9.PubMedCrossRefGoogle Scholar
  181. 181.
    Kim SJ, et al. Predictive value of [18F]FDG PET for pathological response of breast cancer to neo-adjuvant chemotherapy. Ann Oncol. 2004;15(9):1352–7.PubMedCrossRefGoogle Scholar
  182. 182.
    Emmering J, et al. Preoperative [18F]FDG-PET after chemotherapy in locally advanced breast cancer: prognostic value as compared with histopathology. Ann Oncol. 2008;19:1573–7.PubMedCrossRefGoogle Scholar
  183. 183.
    Couturier O, et al. Sequential positron emission tomography using [18F]fluorodeoxyglucose for monitoring response to chemotherapy in metastatic breast cancer. Clin Cancer Res. 2006;12(21):6437–43.PubMedCrossRefGoogle Scholar
  184. 184.
    Dose Schwarz J, et al. Early prediction of response to chemotherapy in metastatic breast cancer using sequential 18F-FDG PET. J Nucl Med. 2005;46(7):1144–50.PubMedGoogle Scholar
  185. 185.
    Gennari A, et al. Role of 2-[18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the early assessment of response to chemotherapy in metastatic breast cancer patients. Clin Breast Cancer. 2000;1(2):156–61. discussion 162–3.PubMedCrossRefGoogle Scholar
  186. 186.
    Mortazavi-Jehanno N, et al. Assessment of response to endocrine therapy using FDG PET/CT in metastatic breast cancer: a pilot study. Eur J Nucl Med Mol Imaging. 2011;39(3):450–60.PubMedCrossRefGoogle Scholar
  187. 187.
    Lin NU, et al. Phase II study of lapatinib in combination with trastuzumab in patients with human epidermal growth factor receptor 2 – positive metastatic breast cancer: clinical outcomes and predictive value of early [18F]fluorodeoxyglucose positron emission tomography imaging (TBCRC 003). J Clin Oncol. 2015;33(24):2623–31.PubMedPubMedCentralCrossRefGoogle Scholar
  188. 188.
    Coleman RE, et al. Bone scan flare predicts successful systemic therapy for bone metastases. J Nucl Med. 1988;29(8):1354–9.PubMedGoogle Scholar
  189. 189.
    Schneider JA, et al. Flare on bone scintigraphy following taxol chemotherapy for metastatic breast cancer. J Nucl Med. 1994;35(11):1748–52.PubMedGoogle Scholar
  190. 190.
    Wade AA, et al. Flare response in 18F-fluoride ion PET bone scanning. Am J Roentgenol. 2006;186(6):1783–6.CrossRefGoogle Scholar
  191. 191.
    Cook GJ, Taylor B, Glendenning J, et al. Heterogeneity of treatment response in skeletal metastases from breast cancer in 18F-fluoride and 18F-FDG PET. Nucl Med Commun. 2015;36:515–16.CrossRefGoogle Scholar
  192. 192.
    Hillner BE, et al. 18F-fluoride PET used for treatment monitoring of systemic cancer therapy: results from the national oncologic PET registry. J Nucl Med. 2015;56(2):222–8.PubMedCrossRefGoogle Scholar
  193. 193.
    Stafford SE, et al. Use of serial FDG PET to measure the response of bone-dominant breast cancer to therapy. Acad Radiol. 2002;9(8):913–21.PubMedCrossRefGoogle Scholar
  194. 194.
    Specht JM, et al. Serial 2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) to monitor treatment of bone-dominant metastatic breast cancer predicts time to progression (TTP). Breast Cancer Res Treat. 2007;105(1):87–94.PubMedCrossRefGoogle Scholar
  195. 195.
    Tateishi U, et al. Bone metastases in patients with metastatic breast cancer: morphologic and metabolic monitoring of response to systemic therapy with integrated PET/CT. Radiology. 2008;247(1):189–96.PubMedCrossRefGoogle Scholar
  196. 196.
    Osborne CK, et al. The value of estrogen and progesterone receptors in the treatment of breast cancer. Cancer. 1980;46 Suppl 12:2884–8.PubMedCrossRefGoogle Scholar
  197. 197.
    van Kruchten M, et al. PET imaging of oestrogen receptors in patients with breast cancer. Lancet Oncol. 2013;14(11):e465–75.PubMedCrossRefGoogle Scholar
  198. 198.
    Mankoff DA. How imaging can impact clinical trial design: molecular imaging as a biomarker for targeted cancer therapy. Cancer J (Sudbury, Mass). 2015;21(3):218–24.CrossRefGoogle Scholar
  199. 199.
    Katzenellenbogen JA, Welch MJ, Dehdashti F. The development of estrogen and progestin radiopharmaceuticals for imaging breast cancer. Anticancer Res. 1997;17:1573–6.PubMedGoogle Scholar
  200. 200.
    Mintun MA, et al. Breast cancer: PET imaging of estrogen receptors. Radiology. 1988;169(1):45–8.PubMedCrossRefGoogle Scholar
  201. 201.
    Peterson LM, et al. Quantitative imaging of estrogen receptor expression of breast cancer with PET and 18F-fluoroestradiol. J Nucl Med. 2008;49:367–74. in press.PubMedCrossRefGoogle Scholar
  202. 202.
    Fowler AM, et al. Imaging diagnostic and therapeutic targets: steroid receptors in breast cancer. J Nucl Med. 2016;57 Suppl 1:75S–80.PubMedPubMedCentralCrossRefGoogle Scholar
  203. 203.
    Clark AS. Using nuclear medicine imaging in clinical practice: update on PET to guide treatment of patients with metastatic breast cancer. Oncology (Williston Park). 2014;28(5):424–30.Google Scholar
  204. 204.
    Hammond MEH, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol. 2010;28(16):2784–95.PubMedPubMedCentralCrossRefGoogle Scholar
  205. 205.
    Chung GG, et al. Quantitative analysis of estrogen receptor heterogeneity in breast cancer. Lab Invest. 2007;87(7):662–9.PubMedCrossRefGoogle Scholar
  206. 206.
    Amir E, et al. Prospective study evaluating the impact of tissue confirmation of metastatic disease in patients with breast cancer. J Clin Oncol. 2012;30(6):587–92.PubMedCrossRefGoogle Scholar
  207. 207.
    Dehdashti F, et al. Assessment of progesterone receptors in breast carcinoma by PET with 21-18F-fluoro-16α,17α-[(R)-(1′-α-furylmethylidene)dioxy]-19-norpregn-4-Ene-3,20-dione. J Nucl Med. 2012;53(3):363–70.PubMedPubMedCentralCrossRefGoogle Scholar
  208. 208.
    Keen JC. The biology of breast carcinoma. Cancer. 2003;97 Suppl 3:825–33.PubMedCrossRefGoogle Scholar
  209. 209.
    Linden HM, Dehdashti F. Novel methods and tracers for breast cancer imaging. Semin Nucl Med. 2013;43(4):324–9.PubMedCrossRefGoogle Scholar
  210. 210.
    Dijkers EC, et al. Development and characterization of clinical-grade 89Zr-trastuzumab for HER2/neu ImmunoPET imaging. J Nucl Med. 2009;50(6):974–81.PubMedCrossRefGoogle Scholar
  211. 211.
    Dijkers EC. Biodistribution of 89Zr-trastuzumab and PET imaging of HER2-positive lesions in patients with metastatic breast cancer. Clin Pharmacol Ther. 2010;87(5):586–92.PubMedCrossRefGoogle Scholar
  212. 212.
    Verma S, et al. Trastuzumab emtansine for HER2-positive advanced breast cancer. N Engl J Med. 2012;367(19):1783–91.PubMedPubMedCentralCrossRefGoogle Scholar
  213. 213.
    Gebhart G, et al. Molecular imaging as a tool to investigate heterogeneity of advanced HER2-positive breast cancer and to predict patient outcome under trastuzumab emtansine (T-DM1): the ZEPHIR trial. Ann Oncol. 2016; 27(4):619–24.Google Scholar
  214. 214.
    Clark AS, DeMichele A, Mankoff D. HER2 imaging in the ZEPHIR study. Ann Oncol. 2016; 27(4):555–7.Google Scholar
  215. 215.
    Rousseau C, et al. Monitoring of early response to neoadjuvant chemotherapy in stage II and III breast cancer by [18F]fluorodeoxyglucose positron emission tomography. J Clin Oncol. 2006;24(34):5366–72.PubMedCrossRefGoogle Scholar
  216. 216.
    Schelling M, et al. Positron emission tomography using [18F]fluorodeoxyglucose for monitoring primary chemotherapy in breast cancer. J Clin Oncol. 2000;18:1689–95.PubMedCrossRefGoogle Scholar
  217. 217.
    Smith I, et al. Positron emission tomography using [18F]-fluorodeoxy-d-glucose to predict the pathologic response of breast cancer to primary chemotherapy. J Clin Oncol. 2000;18:1676–88.PubMedCrossRefGoogle Scholar
  218. 218.
    Migliaccio II. Cyclin-dependent kinase 4/6 inhibitors in breast cancer therapy. Curr Opin Oncol. 2014;26(6):568–75.PubMedCrossRefGoogle Scholar
  219. 219.
    Kenny L, et al. Imaging early changes in proliferation at 1 week post chemotherapy: a pilot study in breast cancer patients with 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography. Eur J Nucl Med Mol Imaging. 2007;34(9):1339–47.PubMedCrossRefGoogle Scholar
  220. 220.
    Kenny L. The use of novel PET tracers to image breast cancer biologic processes such as proliferation, DNA damage and repair, and angiogenesis. J Nucl Med. 2016;57 Suppl 1:89S–95.PubMedCrossRefGoogle Scholar
  221. 221.
    Kostakoglu L, et al. A phase II study of 3′-deoxy-3′-18F-fluorothymidine PET in the assessment of early response of breast cancer to neoadjuvant chemotherapy: results from ACRIN 6688. J Nucl Med. 2015;56(11):1681–9.PubMedPubMedCentralCrossRefGoogle Scholar
  222. 222.
    Dehdashti F, et al. Assessment of cellular proliferation in tumors by PET using 18F-ISO-1. J Nucl Med. 2013;54(3):350–7.PubMedPubMedCentralCrossRefGoogle Scholar
  223. 223.
    Shoghi KI, et al. Quantitative receptor-based imaging of tumor proliferation with the sigma-2 ligand [18F]ISO-1. PLoS One. 2013;8(9):e74188.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marsha Camilla Lynch
    • 1
    • 2
  • Jean H. Lee
    • 3
    • 4
  • David A. Mankoff
    • 1
    • 2
    Email author
  1. 1.Division of Nuclear Medicine, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Division of Nuclear Medicine and Clinical Molecular ImagingUniversity of Pennsylvania Health SystemPhiladelphiaUSA
  3. 3.Department of RadiologyUniversity of WashingtonSeattleUSA
  4. 4.Seattle Cancer Care AllianceSeattleUSA

Personalised recommendations