[18F]FDG PET/CT features for the molecular characterization of primary breast tumors

  • Lidija Antunovic
  • Francesca Gallivanone
  • Martina Sollini
  • Andrea Sagona
  • Alessandra Invento
  • Giulia Manfrinato
  • Margarita Kirienko
  • Corrado Tinterri
  • Arturo ChitiEmail author
  • Isabella Castiglioni
Original Article



The aim of this study was to evaluate the role of imaging features derived from [18F]FDG-PET/CT to provide in vivo characterization of breast cancer (BC).


Images from 43 patients with a first diagnosis of BC were reviewed. Images were acquired before any treatment. Histological data were derived from pretreatment biopsy or surgical histological specimen; these included tumor type, grade, ER and PgR receptor status, lymphovascular invasion, Ki67 index, HER2 status, and molecular subtype. Standard parameters (SUVmean, TLG, MTV) and advanced imaging features (histogram-based and shape and size features) were evaluated. Univariate analysis, hierarchical clustering analysis, and exact Fisher’s test were used for statistical analysis of data. Imaging-derived metrics were reduced evaluating the mutual correlation within group of features as well as the mutual correlation between groups of features to form a signature.


A significant correlation was found between some advanced imaging features and the histological type. Different molecular subtypes were characterized by different values of two histogram-based features (median and energy). A significant association was observed between the imaging signature and luminal A and luminal B HER2 negative molecular subtype and also when considering luminal A, luminal B HER2-negative and HER2-positive groups. Similar results were found between the signature and all five molecular subtypes and also when considering the histological types of BC.


Our results suggest a complementary role of standard PET imaging parameters and advanced imaging features for the in vivo biological characterization of BC lesions.


Breast cancer [18F]FDG-pet/Ct Radiomics 


Compliance with ethical standards


This study was not funded.

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

This retrospective study was approved by the local Ethics Committee.

Informed consent

Informed consent for this retrospective study was obtained from all individual participants included in the study, as part of the consent signed when admitted in the hospital or when submitted to diagnostic procedures.


  1. 1.
    Breast cancer. In EUCAN facktsheets. International Agency for Research on Cancer. Accessed 15 Jan 2017.
  2. 2.
    Melchor L, Benítez J. The complex genetic landscape of familial breast cancer. Hum Genet. 2013;132:845–63.CrossRefPubMedGoogle Scholar
  3. 3.
    Cybulski C, Wokolorczyk D, Jakubowska A, et al. Risk of breast cancer in women with a CHEK2 mutation with and without a family history of breast cancer. J Clin Oncol. 2011;29:3747–52.CrossRefPubMedGoogle Scholar
  4. 4.
    van de Vijver MJ, He YD, van't Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009.CrossRefPubMedGoogle Scholar
  5. 5.
    Cheang MC, Chia SK, Voduc D, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009;101:736–50.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Esposito A, Criscitiello C, Curigliano G. Highlights from the 14(th) St Gallen international breast cancer conference 2015 in Vienna: dealing with classification, prognostication, and prediction refinement to personalize the treatment of patients with early breast cancer. Ecancermedicalscience. 2015;9:518.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Kennecke H, Yerushalmi R, Woods R, et al. Metastatic behavior of breast cancer subtypes. J Clin Oncol. 2010;28:3271–7.CrossRefPubMedGoogle Scholar
  8. 8.
    Gallivanone F, Canevari C, Sassi I, et al. Partial volume corrected 18F-FDG PET mean standardized uptake value correlates with prognostic factors in breast cancer. Q J Nucl Med Mol Imaging. 2014;58:424–39.PubMedGoogle Scholar
  9. 9.
    Kaida H, Toh U, Hayakawa M, et al. The relationship between 18F-FDG metabolic volumetric parameters and clinicopathological factors of breast cancer. Nucl Med Commun. 2013;34:562–70.CrossRefPubMedGoogle Scholar
  10. 10.
    García Vicente AM, Soriano Castrejón Á, León Martín A, et al. Molecular subtypes of breast cancer: metabolic correlation with 18F-FDG PET/CT. Eur J Nucl Med Mol Imaging. 2013;40:1304–11.CrossRefPubMedGoogle Scholar
  11. 11.
    Koo HR, Park JS, Kang KW, et al. 18F-FDG uptake in breast cancer correlates with immunohistochemically defined subtypes. Eur Radiol. 2014;24:610–8.CrossRefPubMedGoogle Scholar
  12. 12.
    Kajáry K, Tőkés T, Dank M, et al. Correlation of the value of 118F-FDG uptake, described by SUVmax, SUVavg, metabolic tumour volume and total lesion glycolysis, to clinicopathological prognostic factors and biological subtypes in breast cancer. Nucl Med Commun. 2015;36:28–37.CrossRefPubMedGoogle Scholar
  13. 13.
    Kitajima K, Fukushima K, Miyoshi Y, et al. Association between 18F-FDG uptake and molecular subtype of breast cancer. Eur J Nucl Med Mol Imaging. 2015;42:1371–7.CrossRefPubMedGoogle Scholar
  14. 14.
    Lee SS, Bae SK, Park YS, et al. Correlation of molecular subtypes of invasive ductal carcinoma of breast with glucose metabolism in FDG PET/CT: based on the recommendations of the St. Gallen consensus meeting 2013. Nucl Med Mol Imaging. 2017;51:79–85.CrossRefPubMedGoogle Scholar
  15. 15.
    Leijenaar RT, Carvalho S, Hoebers FJ, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 2015;54:1423–9.CrossRefPubMedGoogle Scholar
  16. 16.
    Carvalho S, Leijenaar RT, Velazquez ER, et al. Prognostic value of metabolic metrics extracted from baseline PET images in NSCLC in non small cell lung cancer. Acta Oncol. 2015;52:1398–404.CrossRefGoogle Scholar
  17. 17.
    Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60:5471–96.CrossRefPubMedGoogle Scholar
  18. 18.
    Son SH, Kim DH, Hong CM, et al. Prognostic implication of intratumoral metabolic heterogeneity in invasive ductal carcinoma of the breast. BMC Cancer. 2014;14:585.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Shin S, Pak K, Park DY, Kim SJ. Tumor heterogeneity assessed by 18F-FDG PET/CT is not significantly associated with nodal metastasis in breast cancer patients. Oncol Res Treat. 2015;39:61–6.CrossRefPubMedGoogle Scholar
  20. 20.
    Soussan M, Orlhac F, Boubaya M, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9(4):e94017.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Yoon HJ, Kim Y, Kim BS. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol. 2015;25:3648–58.CrossRefPubMedGoogle Scholar
  22. 22.
    Agner SC, Rosen MA, Englander S, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. Radiology. 2014;272:91–9.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: luminal a and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging. 2015;42:902–7.CrossRefPubMedGoogle Scholar
  24. 24.
    Yamaguchi K, Abe H, Newstead GM, et al. Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer. Breast Cancer. 2015;22:496–502.CrossRefPubMedGoogle Scholar
  25. 25.
    Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016;2:16012.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Groheux D, Majdoub M, Tixier F, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different 18F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging. 2015;42:1682–91.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Koo HR, Park JS, Kang KW, et al. Correlation between 18F-FDG uptake on PET/CT and prognostic factors in triple-negative breast cancer. Eur Radiol. 2015;25:3314–21.CrossRefPubMedGoogle Scholar
  28. 28.
    Ha S, Park S, Bang J-I, et al. Metabolic radiomics for pretreatment (18)F-FDG PET/CT to characterize locally advanced breast cancer: histopathologic characteristics, response to neoadjuvant chemotherapy, and prognosis. Sci Rep. 2017;7:1556.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Pinder SE, Provenzano E, Earl H, Ellis IO. Laboratory handling and histology reporting of breast specimens from patients who have received neoadjuvant chemotherapy. Histopathology. 2007;50:409–17.CrossRefPubMedGoogle Scholar
  30. 30.
    Boellaard R, Delgado-Bolton R, Oyen WJG, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.CrossRefPubMedGoogle Scholar
  31. 31.
    Gallivanone F, Interlenghi M, Canervari C, Castiglioni I. A fully automatic, threshold-based segmentation method for the estimation of the metabolic tumor volume from PET images: validation on 3D printed anthropomorphic oncological lesions. J Instrum. 2016;11:C01022.CrossRefGoogle Scholar
  32. 32.
    Gallivanone F, Stefano A, Grosso E, et al. PVE correction in PET-CT whole-body oncological studies from PVE-affected images. IEEE Trans Nucl Sci. 2011;58:736–47.CrossRefGoogle Scholar
  33. 33.
    Gallivanone F, Canevari C, Gianolli L, et al. A partial volume effect correction tailored for 18 F-FDG-PET oncological studies. Biomed Res Int. 2013;2013:1–12.CrossRefGoogle Scholar
  34. 34.
    Gallivanone F, Interlenghi M, D’Ambrosio D, et al. An anthropomorphic phantom for advanced image processing of realistic 18F–FDG PET-CT oncological studies. IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) M04D-20. 2016.Google Scholar
  35. 35.
    Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Chen X, Ma C, Wu J, et al. Molecular subtype approximated by quantitative estrogen receptor, progesterone receptor and Her2 can predict the prognosis of breast cancer. Tumori. 2010;96:103–10.PubMedGoogle Scholar
  37. 37.
    Groheux D, Giacchetti S, Moretti JL, et al. Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. Eur J Nucl Med Mol Imaging. 2011;38:426–35.CrossRefPubMedGoogle Scholar
  38. 38.
    Zhao YH, Zhou M, Liu H, et al. Upregulation of lactate dehydrogenase a by ErbB2 through heat shock factor 1 promotes breast cancer cell glycolysis and growth. Oncogene. 2009;28:3689–701.CrossRefPubMedGoogle Scholar
  39. 39.
    Senkus E, Kyriakides S, Ohno S, et al. Primary breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26:v8–30.CrossRefPubMedGoogle Scholar
  40. 40.
    Tixier F, Le Rest CC, Hatt M, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Van Velden FHP, Cheebsumon P, Yaqub M, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging. 2011;38:1636–47.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Hatt M, Tixier F, Cheze Le Rest C, et al. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71.CrossRefPubMedGoogle Scholar
  43. 43.
    Sollini M, Cozzi L, Antunovic L, et al. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep. 2017;7:358.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Lidija Antunovic
    • 1
  • Francesca Gallivanone
    • 2
  • Martina Sollini
    • 3
  • Andrea Sagona
    • 4
  • Alessandra Invento
    • 5
  • Giulia Manfrinato
    • 6
  • Margarita Kirienko
    • 3
  • Corrado Tinterri
    • 4
  • Arturo Chiti
    • 1
    • 3
    Email author return OK on get
  • Isabella Castiglioni
    • 2
  1. 1.Nuclear Medicine DepartmentHumanitas Research HospitalMilanItaly
  2. 2.Laboratory of Innovation and Integration in Molecular Medicine, Institute of Molecular Bioimaging and PhysiologyNational Research CouncilMilanItaly
  3. 3.Department of Biomedical SciencesHumanitas UniversityMilanItaly
  4. 4.Breast UnitHumanitas Research HospitalMilanItaly
  5. 5.Breast UnitIntegrated University HospitalVeronaItaly
  6. 6.Residency Program in Nuclear MedicineUniversity of MilanMilanItaly

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