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?

  • David Groheux
  • Mohamed Majdoub
  • Florent Tixier
  • Catherine Cheze Le Rest
  • Antoine Martineau
  • Pascal Merlet
  • Marc Espié
  • Anne de Roquancourt
  • Elif Hindié
  • Mathieu HattEmail author
  • Dimitris Visvikis
Original Article



The aim of this retrospective study was to determine if some features of baseline 18F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC).


Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment 18F-FDG PET images. The parameters extracted included SUVmax, SUVmean, metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and 18F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics.


T3 tumours (>5 cm) exhibited higher textural heterogeneity in 18F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUVmax and SUVmean. Invasive ductal carcinoma showed higher SUVmax values than invasive lobular carcinoma (p = 0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUVmax and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUVmax, SUVmean and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative.


SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.


18F-FDG PET/CT Heterogeneity Textural features Breast cancer 


Compliance with ethical standards


This work received French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the "Investing for the Future" programme under reference ANR-10-LABX-07-01.

Conflicts of interest


Research involving human participants and/or animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. For this retrospective study formal consent was not required.

Supplementary material

259_2015_3110_MOESM1_ESM.doc (60 kb)
Supplemental Table 1 (DOC 60 kb)


  1. 1.
    Groheux D, Hindié E, Delord M, Giacchetti S, Hamy A, de Bazelaire C, et al. Prognostic impact of (18)FDG-PET-CT findings in clinical stage III and IIB breast cancer. J Natl Cancer Inst. 2012;104:1879–87.PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Cochet A, Dygai-Cochet I, Riedinger J-M, Humbert O, Berriolo-Riedinger A, Toubeau M, et al. 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;41:428–37.CrossRefPubMedGoogle Scholar
  3. 3.
    NCCN Clinical Practice Guidelines in Oncology. Breast Cancer. Version 3. 2014. Available at:
  4. 4.
    Groheux D, Giacchetti S, Espie M, Rubello D, Moretti JL, Hindie E. Early monitoring of response to neoadjuvant chemotherapy in breast cancer with 18F-FDG PET/CT: defining a clinical aim. Eur J Nucl Med Mol Imaging. 2011;38:419–25.CrossRefPubMedGoogle Scholar
  5. 5.
    De Azambuja E, Holmes AP, Piccart-Gebhart M, Holmes E, Di Cosimo S, Swaby RF, et al. Lapatinib with trastuzumab for HER2-positive early breast cancer (NeoALTTO): survival outcomes of a randomised, open-label, multicentre, phase 3 trial and their association with pathological complete response. Lancet Oncol. 2014;15:1137–46.CrossRefPubMedGoogle Scholar
  6. 6.
    Groheux D, Giacchetti S, Moretti JL, Porcher R, Espie M, Lehmann-Che J, 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
  7. 7.
    Koolen BB, Vrancken Peeters MJTFD, Wesseling J, Lips EH, Vogel WV, Aukema TS, et al. Association of primary tumour FDG uptake with clinical, histopathological and molecular characteristics in breast cancer patients scheduled for neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2012;39:1830–8.CrossRefPubMedGoogle Scholar
  8. 8.
    Oshida M, Uno K, Suzuki M, Nagashima T, Hashimoto H, Yagata H, et al. Predicting the prognoses of breast carcinoma patients with positron emission tomography using 2-deoxy-2-fluoro[18F]-D-glucose. Cancer. 1998;82:2227–34.CrossRefPubMedGoogle Scholar
  9. 9.
    Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Eder V, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One. 2014;9:e94017.PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Son SH, Kim D-H, Hong CM, Kim C-Y, Jeong SY, Lee S-W, et al. Prognostic implication of intratumoral metabolic heterogeneity in invasive ductal carcinoma of the breast. BMC Cancer. 2014;14:585.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Wolff AC, Hammond ME, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Arch Pathol Lab Med. 2007;131:18–43.PubMedGoogle Scholar
  12. 12.
    Hatt M, Cheze le Rest C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77:301–8.CrossRefPubMedGoogle Scholar
  13. 13.
    Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28:881–93.PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Hatt M, Groheux D, Martineau A, Espié M, Hindié E, Giacchetti S, et al. Comparison between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer. J Nucl Med. 2013;54:341–9.CrossRefPubMedGoogle Scholar
  15. 15.
    Van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, 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.PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, 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.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700.PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. 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
  19. 19.
    Hatt M, Majdoub M, Vallières M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med. 2015;56:38–44.CrossRefPubMedGoogle Scholar
  20. 20.
    Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 2010;49:1012–6.PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.
    Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014;55:37–42.PubMedCentralCrossRefPubMedGoogle Scholar
  22. 22.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.Google Scholar
  23. 23.
    El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162–71.PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.PubMedCentralPubMedGoogle Scholar
  25. 25.
    Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.PubMedCentralCrossRefPubMedGoogle Scholar
  26. 26.
    Apostolova I, Steffen IG, Wedel F, Lougovski A, Marnitz S, Derlin T, et al. Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer. Eur Radiol. 2014;24:2077–87.CrossRefPubMedGoogle Scholar
  27. 27.
    Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S, et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med. 2014;55:1235–41.CrossRefPubMedGoogle Scholar
  28. 28.
    Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26.CrossRefPubMedGoogle Scholar
  29. 29.
    Groheux D, Giacchetti S, Delord M, de Roquancourt A, Merlet P, Hamy AS, et al. Prognostic impact of 18F-FDG PET/CT staging and of pathological response to neoadjuvant chemotherapy in triple-negative breast cancer. Eur J Nucl Med Mol Imaging. 2015;42:377–85.CrossRefPubMedGoogle Scholar
  30. 30.
    Tixier F, Groves AM, Goh V, Hatt M, Ingrand P, Le Rest CC, et al. Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One. 2014;9:e99567.PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Basu S, Kwee TC, Gatenby R, Saboury B, Torigian DA, Alavi A. Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging. 2011;38:987–91.CrossRefPubMedGoogle Scholar
  32. 32.
    Rajendran JG, Schwartz DL, O’Sullivan J, Peterson LM, Ng P, Scharnhorst J, et al. Tumor hypoxia imaging with [F-18] fluoromisonidazole positron emission tomography in head and neck cancer. Clin Cancer Res. 2006;12:5435–41.CrossRefPubMedGoogle Scholar
  33. 33.
    Kunkel M, Reichert TE, Benz P, Lehr H-A, Jeong J-H, Wieand S, et al. Overexpression of Glut-1 and increased glucose metabolism in tumors are associated with a poor prognosis in patients with oral squamous cell carcinoma. Cancer. 2003;97:1015–24.CrossRefPubMedGoogle Scholar
  34. 34.
    Alvarez JV, Belka GK, Pan T-C, Chen C-C, Blankemeyer E, Alavi A, et al. Oncogene pathway activation in mammary tumors dictates FDG-PET uptake. Cancer Res. 2014;74:7583–98.CrossRefPubMedGoogle Scholar
  35. 35.
    Straver ME, Rutgers EJ, Rodenhuis S, Linn SC, Loo CE, Wesseling J, et al. The relevance of breast cancer subtypes in the outcome of neoadjuvant chemotherapy. Ann Surg Oncol. 2010;17:2411–8.PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Esserman LJ, Berry DA, DeMichele A, Carey L, Davis SE, Buxton M, et al. Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL–CALGB 150007/150012, ACRIN 6657. J Clin Oncol. 2012;30:3242–9.PubMedCentralCrossRefPubMedGoogle Scholar
  37. 37.
    Yip S, McCall K, Aristophanous M, Chen AB, Aerts HJWL, Berbeco R. Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. 2014;9:e115510.PubMedCentralCrossRefPubMedGoogle Scholar
  38. 38.
    Koolen BB, Vidal-Sicart S, Benlloch Baviera JM, Valdés Olmos RA. Evaluating heterogeneity of primary tumor (18)F-FDG uptake in breast cancer with a dedicated breast PET (MAMMI): a feasibility study based on correlation with PET/CT. Nucl Med Commun. 2014;35:446–52.CrossRefPubMedGoogle Scholar
  39. 39.
    Kalinyak JE, Berg WA, Schilling K, Madsen KS, Narayanan D, Tartar M. Breast cancer detection using high-resolution breast PET compared to whole-body PET or PET/CT. Eur J Nucl Med Mol Imaging. 2014;41:260–75.CrossRefPubMedGoogle Scholar
  40. 40.
    Humbert O, Berriolo-Riedinger A, Cochet A, Gauthier M, Charon-Barra C, Guiu S, et al. Prognostic relevance at 5 years of the early monitoring of neoadjuvant chemotherapy using (18)F-FDG PET in luminal HER2-negative breast cancer. Eur J Nucl Med Mol Imaging. 2014;41:416–27.CrossRefPubMedGoogle Scholar
  41. 41.
    Linden HM, Dehdashti F. Novel methods and tracers for breast cancer imaging. Semin Nucl Med. 2013;43:324–9.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • David Groheux
    • 1
  • Mohamed Majdoub
    • 2
  • Florent Tixier
    • 3
  • Catherine Cheze Le Rest
    • 3
  • Antoine Martineau
    • 1
  • Pascal Merlet
    • 1
  • Marc Espié
    • 4
  • Anne de Roquancourt
    • 5
  • Elif Hindié
    • 6
  • Mathieu Hatt
    • 2
    Email author
  • Dimitris Visvikis
    • 2
  1. 1.Department of Nuclear MedicineSaint-Louis HospitalParisFrance
  2. 2.INSERM, UMR 1101 LaTIMBrestFrance
  3. 3.DACTIM, Department of Nuclear MedicineMilétrie HospitalPoitiersFrance
  4. 4.Breast Diseases Unit and Department of Medical OncologySaint-Louis HospitalParisFrance
  5. 5.Department of PathologySaint-Louis HospitalParisFrance
  6. 6.Department of Nuclear Medicine, CHU BordeauxUniversity of BordeauxBordeauxFrance

Personalised recommendations