Advertisement

Texture analysis of high-resolution dedicated breast 18 F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer

  • Alexis Moscoso
  • Álvaro Ruibal
  • Inés Domínguez-Prado
  • Anxo Fernández-Ferreiro
  • Míchel Herranz
  • Luis Albaina
  • Sonia Argibay
  • Jesús Silva-Rodríguez
  • Juan Pardo-Montero
  • Pablo Aguiar
Original Article
  • 595 Downloads

Abstract

Purpose

This study aims to determine whether PET textural features measured with a new dedicated breast PET scanner reflect biological characteristics of breast tumors.

Methods

One hundred and thirty-nine breast tumors from 127 consecutive patients were included in this analysis. All of them underwent a 18F-FDG PET scan before treatment. Well-known PET quantitative parameters such as SUV m a x , SUV m e a n , metabolically active tumor volume (MATV) and total lesion glycolysis (TLG) were extracted. Together with these parameters, local, regional, and global heterogeneity descriptors, which included five textural features (TF), were computed. Immunohistochemical classification of breast cancer considered five subtypes: luminal A like (LA), luminal B like/HER2 − (LB −), luminal B like/HER2+ (LB+), HER2-positive-non-luminal (HER2pnl), and triple negative (TN). Associations between PET features and tumor characteristics were assessed using non-parametric hypothesis tests.

Results

Along with well-established associations, new correlations were found. HER2-positive tumors had significantly higher uptake (p < 0.001, AUCs > 0.70) and presented different global and regional heterogeneity (p = 0.002, p = 0.016, respectively, AUCs < 0.70). Nine out of ten analyzed features were significantly associated with immunohistochemical subtype. Uptake was lower for LA tumors (p < 0.001) with AUCs ranging from 0.71 to 0.88 for each subgroup comparison. Heterogeneity metrics were significantly associated when comparing LA and LB − (p < 0.01), being regional heterogeneity metrics more discriminative than any other parameter (AUC = 0.80 compared to AUC = 0.71 for SUV). LB+ and HER2pnl tumors also showed more regional heterogeneity than LA tumors (AUCs = 0.79 and 0.84, respectively). After comparison with whole-body PET studies, we observed an overall improvement in the classification ability of both non-heterogeneity metrics and textural features.

Conclusions

PET parameters extracted from high-resolution dedicated breast PET images showed new and stronger correlations with immunohistochemical factors and immunohistochemical subtype of breast cancer compared to whole-body PET.

Keywords

18F-FDG Breast cancer PET Texture analysis Dedicated breast Heterogeneity 

Notes

Funding

This work was supported in part by the project PI14/02001 (Instituto de Salud Carlos III) cofunded by FEDER. JP-M is funded by Miguel Servet grant (CP12/03162), PA is funded by Ramón y Cajal grant (RYC-2015-17430) and AM is funded by IDIS predoctoral fellowship.

Compliance with Ethical Standards

Conflict of interest

None.

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.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

259_2017_3830_MOESM1_ESM.pdf (853 kb)
(PDF 852 KB)

References

  1. 1.
    Heppner GH. Tumor heterogeneity. Cancer Res 1984;44:2259–2265.PubMedGoogle Scholar
  2. 2.
    Nowell PC. The clonal evolution of tumor cell populations. Science 1976;194:23–28.CrossRefPubMedGoogle Scholar
  3. 3.
    Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thrlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013;24(9):2206–23.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;SMC-3:610–21.CrossRefGoogle Scholar
  5. 5.
    Amadasun M, King R. Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 1989;19:1264–74.CrossRefGoogle Scholar
  6. 6.
    Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS ONE 2014;9:1–1.CrossRefGoogle Scholar
  7. 7.
    Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 2013;40:133–40.CrossRefPubMedGoogle Scholar
  8. 8.
    Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, 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;52:369378.CrossRefGoogle Scholar
  9. 9.
    Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 2016;43:1453–60.CrossRefPubMedGoogle Scholar
  10. 10.
    Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, 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;54:1926.Google Scholar
  11. 11.
    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. Engl J Med 2012;366:883892.CrossRefGoogle Scholar
  12. 12.
    Zardavas D, Irrthum A, Swanton C, Piccart M. Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol. 2015;12(7):381–94.CrossRefPubMedGoogle Scholar
  13. 13.
    Bastien RR, Rodrguez-Lescure Á, Ebbert MT, Prat A, Munárriz B, Rowe L, et al. PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers. BMC Med Genomics. 2012;5:44.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Soussan M, Orlhac F, Boubaya M, Zelek L, Ziol M, Vronique E, 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
  16. 16.
    Groheux D, Majdoub M, Tixier F, Le Rest CC, Martineau A, Merlet P, et al. Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer? Eur J Nucl Med Mol Imaging 2015;42 (11):1682–91.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Lemarignier C, Martineau A, Teixeira L, Vercellino L, Espi M, Merlet P, et al. Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients, Eur J Nucl Med Mol Imaging. 2017.  http://dx.doi.org/https://doi.org/10.1007/s00259-017-3641-4.
  18. 18.
    Moliner L, González AJ, Soriano A, Sánchez F, Correcher C, Orero A, et al. Design and evaluation of the MAMMI dedicated breast PET. Med Phys 2012;39:5393–5404.CrossRefPubMedGoogle Scholar
  19. 19.
    García Hernández T, Vicedo González A, Ferrer Rebolleda J, Sánchez Jurado R, Roselló Ferrando J, Brualla González L, et al. Performance evaluation of a high-resolution dedicated breast PET scanner. Med Phys 2016;43:2261–72.CrossRefPubMedGoogle Scholar
  20. 20.
    Koolen BB, Vidal-Sicart S, Benlloch Baviera JM, Valdés Olmos R A. 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(5):446–52.CrossRefPubMedGoogle Scholar
  21. 21.
    Thibault G, Fertil B, Navarro C, Pereira S, Cau P, Levy N, et al. Texture indexes and gray level size zone matrix: application to cell nuclei classification. In: 10th international conference on pattern recognition and information processing, PRIP. Minsk, Belarus; 2009. p. 140–145.Google Scholar
  22. 22.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Tixier F, Hatt M, Cheze Le Rest C, 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.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    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
  25. 25.
    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 multicancer site patient cohort. J Nucl Med 2015;56:38–44.CrossRefPubMedGoogle Scholar
  26. 26.
    Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.Google Scholar
  27. 27.
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B Methodol 1995;57(1):289–300.Google Scholar
  28. 28.
    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.PubMedPubMedCentralGoogle Scholar
  29. 29.
    Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med 2014 Jan;55(1):37–42.CrossRefPubMedGoogle Scholar
  30. 30.
    Jadvar H, Alavi A. Gambhir SS, 18F-FDG uptake in lung, breast, and colon cancers: molecular biology correlates and disease characterization. J Nucl Med 2009;50(11):1820–7.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Osborne JR, Port E, Gonen M, Doane A, Yeung H. Gerald W, others. 18F-FDG PET of locally invasive breast cancer and association of estrogen receptor status with standardized uptake value: microarray and immunohistochemical analysis. J Nucl Med 2010;51(4):543–50.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Koolen BB, Vrancken Peeters MJ, Wesseling J, Lips EH, Vogel WV, Aukema TS. 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(12):1830–8.CrossRefPubMedGoogle Scholar
  33. 33.
    Lee SS, Bae SK, Park YS, Park JS, Kim TH, Yoon HK. 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(1):79–85.CrossRefPubMedGoogle Scholar
  34. 34.
    Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 2009;101(10):736– 50.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst 2011;103(22):1656–64.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Nuclear Medicine Department and Molecular Imaging GroupComplexo Hospitalario Universitario de Santiago de Compostela CHUS-IDISTravesía da Choupana s/nSpain
  2. 2.Molecular Imaging Group, Department of Radiology, Faculty of MedicineUniversity of Santiago de Compostela (USC)Campus VidaSpain
  3. 3.Fundación TejerinaMadridSpain
  4. 4.Pharmacy Department and Pharmacology groupComplexo Hospitalario Universitario de Santiago de Compostela CHUS-IDISTravesía da Choupana s/nSpain
  5. 5.Department of General SurgeryUniversity Hospital A Coruña (SERGAS)A CoruñaSpain
  6. 6.Medical Physics DepartmentComplexo Hospitalario Universitario de Santiago de Compostela (CHUS)Travesía Choupana s/nSpain

Personalised recommendations