Advertisement

PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy

  • Lidija AntunovicEmail author
  • Rita De Sanctis
  • Luca Cozzi
  • Margarita Kirienko
  • Andrea Sagona
  • Rosalba Torrisi
  • Corrado Tinterri
  • Armando Santoro
  • Arturo Chiti
  • Renata Zelic
  • Martina Sollini
Original Article
  • 338 Downloads

Abstract

Purpose

To assess the role of radiomics parameters in predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer.

Methods

Seventy-nine patients who had undergone pretreatment staging 18F-FDG PET/CT and treatment with NAC between January 2010 and January 2018 were included in the study. Primary lesions on PET images were delineated, and extraction of first-, second-, and higher-order imaging features was performed using LIFEx software. The relationship between these parameters and pCR to NAC was analyzed by multiple logistic regression models.

Results

Nineteen patients (24%) had pCR to NAC. Different models were generated on complete information and imputed datasets, using univariable and multivariable logistic regression and least absolute shrinkage and selection operator (lasso) regression. All models could predict pCR to NAC, with area under the curve values ranging from 0.70 to 0.73. All models agreed that tumor molecular subtype is the primary predictor of the primary endpoint.

Conclusions

Our models predicted that patients with subtype 2 and subtype 3 (HER2+ and triple negative, respectively) are more likely to have a pCR to NAC than those with subtype 1 (luminal). The association between PET imaging features and pCR suggested that PET imaging features could be considered as potential predictors of pCR in locally advanced breast cancer patients.

Keywords

Breast cancer Neoadjuvant chemotherapy Radiomics Advanced features 18F-FDG PET/CT Treatment response prediction 

Notes

Acknowledgements

We would like to acknowledge Dr. Katia Marzo and Mrs. Elena Bissolotti for database preparation. The contribution of Drs. Elisa Agostinetto, Jelena Jandric, and Giulia Vatteroni to data collection is greatly acknowledged. We thank also Dr. Bethania Fernandes for histological analyses. We also acknowledge Drs. Alberto Testori and Valentina Errico for patients enrollment. We thank Mr. Lorenzo Leonardi for technical support and image acquisition.

Authors’ contributions

The manuscript has been seen and approved by all authors, whose individual contributions were as follows:

Conception and design: LA, RDS, MS

Patient management and referral: RDS, ASagona, CT, RT, ASantoro

Acquisition of data: LA, MK, RDS

Image analysis: LA, MS, MK, LC

Statistical analysis: LA, RZ, RDS

Interpretation of data: LA, RDS, ASagona, MK, RZ

Drafting the article: LA, RDS, MS

Final approval of the revised manuscript: LA, RDS, LC, ASagona, CT, RT, ASantoro, MK, AC, RZ, MS.

Raw data are available on specific request to the corresponding author.

Compliance with ethical standards

Conflict of interest

A. Chiti received speaker honoraria from General Electric, Blue Earth Diagnostics, and Sirtex Medical, acted as scientific advisor for Blue Earth Diagnostics and Advanced Accelerator Applications, and benefited from an unconditional grant from Sanofi to Humanitas University. A. Santoro received speaker honoraria from Mundipharma, Takeda, Glaxo, Celgene, Roche, Teva, Arqule, Amgen, Eisai, BMS, Bayer, MSD, Astra Zeneca, Gilead, Sandoz, Servier, and Novartis, acted as scientific advisor for Gilead, Pfizer, Eisai, Servier, MSD, Bayer, Eli Lilly, Amgen, Marck Serono, Takeda, Ariad, Italfarmaco, and Celgene. L. Cozzi acts as Scientific Advisor to Varian Medical Systems and is Clinical Research Scientist at Humanitas Cancer Center.

All honoraria and grants are outside the scope of the submitted work.

All other authors have no conflict of interest.

Ethics approval

The study was approved by the Local Ethics Committee (authorization number 1591). Specific informed consent was not required according to Local Ethics Committee rules for retrospective study design.

Supplementary material

259_2019_4313_MOESM1_ESM.doc (132 kb)
ESM 1 (DOC 131 kb)

References

  1. 1.
    Perou CM, Sørile T, Eisen MB, Van De Rijn M, Jeffrey SS, Ress CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–52.CrossRefGoogle Scholar
  2. 2.
    Tyagi NK, Dhesy-Thind S. Clinical practice guidelines in breast cancer. Curr Oncol. 2018;25:S151–60.CrossRefGoogle Scholar
  3. 3.
    Cardoso F, Senkus E, Costa A, Papadopoulos E, Aapro M, André F, et al. 4th ESO–ESMO international consensus guidelines for advanced breast cancer (ABC 4). Ann Oncol. 2018;29:1634–57.CrossRefGoogle Scholar
  4. 4.
    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.CrossRefGoogle Scholar
  5. 5.
    von Minckwitz G, Untch M, Blohmer J-U, Costa SD, Eidtmann H, Fasching PA, et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol. 2012;30:1796–804.CrossRefGoogle Scholar
  6. 6.
    Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384:164–72.CrossRefGoogle Scholar
  7. 7.
    Sella T, Gal Yam EN, Levanon K, Rotenberg TS, Gadot M, Kuchuk I, et al. Evaluation of tolerability and efficacy of incorporating carboplatin in neoadjuvant anthracycline and taxane based therapy in a BRCA1 enriched triple-negative breast cancer cohort. Breast. 2018;40:141–6.CrossRefGoogle Scholar
  8. 8.
    Hurtz H-J, Tesch H, Göhler T, Hutzschenreuter U, Harde J, Kruggel L, et al. Persistent impairments 3 years after (neo)adjuvant chemotherapy for breast cancer: results from the MaTox project. Breast Cancer Res Treat. 2017;165:721–31.CrossRefGoogle Scholar
  9. 9.
    Chakraborty D, Basu S, Ulaner GA, Alavi A, Kumar R. Diagnostic role of fluorodeoxyglucose PET in breast cancer: a history to current application. PET Clin. 2018;13:355–61.CrossRefGoogle Scholar
  10. 10.
    Kaida H, Toh U, Hayakawa M, Hattori S, Fujii T, Kurata S, et al. The relationship between 18F-FDG metabolic volumetric parameters and clinicopathological factors of breast cancer. Nucl Med Commun. 2013;34:562–70.CrossRefGoogle Scholar
  11. 11.
    García Vicente AM, Soriano Castrejón Á, León Martín A, Chacón López-Muñiz I, Muñoz Madero V, Muñoz Sánchez MDM, et al. Molecular subtypes of breast cancer: metabolic correlation with 18F-FDG PET/CT. Eur J Nucl Med Mol Imaging. 2013;40:1304–11.CrossRefGoogle Scholar
  12. 12.
    Koo HR, Park JS, Kang KW, Cho N, Chang JM, Bae MS, et al. 18F-FDG uptake in breast cancer correlates with immunohistochemically defined subtypes. Eur Radiol. 2014;24:610–8.CrossRefGoogle Scholar
  13. 13.
    Kajáry K, Tőkés T, Dank M, Kulka J, Szakáll S, Lengyel Z. Correlation of the value of 18F-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.CrossRefGoogle Scholar
  14. 14.
    Kitajima K, Fukushima K, Miyoshi Y, Nishimukai A, Hirota S, Igarashi Y, et al. Association between 18F-FDG uptake and molecular subtype of breast cancer. Eur J Nucl Med Mol Imaging. 2015;42:1371–7.CrossRefGoogle Scholar
  15. 15.
    Lee SS, Bae SK, Park YS, Park JS, Kim TH, Yoon HK, 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 (2010). 2017;51:79–85.CrossRefGoogle Scholar
  16. 16.
    Garcia Vicente AM, Soriano Castrejón A, Amo-Salas M, Lopez Fidalgo JF, Muñoz Sanchez MM, Alvarez Cabellos R, et al. Glycolytic activity in breast cancer using 18F-FDG PET/CT as prognostic predictor: a molecular phenotype approach. Rev Esp Med Nucl Imagen Mol. 2016;35:152–8.Google Scholar
  17. 17.
    Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast Cancer Res Treat. 2018;169:217–29.CrossRefGoogle Scholar
  18. 18.
    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.CrossRefGoogle Scholar
  19. 19.
    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;9:1–7.CrossRefGoogle Scholar
  20. 20.
    Yoon HJ, Kim Y, Kim BS. Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ. Eur Radiol. 2015;25:3648–58.CrossRefGoogle Scholar
  21. 21.
    Antunovic L, Gallivanone F, Sollini M, Sagona A, Invento A, Manfrinato G, et al. [18F]FDG PET/CT features for the molecular characterization of primary breast tumors. Eur J Nucl Med Mol Imaging. 2017;44:1945–54.CrossRefGoogle Scholar
  22. 22.
    Molina-García D, García-Vicente AM, Pérez-Beteta J, Amo-Salas M, Martínez-González A, Tello-Galán MJ, et al. Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate. Ann Nucl Med. 2018;32:379–88.CrossRefGoogle Scholar
  23. 23.
    Yoon H-J, Kim Y, Chung J, Kim BS. Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging. Breast J. 2018:1–8. [Epub ahead of print]Google Scholar
  24. 24.
    Azad GK, Cousin F, Siddique M, Taylor B, Goh V, Cook GJR. Does measurement of first-order and heterogeneity parameters improve response assessment of bone metastases in breast cancer compared to SUVmax in [18F]fluoride and [18F]FDG PET? Mol Imaging Biol. 2018. [Epub ahead of print].  https://doi.org/10.1007/s11307-018-1262-3.
  25. 25.
    Gong C, Ma G, Hu X, Zhang Y, Wang Z, Zhang J, et al. Pretreatment 18F-FDG uptake heterogeneity predicts treatment outcome of first-line chemotherapy in patients with metastatic triple-negative breast cancer. Oncologist. 2018;23(10):1144–52.  https://doi.org/10.1634/theoncologist.2018-0001.
  26. 26.
    Zwanenburg A, Leger S, Vallières M, Löck S, for the Image Biomarker Standardisation Initiative (IBSI). Image biomarker standardisation initiative — feature definitions 2016. https://arxiv.org/abs/1612.07003.
  27. 27.
    Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2014;42:328–54.CrossRefGoogle Scholar
  28. 28.
    Lemarignier C, Martineau A, Teixeira L, Vercellino L, Espié 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;44:1145–54.CrossRefGoogle Scholar
  29. 29.
    Garcia-Vicente AM, Pérez-Beteta J, Amo-Salas M, Molina D, Jimenez-Londoño GA, Soriano-Castrejón AM, et al. Predictive and prognostic potential of volume-based metabolic variables obtained by a baseline 18F-FDG PET/CT in breast cancer with neoadjuvant chemotherapy indication. Rev Esp Med Nucl Imagen Mol. 2018;37:73–9.Google Scholar
  30. 30.
    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.CrossRefGoogle Scholar
  31. 31.
    Arboretti R, Salmaso L. Model performance analysis and model validation in logistic regression. Statistica. 2003;63:375–96.Google Scholar
  32. 32.
    Donders ART, van der Heijden GJMG, Stijnen T, Moons KGM. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol. 2006;59:1087–91.CrossRefGoogle Scholar
  33. 33.
    Van Buuren S, Oudshoorn K. Flexible multivariate imputation by MICE TNO prevention and health. Leiden: TNO Prevention and Health; 1999.Google Scholar
  34. 34.
    Beukinga RJ, Hulshoff JB, Mul VEM, Noordzij W, Kats-Ugurlu G, Slart RHJA, et al. Prediction of response to neoadjuvant chemotherapy and radiation therapy with baseline and restaging 18 F-FDG PET imaging biomarkers in patients with esophageal cancer. Radiology. 2018;287:983–92.CrossRefGoogle Scholar
  35. 35.
    Groheux D, Biard L, Lehmann-Che J, Teixeira L, Bouhidel FA, Poirot B, et al. Tumor metabolism assessed by FDG-PET/CT and tumor proliferation assessed by genomic grade index to predict response to neoadjuvant chemotherapy in triple negative breast cancer. Eur J Nucl Med Mol Imaging. 2018;45:1279–88.CrossRefGoogle Scholar
  36. 36.
    Wood AM, White IR, Royston P. How should variable selection be performed with multiply imputed data? Stat Med. 2008;27:3227–46.CrossRefGoogle Scholar
  37. 37.
    Airola A, Pahikkala T. A comparison of AUC estimators in small-sample studies. JMLR - Work Mach Learn Syst Biol. 2009; 8:3–13.Google Scholar
  38. 38.
    Haque W, Verma V, Hatch S, Suzanne Klimberg V, Brian Butler E, Teh BS. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Res Treat. 2018;170:559–67.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lidija Antunovic
    • 1
    Email author
  • Rita De Sanctis
    • 2
  • Luca Cozzi
    • 3
  • Margarita Kirienko
    • 3
  • Andrea Sagona
    • 4
  • Rosalba Torrisi
    • 2
  • Corrado Tinterri
    • 4
  • Armando Santoro
    • 2
  • Arturo Chiti
    • 1
    • 3
  • Renata Zelic
    • 5
  • Martina Sollini
    • 3
  1. 1.Department of Nuclear MedicineHumanitas Clinical and Research Center- IRCCSRozzanoItaly
  2. 2.Department of Medical Oncology and HematologyHumanitas Clinical and Research Center- IRCCSRozzanoItaly
  3. 3.Department of Biomedical SciencesHumanitas UniversityPieve EmanueleItaly
  4. 4.Breast Surgery DepartmentHumanitas Clinical and Research Center- IRCCSRozzanoItaly
  5. 5.Clinical Epidemiology Unit, Department of Medicine SolnaKarolinska InstitutetStockholmSweden

Personalised recommendations