A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy
| Autor: | Brittany Z. Dashevsky, Lior Z. Braunstein, Joseph O. Deasy, Meredith Sadinski, Elizabeth A. Morris, Duc Fehr, Elizabeth J. Sutton, Natsuko Onishi, Harini Veeraraghavan, Mahmoud El-Tamer, Katja Pinker, Virgilio Sacchini, Danny F. Martinez, Pedram Razavi, Edi Brogi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_treatment
Breast Neoplasms Machine learning computer.software_genre Neoadjuvant chemotherapy lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Breast cancer Radiomics Surgical oncology Antineoplastic Combined Chemotherapy Protocols Medicine Breast MRI Humans Complete response Retrospective Studies Chemotherapy Training set medicine.diagnostic_test business.industry Carcinoma Ductal Breast Retrospective cohort study Middle Aged medicine.disease Prognosis lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Magnetic Resonance Imaging Neoadjuvant Therapy Carcinoma Lobular ROC Curve 030220 oncology & carcinogenesis Female Artificial intelligence business computer Research Article Follow-Up Studies MRI |
| Zdroj: | Breast Cancer Research, Vol 22, Iss 1, Pp 1-11 (2020) Breast Cancer Research : BCR |
| DOI: | 10.1186/s13058-020-01291-w |
| Popis: | Background For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|