{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:51:11Z","timestamp":1792291871022,"version":"4.4.0"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is important because it could help to minimize unnecessary toxic NAC and to modify regimens mid-treatment to achieve better efficacy. Machine learning (ML) is increasingly being used in radiology and medicine because it can identify relationships amongst complex data elements to inform outcomes without the need to specify such relationships a priori. One of the most popular deep learning methods that applies to medical images is the Convolutional Neural Networks (CNN). In contrast to supervised ML, deep learning CNN can operate on the whole images without requiring radiologists to manually contour the tumor on images. Although there have been many review papers on supervised ML prediction of pCR, review papers on deep learning prediction of pCR are sparse. Deep learning CNN could also incorporate multiple image types, clinical data such as demographics and molecular subtypes, as well as data from multiple treatment time points to predict pCR. The goal of this study is to perform a systematic review of deep learning methods that use whole-breast MRI images without annotation or tumor segmentation to predict pCR in breast cancer.<\/jats:p>","DOI":"10.3390\/tomography8060232","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T05:18:57Z","timestamp":1669094337000},"page":"2784-2795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review"],"prefix":"10.3390","volume":"8","author":[{"given":"Nabeeha","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Adam","sequence":"additional","affiliation":[{"name":"Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pauline","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takouhie","family":"Maldjian","sequence":"additional","affiliation":[{"name":"Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim Q.","family":"Duong","sequence":"additional","affiliation":[{"name":"Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, NY 10461, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1158\/1078-0432.CCR-19-3492","article-title":"Pathologic Complete Response after Neoadjuvant Chemotherapy and Impact on Breast Cancer Recurrence and Survival: A Comprehensive Meta-analysis","volume":"26","author":"Spring","year":"2020","journal-title":"Clin. Cancer Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110561","DOI":"10.1016\/j.ejrad.2022.110561","article-title":"The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: A systematic review and network meta-analysis","volume":"157","author":"Gasior","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9490","DOI":"10.1038\/s41598-018-27764-9","article-title":"Pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: Perfusion metrics of dynamic contrast enhanced MRI","volume":"8","author":"Lee","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1002\/cncr.27995","article-title":"Magnetic resonance imaging as a predictor of pathologic response in patients treated with neoadjuvant systemic treatment for operable breast cancer. Translational Breast Cancer Research Consortium trial 017","volume":"119","author":"Cantor","year":"2013","journal-title":"Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1590\/0100-3984.2019.0049","article-title":"Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: Advances in imaging towards to precision medicine","volume":"52","author":"Santos","year":"2019","journal-title":"Radiol. Bras."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","article-title":"Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: An open, web-based, international, diagnostic study","volume":"20","author":"Tschandl","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s41571-020-0329-7","article-title":"AI outperforms radiologists in mammographic screening","volume":"17","author":"Killock","year":"2020","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"203","DOI":"10.4048\/jbc.2012.15.2.203","article-title":"Impact of immunohistochemistry-based molecular subtype on chemosensitivity and survival in patients with breast cancer following neoadjuvant chemotherapy","volume":"15","author":"Yoo","year":"2012","journal-title":"J. Breast Cancer"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.breast.2019.11.009","article-title":"Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy","volume":"49","author":"Morris","year":"2020","journal-title":"Breast"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12530-019-09297-2","article-title":"Survey of deep learning in breast cancer image analysis","volume":"11","author":"Debelee","year":"2020","journal-title":"Evol. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110247","DOI":"10.1016\/j.ejrad.2022.110247","article-title":"Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis","volume":"150","author":"Liang","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_16","unstructured":"Braman, N., El Adoui, M., Vulchi, M., Turk, P., Etesami, M., Fu, P., Bera, K., Drisis, S., Varadan, V., and Plecha, D. (2020). Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"14123","DOI":"10.1038\/s41598-021-93592-z","article-title":"Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs","volume":"11","author":"Comes","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Duanmu, H., Huang, P.B., Brahmavar, S., Lin, L., Ren, T., Kong, J., Wang, F., and Duong, T.Q. (2020, January 4\u20138). Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Deep Learning with Integrative Imaging, Molecular and Demographic Data. Proceedings of the 3rd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lima, Peru.","DOI":"10.1007\/978-3-030-59713-9_24"},{"key":"ref_19","unstructured":"Duanmu, H., Ren, T., and Duong, T.Q. (2022). Deep learning prediction of pathological complete response, residual cancer burden, and progression-free survival in breast cancer patients. PLoS ONE, in press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1007\/s11548-020-02209-9","article-title":"Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images","volume":"15","author":"Drisis","year":"2020","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s10278-018-0144-1","article-title":"Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset","volume":"32","author":"Ha","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_22","first-page":"101340U","article-title":"Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning","volume":"10134","author":"Huynh","year":"2017","journal-title":"Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"18800","DOI":"10.1038\/s41598-021-98408-8","article-title":"Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer","volume":"11","author":"Joo","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.mri.2020.08.021","article-title":"A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database","volume":"73","author":"Liu","year":"2020","journal-title":"Magn. Reson. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Massafra, R., Comes, M.C., Bove, S., Didonna, V., Gatta, G., Giotta, F., Fanizzi, A., La Forgia, D., Latorre, A., and Pastena, M.I. (2022). Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J. Pers. Med., 12.","DOI":"10.3390\/jpm12060953"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"846775","DOI":"10.3389\/fonc.2022.846775","article-title":"Pretreatment DCE-MRI-Based Deep Learning Outperforms Radiomics Analysis in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer","volume":"12","author":"Peng","year":"2022","journal-title":"Front. Oncol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/1759-7714.13309","article-title":"Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method","volume":"11","author":"Qu","year":"2020","journal-title":"Thorac. Cancer"},{"key":"ref_28","first-page":"105750C-1","article-title":"A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI","volume":"10575","author":"Ravichandran","year":"2018","journal-title":"SPIE Int. Soc. Opt. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1002\/jmri.26762","article-title":"Background parenchymal enhancement on breast MRI: A comprehensive review","volume":"51","author":"Liao","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1245\/s10434-018-6613-4","article-title":"Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm","volume":"25","author":"Ha","year":"2018","journal-title":"Ann. Surg. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e301-e8","DOI":"10.1016\/j.clbc.2019.11.009","article-title":"Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI","volume":"20","author":"Ren","year":"2020","journal-title":"Clin. Breast Cancer"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.clbc.2019.06.006","article-title":"MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer","volume":"20","author":"Cattell","year":"2020","journal-title":"Clin. Breast Cancer"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.clbc.2021.07.002","article-title":"Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy","volume":"22","author":"Ren","year":"2022","journal-title":"Clin. Breast Cancer"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10278-018-0086-7","article-title":"Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset","volume":"31","author":"Ha","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"04TR1","DOI":"10.1088\/1361-6560\/abcd17","article-title":"Interpretation and visualization techniques for deep learning models in medical imaging","volume":"66","author":"Huff","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12938-021-00899-z","article-title":"Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response","volume":"20","author":"Hussain","year":"2021","journal-title":"Biomed. Eng. Online"}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/8\/6\/232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:51Z","timestamp":1760145771000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/8\/6\/232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,21]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["tomography8060232"],"URL":"https:\/\/doi.org\/10.3390\/tomography8060232","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,21]]}}}