This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (72) Clinical Information and Decision Making (2007) Innovations and Technology in Cancer Care (557) Machine Learning (2173) Imaging Informatics (318) Breast Cancer (303) Cancer Survivorship (353) Innovations in Cancer Diagnostic and Decision Support (117)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64685, first published .
Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective Study

Authors of this article:

Chun-Chi Lai1, 2 Author Orcid Image ;   Cheng-Yu Chen3, 4, 5, 6, 7 Author Orcid Image ;   Tzu-Hao Chang1, 8 Author Orcid Image
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Journals

  1. Comes M, Lupo A, Bozzi A, Fanizzi A, Cirillo A, Nunzio G, Pastena M, Rizzo A, Guven D, Vitale E, Zito F, Bove S, Massafra R. Enhancing early prediction of pathological complete response in breast cancer using attention-based convolutional neural networks in digital pathology. DIGITAL HEALTH 2026;12 View