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

Cancer Prognosis Models and Machine Learning (88) Clinical Information and Decision Making (2134) Innovations and Technology in Cancer Care (585) Cancer Survivorship (376) Imaging Informatics (348) Machine Learning (2294) Breast Cancer (331) Innovations in Cancer Diagnostic and Decision Support (122)

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
  2. Hajjo R, Sabbah D, Bardaweel S. ChatGPT Versus DeepSeek for Breast Cancer Information Retrieval: Quantitative Comparative Study. JMIR Cancer 2026;12:e72839 View
  3. Kaplan E, Alakus H, Kaplan S. Pre-Treatment Breast MRI Features and ADC Values as Predictors of Pathologic Complete Response in Breast Cancer: A Molecular Subtype-Based Analysis. Diagnostics 2026;16(6):938 View
  4. Fang J, Zhu J, Huang R, Dai X, Gao F, Li H, Xue J, Liu C, Li Z, Zheng J, Xie K. Explainable machine learning integrating metabolic and inflammatory signatures for personalized prognosis in resected intrahepatic cholangiocarcinoma. European Journal of Surgical Oncology 2026;52(6):111792 View
  5. Li X, Zhou Q, Duan S, Zhang L, Li Z, Chen S, Yuan Y, Liu Y, Li Y, Cai X, Wang Y, Zhang L. An Active-Targeted ZIF8-Based Nanotheranostic Platform for Ultrasound Imaging-Guided Synergistic Therapy of Triple-Negative Breast Cancer. International Journal of Nanomedicine 2026;Volume 21:1 View