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

Clinical Information and Decision Making (2001) Machine Learning (2166) Cancer Prognosis Models and Machine Learning (72) Breast Cancer (302) New Methods (1152)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69864, first published .
Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study

Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study

Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study

Authors of this article:

Yudi Jin1 Author Orcid Image ;   Min Zhao1 Author Orcid Image ;   Tong Su1 Author Orcid Image ;   Yanjia Fan2 Author Orcid Image ;   Zubin Ouyang1 Author Orcid Image ;   Fajin Lv1 Author Orcid Image
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Journals

  1. Yuan Q, Hong Z, Ye R, Yang P, Lin J, Jiang X, Qiu H, Liu A, Yu H, Gao F, He P, Chen K, Cai J, Xie X, You W, Yuan H, Zhang K, Yang S, Yu B, Huang X, Chen D, Niu M. Integrating Deep Feature Extraction and MRI Radiomics for Survival Prediction in Breast Cancer After Neoadjuvant Chemotherapy. Academic Radiology 2025 View
  2. Nag S, P M, Priya M S S, C P. A hierarchical ensemble machine learning framework for precision survival prediction in breast cancer using SEER data. Results in Engineering 2026;29:108837 View
  3. Takefuji Y. Assumption-light feature discovery outperforms Cox-based selection for PM2.5 constituent analysis in an open benchmark. Environmental Pollution 2026;394:127738 View