Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer

[2509.23268] Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2509.23268 (cs) [Submitted on 27 Sep 2025] Title: Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer Authors: Lisa Pilgram , Kai Yang , Ana-Alicia Beltran-Bless , Gregory R. Pond , Lisa Vandermeer , John Hilton , Marie-France Savard , Andréanne Leblanc , Lois Sheperd , Bingshu E. Chen , John M.S. Bartlett , Karen J. Taylor , Jane Bayani , Sarah L. Barker , Melanie Spears , Cornelis J. H. van der Velde , Elma Meershoek-Klein Kranenbarg , Luc Dirix , Elizabeth Mallon , Annette Hasenburg , Christos Markopoulos , Lamin Juwara , Fida K. Dankar , Mark Clemons , Khaled El Emam View a PDF of the paper titled Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer, by Lisa Pilgram and 24 other authors View PDF Abstract: Prognostic information is essential for decision-making in breast cancer management. Recently trials have predominantly focused on genomic prognostication tools, even though clinicopathological prognostication is less costly and more widely accessible. Machine learning (ML), transfer learning and ensemble integration offer opportunities to build robust prognostication frameworks. We evaluate this potential to improve survival prognostication in breast cancer by comparing de-novo ML, transfer learning from a pre-trained prognostic tool and ensemble integration. Data from the MA.27 trial was used for model training, with external validation on the TEAM trial and a SEER cohort. Transfer learning was applied by fine-tuning the pre-trained prognostic tool PREDICT v3, de-novo ML included Random Survival Forests and Extreme Gradient Boosting, and ensemble integration was realized through a weighted sum of model predictions. Transfer learning, de-novo RSF, and ensemble integration improved calibration in MA.27 over the pre-trained model (ICI reduced from 0.042 in PREDICT v3 to <=0.007) while discrimination remained comparable (AUC increased from 0.738 in PREDICT v3 to 0.744-0.799). Invalid PREDICT v3 predictions were observed in 23.8-25.8% of MA.27 individuals due to missing information. In contrast, ML models and ensemble integration could predict survival regardless of missing information. Across all models, patient age, nodal status, pathological grading and tumor size had the highest SHAP values, indicating their importance for survival prognostication. External validation in SEER, but not in TEAM, confirmed the benefits of transfer learning, RSF and ensemble integration. This study demonstrates that transfer learning, de-novo RSF, and ensemble integration can improve prognostication in situations where relevant information for PREDICT v3 is lacking or where a dataset shift is likely. Subjects: Machine Learning (cs.LG) ; Computers and Society (cs.CY) Cite as: arXiv:2509.23268 [cs.LG] (or arXiv:2509.23268v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.23268 Focus to learn more arXiv-issued DOI via DataCite Journal reference: J Med Internet Res 2026;28:e88665 Related DOI : https://doi.org/10.2196/88665 Focus to learn more DOI(s) linking to related resources Submission history From: Khaled El Emam [ view email ] [v1] Sat, 27 Sep 2025 11:49:55 UTC (2,081 KB) Full-text links: Access Paper: View a PDF of the paper titled Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer, by Lisa Pilgram and 24 other authors View PDF view license Current browse context: cs.LG < prev | next > new | recent | 2025-09 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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