Machine Learning Predictions of Overall and Progression-Free Survival in Advanced Breast Cancer

Merzhevich T, Tanzanakis A, Salin E, Quiering C, Kurz C, Gmeiner B, Eskofier B (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer

Series: Lecture Notes in Computer Science

City/Town: Cham

Book Volume: 15735

Pages Range: 267-271

Conference Proceedings Title: Artificial Intelligence in Medicine. 23rd International Conference, AIME 2025, Pavia, Italy, June 23–26, 2025, Proceedings, Part II

Event location: Pavia IT

ISBN: 9783031958403

DOI: 10.1007/978-3-031-95841-0_50

Abstract

Breast cancer remains one of the leading causes of cancer deaths, requiring advanced methods to assess treatment efficacy and improve survival predictions. This study aims to predict overall survival (OS) and progression-free survival (PFS) between 6 and 36 months in patients with advanced breast cancer receiving ribociclib therapy. Survival analysis was performed using two datasets, RIBECCA and RIBANNA, which were derived from German Phase III clinical and non-interventional studies, to assess the survival outcomes in patients with advanced breast cancer under ribociclib therapy. The best OS results were obtained at month 12 with the Cox Proportional Hazards model, achieving a concordance index (C-index) of 0.720. The best PFS predictions were achieved at month 6 with the GBSA model, with a C-index of 0.728. In addition, Shapley Additive Explanation (SHAP) values were used to identify the most influential features and explain model predictions. Key predictors included liver metastases, treatment regimen, prior treatment, and quality of life scores. This study highlights the potential of survival machine learning models, offering valuable insights towards data-driven improvements in clinical decision-making.

Authors with CRIS profile

Tatiana Merzhevich Machine Learning and Data Analytics Lab Alexandros Tanzanakis Machine Learning and Data Analytics Lab Emmanuelle Salin Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

Novartis AG CH Switzerland (CH)

How to cite

APA:

Merzhevich, T., Tanzanakis, A., Salin, E., Quiering, C., Kurz, C., Gmeiner, B., & Eskofier, B. (2025). Machine Learning Predictions of Overall and Progression-Free Survival in Advanced Breast Cancer. In Riccardo Bellazzi, José Manuel Juarez Herrero, Lucia Sacchi, Blaž Zupan (Eds.), Artificial Intelligence in Medicine. 23rd International Conference, AIME 2025, Pavia, Italy, June 23–26, 2025, Proceedings, Part II (pp. 267-271). Pavia, IT: Cham: Springer.

MLA:

Merzhevich, Tatiana, et al. "Machine Learning Predictions of Overall and Progression-Free Survival in Advanced Breast Cancer." Proceedings of the 23rd International Conference on Artificial Intelligence in Medicine, AIME 2025, Pavia Ed. Riccardo Bellazzi, José Manuel Juarez Herrero, Lucia Sacchi, Blaž Zupan, Cham: Springer, 2025. 267-271.

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