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Merzhevich, T.* ; Tanzanakis, A.* ; Salin, E.* ; Quiering, C.* ; Kurz, C.* ; Gmeiner, B.* ; Eskofier, B.M.

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

In: (Artificial Intelligence in Medicine). Berlin [u.a.]: Springer, 2025. 267-271 (Lect. Notes Comput. Sc. ; 15735 LNAI)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Advanced Breast Cancer ; Machine Learning ; Overall Survival ; Progression-free Survival ; Survival Analysis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Artificial Intelligence in Medicine
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15735 LNAI, Heft: , Seiten: 267-271 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)