PuSH - Publication Server of Helmholtz Zentrum München: Machine Learning Predictions of Overall and Progression-Free Survival in Advanced Breast Cancer.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

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

Open Access Green as soon as Postprint is submitted to ZB.

Abstract
Metrics
Extra information

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.

Altmetric