Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.

ABSTRACT: For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics

SUBMITTER: Fanizzi A 

PROVIDER: S-EPMC10220052 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature