Enhanced prognostic prediction of cancer-specific mortality in elderly bladder cancer patients post-radical cystectomy: an XGBoost model study.

ABSTRACT:

Background

Tumor stage, surgery and age are positively correlated with cancer-specific mortality (CSM) in patients diagnosed with bladder cancer (BCa). In light of the successful application of machine learning to process big data in many fields outside of medicine, we aimed to establish and validate whether machine learning models could improve our ability to predict the development of CSM in elderly BCa patients after radical cystectomy (RC).

Methods

Data on eligible patients diagnosed with BCa were obtained from the Surveillance, Epidemiology, and End Results database (2000-2021) and divided into training and validation cohorts in a ratio of 7:3. First, risk factors for the development of CSM in patients were identified by Cox regression analysis. Then, iterative testing

SUBMITTER: Li G 

PROVIDER: S-EPMC11985172 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature