Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

ABSTRACT:

Background

Prognostication is essential to determine the risk profile of patients with urologic cancers.

Methods

We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the te

SUBMITTER: Eminaga O 

PROVIDER: S-EPMC9264864 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature