Machine learning models for predicting survival in patients of hepatocellular carcinoma with second primary malignancy.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality, and an increasing number of long-term survivors develop second primary malignancies (SPMs). Reliable risk prediction in this heterogeneous population remains challenging, and it is unclear whether modern machine learning methods can offer superior prognostic accuracy. This study aimed to develop and validate machine learning models to improve prognostic prediction for HCC survivors.

Methods

A total of 1,580 HCC patients with second primary cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training group and a test group at a ratio of 7:3. Prognostic prediction models were developed using random survival forest (RSF), DeepSurv, a

SUBMITTER: Nie Y 

PROVIDER: S-EPMC12605104 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature