Development and validation of a clinical risk-prediction model for immune checkpoint inhibitor-related pneumonitis in patients with gastrointestinal cancer based on four machine learning algorithms.

ABSTRACT:

Objective

Immune checkpoint inhibitor-related pneumonitis (ICIP) is a common and potentially life-threatening adverse event with non-specific symptoms. It is of significance to identify high-risk population of ICIP. However, existing prediction models for ICIP are often limited by their reliance on clinically inaccessible variables and homogeneous methodologies, hindering their clinical utility. This study aimed to develop a clinical risk-prediction model for ICIP in patients with gastrointestinal (GI) cancer based on four machine learning (ML) methods.

Methods

We conducted a retrospective analysis of data from GI cancer patients who received immune checkpoint inhibitors (ICIs) between 2018 and 2022 in Beijing Cancer Hospital. For each patient, 36 clinical indicators associa

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12780786 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature