Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning.

ABSTRACT:

Purpose

We aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-β1 pathway with RP.

Methods

We prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFβ pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapp

SUBMITTER: Choi SH 

PROVIDER: S-EPMC11332843 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature