Predicting pathological staging of non-small cell lung cancer using a multi-task radiomics model integrating intratumoral and peritumoral features.

ABSTRACT: Pathological staging is essential for guiding treatment decisions and determining prognosis in patients with non-small cell lung cancer (NSCLC). The present study aimed to establish a model using intratumoral and peritumoral features from computed tomography radiomics data and a multi-task learning algorithm, and evaluate its predictive performance for the pathological stage of NSCLC. Data from 198 eligible patients with NSCLC from The Cancer Imaging Archive database were retrospectively analyzed, which was used to develop four radiomics models to classify the pathological stages of NSCLC. These models combined the traditional random forest and multi-task random forest algorithms with the volumes of interest for the intratumoral region alone and with the intratumoral and peritumoral region

SUBMITTER: Pan R 

PROVIDER: S-EPMC12273780 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature