Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.

ABSTRACT: The significant heterogeneity and complex morphology of invasive pulmonary adenocarcinoma (IPA) make grading challenging for pathologists. However, thorough investigations into radiopathomics features extracted from computed tomography (CT) and whole slide images (WSIs) for IPA grading and their biological significance remain limited. We aim to integrate multi-omics analysis to establish a robust grading model for IPA and reveal its biological significance. This multicenter study encompassed 988 patients who underwent radical surgical resection and received a pathological confirmation of IPA. Through integrated analysis of radiomics and pathomics, we constructed and validated an optimal ensemble learning grading model, which integrates multi-scale and multi-modal characteristics, achieved

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12824164 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature