Integrating genomic and pathological characteristics to enhance prognostic precision in advanced NSCLC.

ABSTRACT: Although immunotherapy combined with chemotherapy (ICT) is the standard treatment for advanced non-small cell lung cancer (NSCLC), identification of reliable prognostic biomarkers remains challenging. In this multicenter study, we performed next-generation sequencing of tumor samples from 162 patients receiving first-line ICT at the Chinese PLA General Hospital and collected their pathological image information. First, we established a model to predict the risk of tumor progression based on genomic characteristics. Furthermore, a deep learning method was employed to recognize different cell types from pathological images, which significantly improved the accuracy of progression-free survival (PFS) and overall survival (OS) prediction. In summary, we constructed a Prognostic Multimodal Clas

SUBMITTER: Han Y 

PROVIDER: S-EPMC12318087 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature