biostudies-literatureJiang YPostdoctoral Science Foundation of Jiangsu Province16th batch 'Summit of the Six Top Talents' Program of Jiangsu ProvinceJiangsu Provincial Health Committee Medical projectsNational Natural Science Foundation of ChinaChina Postdoctoral Science Foundation 12th batch Special fund16th batch ‘Summit of the Six Top Talents’ Program of Jiangsu ProvinceNatural Science Foundation of Jiangsu Province241https://www.ebi.ac.uk/biostudies/studies/S-EPMC11448477biostudies-literatureUnknown73(12)<h4>Background</h4>Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME.<h4>Methods</h4>We retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 staiCancer immunology, immunotherapy : CIIA random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.PMC114484774578682100095WSN-154M20221102018K049ABK20180139Zhang FLu WWu QZuo XWang DJiang YZhan PLi YWang QCheng QChen YLv TWang XSong YfalseA random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.<h4>Background</h4>Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor with high mortality, and only a limited subset of extensive-stage SCLC (ES-SCLC) patients demonstrate prolonged survival under chemoimmunotherapy, which warrants the exploration of reliable biomarkers. Herein, we built a machine learning-based model using pathomics features extracted from hematoxylin and eosin (H&E)-stained images to classify prognosis and explore its potential association with genomics and TIME.<h4>Methods</h4>We retrospectively recruited ES-SCLC patients receiving first-line chemoimmunotherapy at Nanjing Jinling Hospital between April 2020 and August 2023. Digital H&E-stained whole-slide images were acquired, and targeted next-generation sequencing, programmed death ligand-1 stai2024-01-01T00:00:00Z2024 Oct2026-07-16T23:55:44.904Z2026-07-13T03:06:40.949ZS-EPMC114484773935857510.1007/s00262-024-03829-9