{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jiang Y"],"funding":["Postdoctoral Science Foundation of Jiangsu Province","16th batch 'Summit of the Six Top Talents' Program of Jiangsu Province","Jiangsu Provincial Health Committee Medical projects","National Natural Science Foundation of China","China Postdoctoral Science Foundation 12th batch Special fund","16th batch ‘Summit of the Six Top Talents’ Program of Jiangsu Province","Natural Science Foundation of Jiangsu Province"],"pagination":["241"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11448477"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["73(12)"],"pubmed_abstract":["Background
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.Methods
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 stai"],"journal":["Cancer immunology, immunotherapy : CII"],"pubmed_title":["A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients."],"pmcid":["PMC11448477"],"funding_grant_id":["45786","82100095","WSN-154","M2022110","2018K049A","BK20180139"],"pubmed_authors":["Zhang F","Lu W","Wu Q","Zuo X","Wang D","Jiang Y","Zhan P","Li Y","Wang Q","Cheng Q","Chen Y","Lv T","Wang X","Song Y"],"additional_accession":[]},"is_claimable":false,"name":"A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.","description":"Background
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.Methods
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 stai","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-07-16T23:55:44.904Z","creation":"2026-07-13T03:06:40.949Z"},"accession":"S-EPMC11448477","cross_references":{"pubmed":["39358575"],"doi":["10.1007/s00262-024-03829-9"]}}