ArticleCancer immunology, immunotherapy : CII2024
A random survival forest-based pathomics signature classifies immunotherapy prognosis and profiles TIME and genomics in ES-SCLC patients.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of baseline LIPI, LDH and dNLR in ES-SCLC patients receiving immune checkpoint inhibitors: a systematic review and meta-analysis.Frontiers in immunology · 2025Pooled it
- SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides.iScience · 2026Article
- Divergent outcomes of neoadjuvant therapy for locally advanced small-cell lung cancer: two cases report and literature review.Discover oncology · 2025Article
- MuTATE: an interpretable multi-endpoint machine learning framework for automated molecular subtyping in cancer.npj health systems · 2025Article
- Efficacy and safety of integrating consolidative thoracic radiotherapy with immunochemotherapy in extensive-stage small cell lung cancer: a real-world retrospective analysis.Journal of thoracic disease · 2025Article
- Multi-omics integration and machine learning-driven construction of an immunogenic cell death prognostic model for colon cancer and functional validation of FCGR2A.Frontiers in pharmacology · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundSmall 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.
methodsWe 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 staining, and multiplex immunohistochemical staining for immune cells were performed on a subset of patients. A random survival forest (RSF) model encompassing clinical and pathomics features was established to predict overall survival. The function of putative genes was assessed via single-cell RNA sequencing. RESULTS AND
conclusionDuring the median follow-up period of 12.12 months, 118 ES-SCLC patients receiving first-line immunotherapy were recruited. The RSF model utilizing three pathomics features and liver metastases, bone metastases, smoking status, and lactate dehydrogenase, could predict the survival of first-line chemoimmunotherapy in patients with ES-SCLC with favorable discrimination and calibration. Underlyingly, the higher RSF-Score potentially indicated more infiltration of CD8
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