Computational pathology features of immune architecture predict clinically relevant outcomes in small-cell lung cancer (SCLC)
Year: 2026
Type: article
Study design: Observational Study
Abstract: First-line treatment for small-cell lung cancer (SCLC) involves platinum-based chemotherapy and immunotherapy for extensive (ES) and limited (LM) disease, respectively. Rapid progression and metastasis highlight the need for improved biomarkers. We developed PhenopyCell, a computational pathology tool that quantifies immune-tumor spatial architecture on Hematoxylin and Eosin (H&E) slides to predict outcomes. Developing PhenopyCell for spatial quantification of immune-tumor interactions and clinical outcome prediction. Retrospective study of 281 SCLC patients (149 LM, 132 ES) treated with platinum chemotherapy (2010–2020) from multi-institutional archives, divided into training (D1, n = 101) and validation (D2/D3, n = 180) cohorts. PhenopyCell extracted 101 spatial features (immune clustering, tumor density) from whole-slide images. Overall survival (OS) via Cox models and chemotherapy response through ROC and precision-recall analyses. PhenopyCell-derived features correlated with OS and treatment response across datasets (D1 HR = 1.66, P = 0.036; D2 HR = 1.98, P = 0.04; D3 HR = 2.13, P = 0.04). Stratified analyses showed strong prognostic value for both ES-SCLC (HR up to 5.11) and LM-SCLC (HR up to 34.91). Chemotherapy response prediction achieved AUCs of 0.62–0.79. PhenopyCell independently predicts survival and therapy response in SCLC, outperforming conventional histopathology and supporting personalized treatment approaches.
Source: npj Precision Oncology
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,090
Funders: Biomedical Laboratory Research and Development, VA Office of Research and Development, National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Dental and Craniofacial Research, U.S. National Library of Medicine
Awards: G1057899285, G1365000891, G1521530650, Computational Pathology of Proteinuric Diseases, Lung cancer in East Africa and the relationship to HIV-1 infection: epidemiology, molecular characterization and imaging, CHIRP Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung Cancers, RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATION, G5093361859, G522160186, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, G5879436965, G6100242255, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, Multi-Consortia Coordinating Center (MC2 Center) for Cancer Biology: Building Interdisciplinary Scientific Communities, Coordinating Impactful Resource Sharing, and Advancing Cancer Research, Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC), Quantitative imaging phenotypic classifier for distinguishing radiation effects from tumor recurrence in Glioblastoma, Prostate Cancer Biomarker and Imaging Validation Alliance: Emory University, University of Alabama Birmingham, and University of Texas Southwestern, P-CARRS-BRAIN: Multi-domain (genetic, socio-behavioral, vascular) risk factors and prediction of Alzheimer’s Disease continuum in South Asians in India, Prognostic and Predictive Digital Tissue Image Assay for Prostate Cancer, An AI-enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit
OpenAlex ID: W7138121023