Computational pathology features of immune architecture predict clinically relevant outcomes in small-cell lung cancer (SCLC)

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.