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<p>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&amp;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, <i>n</i> = 101) and validation (D2/D3, <i>n</i> = 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 (D<sub>1</sub> HR = 1.66, <i>P</i> = 0.036; D<sub>2</sub> HR = 1.98, <i>P</i> = 0.04; D<sub>3</sub> HR = 2.13, <i>P</i> = 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Cristian Barrera,
  • Prantesh Jain,
  • Germán Corredor,
  • Tanmoy Dam,
  • Himanshu Maurya,
  • Tilak Pathak,
  • Boulos Beshai,
  • Stuthi Perimbeti,
  • Jaishree Jagirdar,
  • Kristin Higgins,
  • Afshin Dowlati,
  • Anant Madabhushi

摘要

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.