Multiregional CT Features Improve Prediction of Immunotherapy Response in Advanced Melanoma

作者：Grant Kokenberger, Jatin Singh, Drew E. Hurd, Tong Yu, Xin Meng, Madison Nguyen, Walter J. Storkus, W. J. McKay, Jason J. Luke, Yana G. Najjar, John M. Kirkwood, Hassane M. Zarour, Diwakar Davar, Jiantao Pu · 发表于：medRxiv · 年份：2025 · DOI：10.1101/2025.10.26.25338837 · 被引用次数：1 · 研究领域：Cutaneous Melanoma Detection and Management、Cancer Immunotherapy and Biomarkers、Radiomics and Machine Learning in Medical Imaging

Objective: Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomography (CT) features from multiple anatomical regions could predict immunotherapy response. Materials and Methods: This study included 157 advanced cutaneous melanoma patients (mean age: 63.3 years; 65.6% male) treated with PD-1 immune checkpoint inhibitor (ICI) singly or in combination with LAG-3 or CTLA-4 ICIs. The primary outcome was 1-year progression-free survival (PFS ≥12 months). Available artificial intelligence (AI) algorithms were applied to pretreatment CT scans to extract and quantify three-dimensional (3D) body composition and thoracic features across abdominal, chest, pelvic regions, and spinal vertebrae. Feature relationship to PFS was assessed. Machine learning (ML) models were used to predict PFS, utilizing only the most important five features to mitigate overfitting. Prediction performance was evaluated using the area under the receiver operating curve (AUROC) with stratified 10-fold cross-validation. Results: Multiple CT features are significantly associated with immunotherapy response. Body tissues at the L5 spinal vertebrae emerged as key predictors. A random forest classifier (RFC) trained on three CT features (L5 bone volume, L5 subcutaneous adipose tissue volume, pelvis visceral adipose tissue density) and two clinical variables achieved a mean AUROC of 0.83 (95% CI: 0.72-0.94). A logistic regress...