medRxiv : the preprint server for health sciences · 2025
Multiregional CT Features Improve Prediction of Immunotherapy Response in Advanced Melanoma.
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Kokenberger Grant, Singh Jatin, Hurd Drew, Yu Tong, Meng Xin, Nguyen Madison, Storkus Walter J, McKay William, Luke Jason J, Najjar Yana G, Kirkwood John M, Zarour Hassane M, Davar Diwakar, Pu Jiantao
Аннотация
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 regression model using the same features yielded an AUROC of 0.82 (95% CI: 0.74-0.91), significantly outperforming a clinical-only model (AUROC 0.65; 95% CI: 0.56-0.74; p=0.006).
CONCLUSION: Integrating imaging features from multiple anatomical regions can improve the prediction of immunotherapy response in advanced melanoma.
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 regression model using the same features yielded an AUROC of 0.82 (95% CI: 0.74-0.91), significantly outperforming a clinical-only model (AUROC 0.65; 95% CI: 0.56-0.74; p=0.006).
CONCLUSION: Integrating imaging features from multiple anatomical regions can improve the prediction of immunotherapy response in advanced melanoma.