Abstract
METHODS
Patients with advanced cutaneous melanoma treated with first-line anti-PD1 ±anti-CTLA4 between 2016 and 2023 across 11 Dutch centers were retrospectively identified using prospectively collected registry data. Pre-treatment H&E-stained metastatic slides were collected from 30 pathology labs and analyzed using 14 foundation models combined with attention-based or clustering-constrained multiple-instance learning. Primary outcome was best overall response (ORR) (RECIST 1.1) assessed through leave-one-hospital-out cross-validation. Response probabilities were added to a clinical model built with AIC-based backward selection (including WHO performance status, liver metastases, LDH, ICI-type, AJCC stage, brain metastases). Feature embedding clusters were labelled by three pathologists blinded to outcomes to assess histology driving predictions.
CONCLUSION
AI-based analysis of diagnostic pre-treatment metastatic melanoma samples predicts ICI outcomes by using explainable histological features, such as immune infiltration, cell morphology, and necrosis. The predictive performance of the AI-based analysis improves further upon addition of clinical information.
RESULTS
Of 1935 patients, 1177 had a pre-treatment metastatic specimen available, most commonly lymph node or skin/soft tissue; 35.3% was treated with anti-PD1 +anti-CTLA4 combination therapy. ORR was 56.7% (n = 667/1177). The best AI-based model achieved an AUROC of 0.63 (95% CI:0.60-0.66) with overestimated risk. Combining the AI-based analysis with clinical information increased AUROC to 0.66 (95% CI:0.63-0.69) with improved calibration. Model predictions were driven by immune infiltration and epithelioid morphology for response, and by spindle-cell morphology, and necrosis for non-response.
INTRODUCTION
Readily available predictive biomarkers for immune checkpoint inhibitor (ICI) response in advanced melanoma are limited. This study evaluates the predictive value of AI-based histopathology analysis.