Radiomic analysis of patient and interorgan heterogeneity in response to immunotherapies and BRAF-targeted therapy in metastatic melanoma.

Abstract

Variability in treatment response may be attributable to organ-level heterogeneity in tumor lesions. Radiomic analysis of medical images can elucidate non-invasive biomarkers of clinical outcome. Organ-specific radiomic comparison across immunotherapies and targeted therapies has not been previously reported. We queried the UPMC Hillman Cancer Center registry for patients with metastatic melanoma (MEL) treated with immune checkpoint inhibitors (ICI) (anti-programmed cell death protein-1 (PD-1)/cytotoxic T-lymphocyte associated protein 4 (CTLA-4) (ipilimumab+nivolumab; I+N) or anti-PD-1 monotherapy) or BRAF-targeted therapy. The best overall response was measured using Response Evaluation Criteria in Solid Tumors V.1.1. Lesions were segmented into discrete volume-of-interest with 400 radiomics features extracted. Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. 291 patients with MEL were identified, including 242 ICI (91 I+N, 151 PD-1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD-1) and 126 BRAF. Across cohorts, baseline demographics included 39-47% women, 24%-29% M1C, 24-46% M1D, and 61-80% with elevated lactate dehydrogenase. Among ICI patients experiencing DC, the organs with the greatest reduction were liver (-66%±8%; mean±SEM) and lung (-63%±5%). For patients with multiple same-organ target lesions, the highest interlesion heterogeneity was observed in brain among patients who received ICI while no intraorgan heterogeneity was observed in BRAF. 221 ICI patients were included for radiomic modeling, consisting of 86 I+N and 135 PD-1. Models consisting of optimized radiomic signatures classified DC/PD across I+N (area under curve (AUC)=0.85) and PD-1 (0.71) and within individual organ sites (AUC=0.72~0.94). Integration of clinical variables improved the models' performance. Comparison of models between treatments and across organ sites suggested mostly non-overlapping DC or PD features. Skewness, kurtosis, and informational measure of correlation (IMC) were among the radiomic features shared between overall response models. Kurtosis and IMC were also used by multiple organ-site models. In conclusion, differential organ-specific response was observed across BRAF and ICI with within organ heterogeneity observed for ICI but not for BRAF. Radiomic features of organ-specific response demonstrated little overlap. Integrating clinical factors with radiomics improves the prediction of disease course outcome and prediction of tumor heterogeneity.