Prediction of Treatment Outcome and Survival Using 18F-FDG PET/CT Radiomic Features and Standard Biomarkers in Patients with Advanced Melanoma Treated Using Dual Immune Checkpoint Inhibitor Therapy

1704 Objectives: Dual immune checkpoint inhibitor (DICB) therapy has been shown to improve outcomes for patients with advanced melanoma. The aim of the present study was to assess if radiomic features extracted from baseline 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) images can be used to predict the overall survival (OS) and progression-free survival (PFS) of patients with advanced melanoma who underwent DICB therapy. The predictive value of these features was compared with commonly-reported biomarkers of melanoma treatment outcomes. Methods: This IRB-approved retrospective study was conducted by identifying melanoma patients who underwent dual immune checkpoint inhibitor therapy (ipilimumab + nivolumab) and who had a pre-treatment FDG PET/CT scan within 2 months of treatment initiation. Patients were categorized as treatment responders (complete response or partial response) or non-responders (stable or progressive disease) based on the oncologist’s clinical assessment at the last documented clinic visit following the end of ICI therapy. Using these response metrics, receiver operating characteristic (ROC) analysis was performed to determine optimal baseline cutoffs (using Youden’s index) for standardized uptake value (SUV)-based and radiomic features. Using MIM v6.9.3, regions of interest (ROI) were drawn to delineate the lesion with the highest maximum SUV (SUVMAX). ROIs were created semi-automatically with manual corrections using thresholds based on the PET response criteria in solid tumors (PERCIST). Radiomic features (68 in total) were then extracted using LIFEx v5.28. Baseline Eastern Cooperative Oncology Group (ECOG) score, disease stage, age, lactate dehydrogenase (LDH) levels, and mutational status (BRAF or NRAS) were also collected. Survival was then assessed using Kaplan-Meier analysis. Hazard ratios (HR) were calculated using Cox proportional hazards models. Data analyses were performed using R version 3.6.2 (http://cran.r-project.org/). A p-value less than 0.05 was considered significant, unless otherwise indicated. Results: We identified 55 patients (36M/19F; mean age 60.0 ± 10.0 years) who were treated using DICB therapy and who underwent baseline FDG PET/CT imaging between November, 2014 and November, 2018. Thirty patients were categorized as responders and 25 patients as non-responders. After correcting for multiple comparisons, baseline SUVPEAK (p = 0.010), intensity-based entropy (p = 0.009), mutational status (p < 0.001), and ECOG (p = 0.001) were predictive of treatment outcome. Baseline intensity, shape, and textural features, as well as baseline disease stage and age, were not predictive of OS or PFS. Baseline ECOG (p = 0.009; HR = 2.9), mutational status (p < 0.001; HR = 9.5), and LDH (p < 0.001; HR = 3.9) predicted OS (Figure 1A - 1D). An OS model combining baseline ECOG, mutational status, and LDH showed a hazard ratio of 8.1 (p < 0.001). Baseline ECOG (p = 0.013; HR = 2.5), mutational status (p < 0.001; HR = 3.6), and LDH (p < 0.010; HR = 2.5) also predicted PFS (Figure 1E - 1H). A PFS model combining baseline ECOG, mutational status, and LDH showed a hazard ratio of 3.6 (p < 0.001). Conclusions: Radiomic features extracted from baseline FDG PET/CT images were mostly unsuccessful at predicting OS, PFS, or DICB treatment response, though intensity-based entropy was predictive of treatment response. SUVPEAK also successfully predicted treatment response. OS and PFS were not predicted by any SUV-based or radiomic features. Baseline ECOG score, LDH levels, and mutational status (BRAF or NRAS) all successfully predicted OS and PFS. Radiomic features, generally, appear to be poor predictors of DICB treatment response, OS, and PFS, while baseline performance status, LDH, and tumor mutational status are useful predictors of outcomes following DICB therapy.