Radiomic signatures to predict response to targeted therapy and immune checkpoint blockade in melanoma patients (pts) on neoadjuvant therapy.

10067 Background: Metastatic melanoma pt outcomes have been revolutionized by targeted therapy (TT) and immune checkpoint blockade (ICB), which are now being evaluated in the neoadjuvant (neoadj) setting. While tumor-based biomarkers may help predict response, predictors of response obtained by less invasive strategies could greatly benefit pt care and allow real-time treatment response monitoring. Radiomic signatures derived from computerized tomography (CT) images have recently been shown to predict response to ICB in stage IV pts. However, the association of radiomic features with pathological response following neoadj therapy has not been assessed. We sought to determine if radiomic assessment predicts pCR in pts receiving neoadj TT and ICB. Methods: We collected data for a cohort of melanoma pts with locoregional metastases who were treated with neoadj TT (n = 33) or ICB (n = 30). Pts received systemic therapy for 8-10 weeks prior to planned surgical resection. Responses were evaluated radiographically (RECIST 1.1) and via pathological assessment (evaluating for pathologic complete response; (pCR) versus < pCR). Thirty two pts (19 ICB; 13 TT) were included in the radiomics analysis based on the availability of appropriate CT imaging. A total of 310 unique radiomic features (10 histogram-based and 300 second-order texture features) were calculated from each extracted volume of interest (VOI). Feature extraction was performed on baseline and initial on-treatment pre-operative CT scans. Features associated with pCR were assessed using a feature selection approach based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model for prediction of pCR to ICB or TT. Leave-One-Out Cross-Validation was performed to evaluate the robustness of the estimates. Results: Out of 310 radiomic features, three features measured at baseline were able to predict a pCR to neoadj ICB or TT with sensitivity, specificity and accuracy of 100%, though these signatures were non-overlapping. In the on-treatment pre-operative scans, 3 distinct features (also non-overlapping and distinct from the predictive pre-treatment signatures) also predicted pCR to ICB and TT with 100% sensitivity, specificity and accuracy. Conclusions: Radiomic signatures in baseline and on-treatment CT scans accurately predict pCR in melanoma pts with locoregional metastases treated with neoadj TT or ICB. These provocative findings warrant further investigation in larger, independent cohorts.