Radiomic features in recurrent/metastatic platinum refractory head and neck squamous cell carcinoma treated with immunotherapy.

Abstract

e18000 Background: Radiomics is the computerized extraction of quantitative features from medical images, beyond the level of detail accessible to an unaided human eye. Several studies on radiomic analysis have been carried out to identify predictive, prognostic and diagnostic biomarkers for diverse tumor types including HNSCC. Radiomic features proved to be effective in predicting outcomes in patients with locally advanced HNSCC. The aim of the present study was to dentify predictive and prognostic radiomic features in platinum-refractory HNSCC patients with recurrent and/or metastatic (R/M) disease treated with anti PD-1 monotherapy. Methods: We retrospectively reviewed the data of 38 patients treated with Nivolumab at our Institution between January 2018 and March 2020. Nasopharyngeal carcinomas were not eligible. CT radiomic textural features were extracted from regions of interests (ROIs) manually delineated around tumor volumes. Minimum Redundancy Maximum Relevance (mRMR) algorithm was the method of choice to rank radiomic features based on three outcomes: overall response rate (ORR); disease progression (PD) and overall survival (OS). Logistic regression was employed to build predictive/prognostic models. Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was chosen as the metric to assess model performances. Results: Data from 29 out of 38 patients were ultimately analyzed. Nine patients were excluded due to nasopharynx as primary tumor site and/or inadequate CT scan imaging. A total of 57 ROIs were extracted and analyzed. We obtained 9 models: 3 radiomic models, 3 clinical models and 3 combined models (radiomic plus clinical) for each outcome of interest (ORR, PD, OS). In the radiomic models 2 features were identified as predictor for ORR (AUC 0.69), 1 radiomic feature was predictive for PD (AUC 0.83) and 1 predictive for OS (AUC 0.72). In the clinical models, 3 clinical characteristics were identified for both ORR (AUC 0.91) and PD (AUC 0.73) and 2 clinical features were found to be predictive for OS (AUC 0.91). The combined model identified 3 features (2 radiomics and 1 clinical) predictive for ORR (AUC 0.76). No clinical characteristics were found in the combined model for PD. No radiomics features have been shown to be related to OS. Conclusions: This is an explorative study to test the power of radiomics and the potential value of combining radiomics and clinical data as a prognostic and predictive instrument. In this small sample size, the quantitative analysis of CT images seems to be an interesting tool which has to be further explored as predictor of outcome in a larger series of patients.[Table: see text]