Abstract

PURPOSE: Prognostic biomarkers of disease relapse are needed for risk-adaptive therapy of oropharyngeal cancer (OPC). This work aims to identify an imaging signature to predict distant metastasis in OPC.MATERIALS/METHODS: This single-institution retrospective study included 140 patients treated with definitive concurrent chemoradiotherapy, for whom both pre and mid-treatment contrast-enhanced CT scans were available. Patients were divided into separate training and testing cohorts. Forty-five quantitative image features were extracted to characterize tumor and involved lymph nodes at both time points. By incorporating both imaging and clinicopathological features, a random survival forest (RSF) model was built to predict distant metastasis-free survival (DMFS). The model was optimized via repeated cross-validation in the training cohort, and then independently validated in the testing cohort.RESULTS: The most important features for predicting DMFS were the maximum distance among nodes, maximum distance between tumor and nodes at mid-treatment, and pre-treatment tumor sphericity. In the testing cohort, the RSF model achieved good discriminability for DMFS (C-index=0.73, P=0.008), and further divided patients into two risk groups with different 2-year DMFS rates: 96.7% vs. 67.6%. Similar trends were observed for patients with p16+ tumors and smoking =10 pack-years. The RSF model based on pre-treatment CT features alone achieved lower performance (C-index=0.68, P=0.03).CONCLUSION: Integrating tumor and nodal imaging characteristics at baseline and mid-treatment CT allows prediction of distant metastasis in OPC. The proposed imaging signature requires prospective validation, and if successful, may help identify high-risk HPV-positive patients who should not be considered for de-intensification therapy.

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