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.
Methods and materials: This single-institution retrospective study included 140 patients treated with definitive concurrent chemoradiotherapy, for whom both pre- and midtreatment contrast-enhanced computed tomography (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 pretreatment tumor sphericity. In the testing cohort, the RSF model achieved good discriminability for DMFS (C-index = 0.73, P = .008), and further divided patients into 2 risk groups with different 2-year DMFS rates: 96.7% versus 67.6%. Similar trends were observed for patients with p16+ tumors and smoking ≤10 pack-years. The RSF model based on pretreatment CT features alone achieved lower performance (concordance index = 0.68, P = .03).
Conclusions: 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 human papillomavirus-positive patients who should not be considered for deintensification therapy.
Copyright © 2019 Elsevier Inc. All rights reserved.