Deep learning for fully-automated prediction of overall survival in patients with oropharyngeal cancer using FDG PET imaging: an international retrospective study.

Purpose Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [18F]fluorodeoxyglucose PET imaging. Experimental design The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort (n = 268) by integrating five convolutional neural network models for volumetric segmentation and ten models for OS prognostication. Two external test cohorts were enrolled - the first based on the Cancer Imaging Archive (TCIA) database (n = 353) and the second being a clinical deployment cohort (n = 31) - to assess the DeepPET-OPSCC performance and goodness of fit. Results After adjustment for potential confounders, DeepPET-OPSCC was found to be an independent predictor of OS in both discovery and TCIA test cohorts (HR = 2.07; 95% CI 1.31-3.28 and HR = 2.39; 1.38-4.16; both P = 0.002). The tool also revealed good predictive performance, with a c-index of 0.707 (95% CI 0.658-0.757) in the discovery cohort, 0.689 (0.621-0.757) in the TCIA test cohort, and 0.787 (0.675-0.899) in the clinical deployment test cohort; the average time taken was 2 min for calculation per exam. The integrated nomogram of DeepPET-OPSCC and clinical risk factors significantly outperformed the clinical model (AUC at 5 years: 0.801 [95% CI 0.727-0.874] versus 0.749 [0.649-0.842]; P = 0.031) in the TCIA test cohort. Conclusions DeepPET-OPSCC achieved an accurate OS prediction in patients with OPSCC and enabled an objective, unbiased, and rapid assessment for OPSCC prognostication.