Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging.

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 [F]fluorodeoxyglucose (FDG)-PET imaging.

Experimental Design: The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort ( = 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 ( = 353) and the second being a clinical deployment cohort ( = 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% confidence interval (CI), 1.31-3.28 and HR = 2.39; 95% CI, 1.38-4.16; both = 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 (95% CI, 0.621-0.757) in the TCIA test cohort, and 0.787 (95% CI, 0.675-0.899) in the clinical deployment test cohort; the average time taken was 2 minutes 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) vs. 0.749 (95% CI, 0.649-0.842); = 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.