AI in Oncology
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 predict- ing overall survival (OS) in OPSCC using [18F]fluorodeoxyglucose (FDG)-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.
The DeepPET-OPSCC demo could be found here
- Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging, Clinical Cancer Research, 2021, OnlineFirst.