Predicting outcomes in head and neck cancer using CT images via transfer learning

Our transfer learning-based framework, OPHN-Net, provided a robust and data-efficient method for predicting treatment outcomes in H&N cancer from non-invasive CT images. The integration of imaging-based predictions with clinical characteristics created a more comprehensive prognostic model. This approach had the potential to facilitate personalized treatment stratification, ultimately leading to improved clinical decision-making and patient outcomes.