Multilabel learning and model visualization for reliable head and neck cancer radiotherapy outcome prediction

Head and neck cancers (HNC) encompass a spectrum of malignancies affecting the oral cavity, throat, and adjacent regions. For treating HNC patients, radiation therapy (RT) is often required for managing the disease. However, the accurate prognosis and personalized RT of HNC remain challenging, given the limited number of training data, varied anatomical sites and heterogeneous patient-specific responses to treatment. While investigations into patients’ electronic health records (EHR) data and image data have shown improved prognostic value compared to the widely used tumor staging based method, most of the models for RT outcome have predominantly focused on the prediction of a single specific outcome. These models have provided valuable insights, however, they neglect the interconnected nature of different clinical outcomes and the potential for shared representation learning across multiple tasks or labels. In addition, for deep learning-based prediction, scant attention has been given to deep model interpretation analysis to elucidate the decision-making process. These oversights limit the models’ ability to harness the full spectrum of available data, potentially obscuring critical insights into the multifaceted nature of cancer progression and treatment response.