A multimodal dataset for precision oncology in head and neck cancer

Abstract

Head and neck cancer is a common disease and is associated with a poor prognosis. A promising approach to improving patient outcomes is personalized treatment, which uses information from a variety of modalities. However, only little progress has been made due to the lack of large public datasets. We present a multimodal dataset, HANCOCK, that comprises monocentric, real-world data of 763 head and neck cancer patients. Our dataset contains demographical, pathological, and blood data as well as surgery reports and histologic images. We show its potential clinical impact in a multimodal machine-learning setting by proposing adjuvant treatment for previously unidentified risk patients. We found that especially the multimodal model outperformed single-modality models (area under the curve (AUC): 0.85). We believe that HANCOCK will not only open new insights into head and neck cancer pathology but also serve as a major source for researching multimodal machine-learning methodologies in precision oncology.