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, that can be explored in a low-dimensional representation. We can show that combining these modalities using machine learning is superior to a single modality and the integration of imaging data using foundation models helps in endpoint prediction. We believe tha

SUBMITTER: Dorrich M 

PROVIDER: S-EPMC12322140 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature