ArticleNature communications2025
A multimodal dataset for precision oncology in head and neck cancer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development and Validation of an Interpretable Machine Learning Model for Predicting Distant Metastasis in Tongue Squamous Cell Carcinoma: A Multicentre Study.International dental journal · 2026Article
- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Review
- Clinical and laboratory biomarker correlates of lip, oral cavity, and pharyngeal cancer status in a large retrospective clinical database.Frontiers in oral health · 2026Article
- Enhancing deep learning interpretability for hand-crafted feature-guided histologic image classification via weak-to-strong generalization.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- A multimodal knowledge-enhanced whole-slide pathology foundation model.Nature communications · 2025Article
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Authors and funding
13 authors.
Funding
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 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.
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Registered trials
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