A multimodal dataset for precision oncology in head and neck cancer.

Journal: Nature communications
Published Date: Aug 4, 2025

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.

Authors

  • Marion Dörrich
    Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Matthias Balk
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Tatjana Heusinger
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Sandra Beyer
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Hamed Mirbagheri
    Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • David J Fischer
    Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Hassan Kanso
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Christian Matek
    Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
  • Arndt Hartmann
    Institute of Pathology, University Hospital of Friedrich-Alexander-University Erlangen-Nürnberg, Germany.
  • Heinrich Iro
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Markus Eckstein
    Institute of Pathology, University Hospitals Erlangen, Erlangen, Germany.
  • Antoniu-Oreste Gostian
    Department of Otolaryngology - Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
  • Andreas M Kist
    Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology-Head & Neck Surgery, University Hospital Erlangen, Germany.

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