A multimodal dataset for precision oncology in head and neck cancer

Marion Dörrich, Matthias Balk, Tatjana Heusinger, Sandra Beyer, Hamed Mirbagheri, David J. Fischer, Hassan Kanso, Christian Matek, Arndt Hartmann, Heinrich Iro, Markus Eckstein, Antoniu-Oreste Gostian and Andreas M. Kist ()
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Marion Dörrich: Friedrich-Alexander-Universität Erlangen-Nürnberg
Matthias Balk: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Tatjana Heusinger: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandra Beyer: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Hamed Mirbagheri: Friedrich-Alexander-Universität Erlangen-Nürnberg
David J. Fischer: Friedrich-Alexander-Universität Erlangen-Nürnberg
Hassan Kanso: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Christian Matek: Bavarian Cancer Research Center (BZKF)
Arndt Hartmann: Bavarian Cancer Research Center (BZKF)
Heinrich Iro: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Markus Eckstein: Bavarian Cancer Research Center (BZKF)
Antoniu-Oreste Gostian: University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg
Andreas M. Kist: Friedrich-Alexander-Universität Erlangen-Nürnberg

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-62386-6

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