️ Episode 111: A Multimodal Dataset for Precision Oncology in Head and Neck Cancer

In this episode of PaperCast Base by Base, we explore the creation of HANCOCK, a comprehensive multimodal dataset designed to advance precision oncology in head and neck cancer. The study addresses the urgent need for large, publicly available datasets to improve biomarker discovery and outcome prediction in this challenging disease.

Study Highlights:
The HANCOCK dataset integrates real-world data from 763 patients, including demographics, pathology, blood tests, surgery reports, and histologic images. By combining these modalities, the researchers created multimodal patient vectors that capture complex interdependencies and enable robust machine learning analyses. They demonstrated that multimodal integration significantly improves prediction of survival and recurrence compared to single-modality approaches, achieving high accuracy with Random Forest classifiers. Furthermore, the study showed that incorporating imaging data using multiple instance learning and pathology foundation models enhances clinical endpoint prediction, reinforcing the value of multimodal strategies for oncology.

Conclusion:
This work establishes HANCOCK as a unique open-access resource that will catalyze future research in biomarker discovery, multimodal AI integration, and personalized treatment strategies for head and neck cancer.

Reference:
Dörrich M, Balk M, Heusinger T, Beyer S, Mirbagheri H, Fischer DJ, Kanso H, Matek C, Hartmann A, Iro H, Eckstein M, Gostian A-O, Kist AM. A multimodal dataset for precision oncology in head and neck cancer. *Nature Communications*. 2025;16:7163. https://doi.org/10.1038/s41467-025-62386-6

License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/

Support:
If you'd like to support Base by Base, you can make a one-time or monthly donation here: https://basebybase.castos.com/

Keywords: head and neck cancer, multimodal dataset, machine learning, biomarkers, precision oncology

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