A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction

• URL: http://arxiv.org/abs/2509.00367v3
• Date: Sat, 20 Sep 2025 11:24:54 GMT
• Title: A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction
• Authors: Numan Saeed, Salma Hassan, Shahad Hardan, Ahmed Aly, Darya Taratynova, Umair Nawaz, Ufaq Khan, Muhammad Ridzuan, Vincent Andrearczyk, Adrien Depeursinge, Yutong Xie, Thomas Eugene, Raphaël Metz, Mélanie Dore, Gregory Delpon, Vijay Ram Kumar Papineni, Kareem Wahid, Cem Dede, Alaa Mohamed Shawky Ali, Carlos Sjogreen, Mohamed Naser, Clifton D. Fuller, Valentin Oreiller, Mario Jreige, John O. Prior, Catherine Cheze Le Rest, Olena Tankyevych, Pierre Decazes, Su Ruan, Stephanie Tanadini-Lang, Martin Vallières, Hesham Elhalawani, Ronan Abgral, Romain Floch, Kevin Kerleguer, Ulrike Schick, Maelle Mauguen, David Bourhis, Jean-Christophe Leclere, Amandine Sambourg, Arman Rahmim, Mathieu Hatt, Mohammad Yaqub,
• Abstract summary: We present a publicly available multimodal dataset for head and neck cancer research.<n>All studies contain co-registered PET/CT scans with varying acquisition protocols.<n>We benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification.
• Score: 5.4735577512942655
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a publicly available multimodal dataset for head and neck cancer research, comprising 1123 annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies from patients with histologically confirmed disease, acquired from 10 international medical centers. All studies contain co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity from a long-term, multi-institution retrospective collection. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following established guidelines. We provide anonymized NifTi files, expert-annotated segmentation masks, comprehensive clinical metadata, and radiotherapy dose distributions for a patient subset. The metadata include TNM staging, HPV status, demographics, long-term follow-up outcomes, survival times, censoring indicators, and treatment information. To demonstrate its utility, we benchmark three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, using state-of-the-art deep learning models like UNet, SegResNet, and multimodal prognostic frameworks.

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