A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction
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.
Keywords
Cite
@article{arxiv.2509.00367,
title = {A Multimodal and Multi-centric Head and Neck Cancer Dataset for Segmentation, Diagnosis and Outcome Prediction},
author = {Numan Saeed and Salma Hassan and Shahad Hardan and Ahmed Aly and Darya Taratynova and Umair Nawaz and Ufaq Khan and Muhammad Ridzuan and Vincent Andrearczyk and Adrien Depeursinge and Yutong Xie and Thomas Eugene and Raphaël Metz and Mélanie Dore and Gregory Delpon and Vijay Ram Kumar Papineni and Kareem Wahid and Cem Dede and Alaa Mohamed Shawky Ali and Carlos Sjogreen and Mohamed Naser and Clifton D. Fuller and Valentin Oreiller and Mario Jreige and John O. Prior and Catherine Cheze Le Rest and Olena Tankyevych and Pierre Decazes and Su Ruan and Stephanie Tanadini-Lang and Martin Vallières and Hesham Elhalawani and Ronan Abgral and Romain Floch and Kevin Kerleguer and Ulrike Schick and Maelle Mauguen and David Bourhis and Jean-Christophe Leclere and Amandine Sambourg and Arman Rahmim and Mathieu Hatt and Mohammad Yaqub},
journal= {arXiv preprint arXiv:2509.00367},
year = {2025}
}
Comments
10 pages, 5 figures. Numan Saeed is the corresponding author. Numan Saeed, Salma Hassan and Shahad Hardan contributed equally to this work. Project page: https://hecktor25.grand-challenge.org/