A Multimodal Head and Neck Cancer Dataset for AI-Driven Precision Oncology
Numan Saeed, Salma Hassan, Shahad Hardan, Ahmed Aly, Darya Taratynova, Umair Nawaz, Ufaq Khan, Muhammad Ridzuan, Thomas Eugene, Rapha"el Metz, M'elanie 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 Valli`eres, Hesham Elhalawani, Ronan Abgral, Romain Floch, Kevin Kerleguer, Ulrike Schick, Maelle Mauguen, Vincent Andrearczyk, Adrien Depeursinge, Mathieu Hatt, Arman Rahmim, Mohammad Yaqub
公開日: 2025/8/30
Abstract
We describe a publicly available multimodal dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies for head and neck cancer research. The dataset includes 1123 FDG-PET/CT studies from patients with histologically confirmed head and neck cancer, acquired from 10 international medical centers. All examinations consisted of co-registered PET/CT scans with varying acquisition protocols, reflecting real-world clinical diversity across institutions. Primary gross tumor volumes (GTVp) and involved lymph nodes (GTVn) were manually segmented by experienced radiation oncologists and radiologists following standardized guidelines and quality control measures. We provide anonymized NifTi files of all studies, along with expert-annotated segmentation masks, radiotherapy dose distribution for a subset of patients, and comprehensive clinical metadata. This metadata includes TNM staging, HPV status, demographics (age and gender), long-term follow-up outcomes, survival times, censoring indicators, and treatment information. We demonstrate how this dataset can be used for three key clinical tasks: automated tumor segmentation, recurrence-free survival prediction, and HPV status classification, providing benchmark results using state-of-the-art deep learning models, including UNet, SegResNet, and multimodal prognostic frameworks.