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

arXiv: https://arxiv.org/abs/2509.00367 · HTML: https://mlsanity.com/paper/2509.00367/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction](https://mlsanity.com/paper/2209.05036/) (2022)
- [An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data](https://mlsanity.com/paper/2202.12537/) (2022)
- [SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast](https://mlsanity.com/paper/2403.10603/) (2024)

## Built on by (most cited first, 6 of 6)

- [HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT](https://mlsanity.com/paper/2606.20143/) (2026)
- [Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction](https://mlsanity.com/paper/2607.16802/) (2026)
- [AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising](https://mlsanity.com/paper/2607.00509/) (2026)
- [HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT](https://mlsanity.com/paper/2607.26498/) (2026)
- [SGRNet: Spatially Guided Radiology Network for Structured Radiological Reporting of Head and Neck Cancer](https://mlsanity.com/paper/2608.29153/) (2026)
- [Adaptive Computation with Conformal Certification](https://mlsanity.com/paper/2610.19189/) (2026)

## Abstract (arXiv)

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.