HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT

2026-06-18

AI Summary

Accurate segmentation of head and neck tumors and non-invasive imaging-based prediction of recurrence risk and HPV status remain challenging. This study establishes the first comprehensive benchmark integrating multimodal PET/CT scans and electronic health records from over 1,100 patients across multiple centers, simultaneously advancing three core tasks: automated tumor segmentation, survival prognosis prediction, and HPV status classification. The proposed deep learning framework fuses imaging and clinical data, achieving a Dice coefficient of 0.75 for segmentation, a concordance index (C-index) of 0.66 for survival prediction, and a balanced accuracy of 0.56 for HPV classification on the test set. These results lay a robust foundation for clinically translatable artificial intelligence systems in head and neck oncology.

Abstract

Head and neck cancers (HNC) represent a significant global health burden, with accurate tumor delineation being essential for effective radiotherapy planning. The complexity of the oropharyngeal anatomy, combined with the heterogeneous appearance of tumors on imaging, makes manual segmentation time-intensive and subject to inter-observer variability. Beyond segmentation, predicting long-term clinical outcomes, such as recurrence-free survival (RFS), and determining human papillomavirus (HPV) status from noninvasive imaging, remain challenging yet clinically valuable goals. The HECKTOR 2025 challenge addresses these needs by establishing a comprehensive benchmark for automated HNC analysis using multimodal PET/CT imaging and electronic health records. Building on previous editions (2020-2022), this challenge features an expanded multi-institutional dataset comprising over 1,100 patients from 10 centers worldwide. Participants were tasked with three complementary objectives: (1) segmenting primary gross tumor volumes (GTVp) and metastatic lymph nodes (GTVn), (2) predicting recurrence-free survival, and (3) classifying HPV status. The challenge attracted 35 registered teams, with 15 final submissions evaluated on a held-out test set. Top-performing algorithms achieved a mean Dice similarity coefficient of 0.75 for segmentation, a concordance index of 0.66 for survival prediction, and a balanced accuracy of 0.56 for HPV classification. This paper presents a comprehensive analysis of the submitted methodologies, evaluates their performance across different lesion characteristics, and discusses their implications for clinical translation in automated oncology workflows and decision support systems.

Problem

• head and neck cancer
• tumor segmentation
• recurrence-free survival
• HPV status
• multimodal PET/CT

Innovation

• multimodal PET/CT
• automated segmentation
• recurrence-free survival prediction
• HPV status classification
• multi-institutional benchmark