HEad and neCK TumOR (HECKTOR) Lesion Segmentation, Diagnosis and Prognosis using Multimodal Data
Creators: name: Saeed, Numan
name: Ridzuan, Muhammad
name: Hardan, Shahad
name: Hassan, Salma
name: Taratynova, Darya
name: Khan, Ufaq
name: Nawaz, Umair
name: Xie, Yutong
name: Yaqub, Mohammad
name: Rahmim, Arman
name: Yousefirizi, Fereshteh
name: Depeursinge, Adrien
name: Andrearczyk, Vincent
name: Hatt, Mathieu
name: Shiri, Isaac
Publisher: name: Zenodo
Year: 2025
Subjects: Head and Neck cancer
automatic segmentation
radiomics
PET/CT
multimodal
outcome prediction
MICCAI 2025 challenge

description: Head and Neck (H&N;) cancers are among the most common cancers worldwide (5th leading cancer by incidence) (Parkin et al. 2005). Radiotherapy combined with cetuximab has been established as a standard treatment (Bonner et al. 2010). However, locoregional failures remain a major challenge, occurring in up to 40% of patients in the first two years after the treatment (Chajon et al. 2013). Recently, several radiomics studies based on Positron Emission Tomography (PET) and Computed Tomography (CT) imaging were proposed to better identify patients with a worse prognosis in a non-invasive fashion and by exploiting already available images such as these acquired for diagnosis and treatment planning (Vallières et al. 2017),(Bogowicz et al. 2017), (Castelli et al. 2017). By focusing on metabolic and morphological tissue properties, PET and CT modalities include complementary and synergistic information for cancerous lesion segmentation and tumor characteristics potentially relevant for patient outcome prediction, in addition to usual clinical variables (e.g., age, gender, treatment modality, etc.). Modern image analysis (radiomics, machine, and deep learning) methods must be developed and, more importantly, rigorously evaluated in order to extract and leverage this information. Although highly promising, these methods were validated on relatively small cohorts of patients. Further validation on larger, multicentric cohorts (e.g.,>1000 patients, >5 centers) is required to ensure an adequate ratio between the number of variables and observations to avoid overestimating the generalization performance.

In 2020, we organized the first HECKTOR challenge to offer an opportunity for participants working on 3D segmentation algorithms to develop automatic bi-modal approaches for the segmentation of H&N; primary tumors (GTVp) in PET/CT scans, focusing on oropharyngeal cancers.

In the 2021 edition, the scope of the challenge was  expanded by proposing the segmentation task on a larger population (adding patients from a new clinical center for a total of 325 cases) as well as adding a second task (split into two subtasks: with or without reference contours), namely the prediction of Progression-Free Survival (PFS). In 2022, the scope was expanded even further by increasing the complexity of the segmentation task (no bounding box provided, and lymph nodes (GTVn) had to be delineated in addition to the primary tumor), adding data from 3 new centers (total of 883/828 cases for Tasks 1 and 2 respectively) to ensure an entirely unseen test set (all test data of 2021 was moved to the training set of 2022), and switching to the prediction of Recurrence-Free Survival (RFS).

For the 2025 edition, we propose the following updates and additions to improve the clinical relevance, increase the interest of competitors, and add novelty:



All data included in the 2022 edition (~850 patients) will be updated as much as possible, especially regarding the previously missing clinical information, such as the HPV status, which is highly clinically relevant, and the follow-up (to increase the duration of follow-up and potentially the number of recorded recurrence events). It will constitute the training set of 2025.

Four new cohorts (1 multicentric, 2 French, 1 Swiss) joined the consortium and will provide 4 entirely new cohorts of patients, which will constitute a new, previously unseen test set (total of ~400 patients). The 2025 database should, therefore, include more than 1500 patients.

The segmentation task (Task 1) will be the same as in 2022 but the evaluation will be refined using more appropriate metrics to differentiate better algorithms’ ability to detect multiple distinct lesions in addition to segmenting them.

Regarding the outcome prediction task, it will be split into a diagnostic task (new Task 3) with high clinical relevance, i.e. the prediction of the HPV status from images and clinical data, in addition to the RFS prediction (Task 2, same as in 2022), which will be expanded by providing updated clinical information (see point 1 above),and dose maps (+ dosimetry CT) of the radiotherapy planning as an added information channel to include in the prediction models, for an estimated subset of 650 patients.


References



Bogowicz, Marta, Oliver Riesterer, Luisa Sabrina Stark, Gabriela Studer, Jan Unkelbach, Matthias Guckenberger, and Stephanie Tanadini-Lang. 2017. “Comparison of PET and CT Radiomics for Prediction of Local Tumor Control in Head and Neck Squamous Cell Carcinoma.” Acta Oncologica 56 (11): 1531–36.

Bonner, James A., Paul M. Harari, Jordi Giralt, Roger B. Cohen, Christopher U. Jones, Ranjan K. Sur, David Raben, et al. 2010. “Radiotherapy plus Cetuximab for Locoregionally Advanced Head and Neck Cancer: 5-Year Survival Data from a Phase 3 Randomised Trial, and Relation between Cetuximab-Induced Rash and Survival.” The Lancet Oncology 11 (1): 21–28.

Castelli, J., A. Depeursinge, V. Ndoh, J. O. Prior, M. Ozsahin, A. Devillers, H. Bouchaab, et al. 2017. “A PET-Based Nomogram for Oropharyngeal Cancers.” European Journal of Cancer 75 (April): 222–30.

Chajon, Enrique, Caroline Lafond, Guillaume Louvel, Joël Castelli, Danièle Williaume, Olivier Henry, Franck Jégoux, et al. 2013. “Salivary Gland-Sparing Other than Parotid-Sparing in Definitive Head-and-Neck Intensity-Modulated Radiotherapy Does Not Seem to Jeopardize Local Control.” Radiation Oncology. https://doi.org/10.1186/1748-717x-8-132.

Parkin, D. M., F. Bray, J. Ferlay, and P. Pisani. 2005. “Global Cancer Statistics, 2002.” CA: A Cancer Journal for Clinicians. https://doi.org/10.3322/canjclin.55.2.74.

Vallières, Martin, Emily Kay-Rivest, Léo Jean Perrin, Xavier Liem, Christophe Furstoss, Hugo J. W. L. Aerts, Nader Khaouam, et al. 2017. “Radiomics Strategies for Risk Assessment of Tumour Failure in Head-and-Neck Cancer.” Scientific Reports 7 (1): 10117.