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HEad and neCK TumOR segmentation and outcome prediction in PET/CT images

Vincent Andrearczyk; Valentin Oreiller; Martin Vallières; Mathieu Hatt; Catherine Cheze-Le Rest; Dimitris Visvikis; Mario Jreige; Hesham Elhalawani; Sarah Boughdad; John O. Prior; Adrien Depeursinge

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 standard treatment (Bonner et al. 2010). However, locoregional failures remain a major challenge and occur 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). Although highly promising, these methods were validated on 100-400 patients. Further validation on larger cohorts (e.g. 300-3000 patients) is required to ensure an adequate ratio between the number of variables and observations in order to avoid an overestimation of the generalization performance. Achieving such a validation requires the manual delineation of primary tumors and nodal metastases for every patient and in three dimensions, which is intractable and error-prone.

Methods for automated lesion segmentation in medical images were proposed in various contexts, often achieving expert-level performance (Heimann and Meinzer 2009), (Menze et al. 2015). Surprisingly few studies evaluated the performance of computerized automated segmentation of tumor lesions in PET and CT images (Song et al. 2013),(Blanc-Durand et al. 2018), (Moe et al. 2019).

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 tumors in PET/CT scans, focusing on oropharyngeal cancers. Following good participation and promising results in the 2020 challenge, we will increase the dataset size with 81 new cases provided by additional organization partners, from another clinical center with a different PET/CT scanner model and associated reconstruction settings (CHU Milétrie, Poitiers, France). In addition, we expand the scope of the challenge by considering an additional task with the purpose of outcome prediction based on the PET/CT images. A clinically-relevant endpoint that can be leveraged for personalizing patient management at diagnosis will be considered: prediction of progression-free survival from diagnostic PET/CT images. By focusing on metabolic and morphological tissue properties respectively, PET and CT modalities include complementary and synergistic information for cancerous lesion segmentation as well as tumor characteristics relevant for patient outcome prediction, in addition to usual clinical variables (e.g., clinical stage, age, gender, treatment modality). Modern image analysis methods must be developed to best extract and leverage this information. The data used in this challenge is multi centric, including four centers in Canada (Vallières et al. 2017), one center in Switzerland (Castelli et al. 2017), and one center in France (Hatt et al. 2019; Legot et al. 2018) for a total of 335 patients with annotated primary tumors.


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