HEad and neCK TumOR segmentation and outcome prediction in PET/CT images
Creators: name: Andrearczyk, Vincent
name: Oreiller, Valentin
name: Vallières, Martin
name: Fuller, Clifton Dave
name: Naser, Mohamed
name: Wahid, Kareem
name: Mohamed, Abdallah
name: Ruan, Su
name: Decazes, Pierre
name: Vera, Pierre
name: Zaidi, Habib
name: Tanadini-Lang, Stephanie
name: Rest, Catherine Cheze-Le
name: Visvikis, Dimitris
name: Elhalawani, Hesham
name: Castelli, Joël
name: Jreige, Mario
name: Dias-Correia, Ricardo
name: Boughdad, Sarah
name: Prior, John O.
name: Hatt, Mathieu
name: Depeursinge, Adrien
Publisher: name: Zenodo
Year: 2022
Subjects: Head and Neck cancer
automatic segmentation
radiomics
PET/CT
multimodal
deep learning
MICCAI
Challenge

description: Head and Neck (H&amp;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. In 2020, we organized the HECKTOR challenge to offer an opportunity for participants working on 3D segmentation algorithms to develop automatic bi-modal approaches for the segmentation of H&amp;N primary tumors (GTVt) in PET/CT scans, focusing on oropharyngeal cancers. <br> 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) as well as adding a second task (split into two subtasks: with or without reference contours provided), namely the prediction of Progression-Free Survival (PFS). For the 2022 edition, we propose expanding the scope of the challenge even further by:<br> i) adding the task of nodal Gross Tumor Volumes (GTVn) segmentation (which also carries information on the outcome via the presence and amount of metastatic lesions).<br> ii) adding more data with an approximative total of 1385 cases from three new centers (versus 325 in 2021). The test data of 2021 will be moved to the training set of 2022, whereas new data from new centers will be split between training and test sets.<br> iii) not providing bounding boxes. Only the entire PET/CT image will be provided to the challengers for fully automatic algorithms that can predict from entire images.<br> iv) not providing test ground truth tumor delineations for outcome prediction, as we learned from 2021’s results that these delineations were not necessary to achieve best prediction performance. <br> 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 potentially relevant for patient outcome prediction, in addition to usual clinical variables (e.g. age, gender, treatment modality, etc.). Modern image analysis methods must be developed and more importantly, rigorously evaluated, in order to extract and leverage this information. The data used in this challenge is multi-centric (9 centers in total), including four centers in Canada (Vallières et al. 2017), two centers in Switzerland (Castelli et al. 2017),(Bogowicz et al. 2017), two centers in France (Hatt et al. 2019; Legot et al. 2018), and one center in USA for a<br> total of 1385 patients with annotated GTVt and GTVn.