Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CT

Huynh, Bao Ngoc; Ren, Jintao; Groendahl, Aurora Rosvoll; Tomić, Oliver; Korreman, S.; Futsæther, Cecilia Marie

doi:10.1007/978-3-030-98253-9_30

Cited by 3 publications

(2 citation statements)

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“…In [27], Ren et al (team "Aarhus Oslo") team compared a conventional radiomics approach (although without tumor delineation, i.e., features were extracted from the whole bounding-box) and a deep learning approach in Task 2 only. Both used the provided bounding-box of PET and CT images as inputs, and in the case of the deep learning approach, an additional pre-processing step was applied to PET images in order to reduce the variability of images due to various centers based on a sin transform.…”

Section: Results: Reporting Of Challenge Outcomementioning

confidence: 99%

“…Individual participants' papers reporting their methods and results were submitted to the challenge organizers. Reviews were organized by the organizers and the papers of the participants are published in the LNCS challenges proceedings [60,1,52,56,59,12,53,62,21,32,43,37,54,6,67,16,9,48,49,58,40,51,17,46,65,39,33,45,27]. When participating in multiple tasks, participants could submit one or multiple papers.…”

Section: Introduction: Research Contextmentioning

confidence: 99%

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Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT

Andrearczyk

Oreiller

Jreige

et al. 2021

Lecture Notes in Computer Science

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This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021. The challenge is composed of three tasks related to the automatic analysis of PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the automatic segmentation of H&N primary Gross Tumor Volume (GTVt) in FDG-PET/CT images. Task 2 is the automatic prediction of Progression Free Survival (PFS) from the same FDG-PET/CT. Finally, Task 3 is the same as Task 2 with ground truth GTVt annotations provided to the participants. The data were collected from six centers for a total of 325 images, split into 224 training and 101 testing cases. The interest in the challenge was highlighted by the important participation with 103 registered teams and 448 result submissions. The best methods obtained a Dice Similarity Coefficient (DSC) of 0.7591 in the first task, and a Concordance index (C-index) of 0.7196 and 0.6978 in Tasks 2 and 3, respectively. In all tasks, simplicity of the approach was found to be key to ensure generalization performance. The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used. This potentially obviates the need for GTVt contouring, opening avenues for reproducible and large scale radiomics studies including thousands potential subjects. equal contribution equal contribution

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Section: Results: Reporting Of Challenge Outcomementioning

confidence: 99%