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Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients

arXiv 2304.08106 ↗First posted 17 Apr 2023eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

With nearly one million new cases diagnosed worldwide in 2020, head & neck cancer is a deadly and common malignity. There are challenges to decision making and treatment of such cancer, due to lesions in multiple locations and outcome variability between patients. Therefore, automated segmentation and prognosis estimation approaches can help ensure each patient gets the most effective treatment. This paper presents a framework to perform these functions on arbitrary field of view (FoV) PET and CT registered scans, thus approaching tasks 1 and 2 of the HECKTOR 2022 challenge as team textttVokCow. The method consists of three stages: localization, segmentation and survival prediction. First, the scans with arbitrary FoV are cropped to the head and neck region and a u-shaped convolutional neural network (CNN) is trained to segment the region of interest. Then, using the obtained regions, another CNN is combined with a support vector machine classifier to obtain the semantic segmentation of the tumours, which results in an aggregated Dice score of 0.57 in task 1. Finally, survival prediction is approached with an ensemble of Weibull accelerated failure times model and deep learning methods. In addition to patient health record data, we explore whether processing graphs of image patches centred at the tumours via graph convolutions can improve the prognostic predictions. A concordance index of 0.64 was achieved in the test set, ranking 6th in the challenge leaderboard for this task.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 17 Apr 2023.

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References · 14

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  12. 2022Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Imagescited by 29
2 more
  1. 2021Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Imagescited by 20
  2. 2022An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Datacited by 13

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