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

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Head and Neck Tumor Segmentation and Outcome Prediction (HECKTOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13626))

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Abstract

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 VokCow. 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.

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Authors and Affiliations

  1. Canon Medical Research Europe Ltd., Edinburgh, UK

    Ángel Víctor Juanco-Müller, Keith Goatman & Corné Hoogendoorn

  2. Heriot-Watt University, Edinburgh, UK

    Ángel Víctor Juanco-Müller & João F. C. Mota

Authors
  1. Ángel Víctor Juanco-Müller
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  2. João F. C. Mota
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  3. Keith Goatman
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  4. Corné Hoogendoorn
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Corresponding author

Correspondence to Ángel Víctor Juanco-Müller .

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Editors and Affiliations

  1. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Vincent Andrearczyk

  2. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Valentin Oreiller

  3. LaTIM, INSERM, University of Brest, Brest, France

    Mathieu Hatt

  4. HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Adrien Depeursinge

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Juanco-Müller, Á.V., Mota, J.F.C., Goatman, K., Hoogendoorn, C. (2023). Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022. Lecture Notes in Computer Science, vol 13626. Springer, Cham. https://doi.org/10.1007/978-3-031-27420-6_18

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  • DOI: https://doi.org/10.1007/978-3-031-27420-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27419-0

  • Online ISBN: 978-3-031-27420-6

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