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

Angel Victor Juanco-Muller*, João F. C. Mota, Keith Goatman, Corné Hoogendoorn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
10 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationHead and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2022
EditorsVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
Number of pages14
ISBN (Electronic)9783031274206
ISBN (Print)9783031274190
Publication statusPublished - 18 Mar 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'Toward Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients'. Together they form a unique fingerprint.

Cite this