Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information

Conference item

The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over the past decade and one common cause of treatment failure is recurrence. In this paper, we built Cox proportional hazard (CoxPH) models that predict the recurrence free survival (RFS) of oropharyngeal HNC patients. Our models utilise both clinical information and multimodal radiomics features extracted from tumour regions in Computed Tomography (CT) and Positron Emission Tomography (PET). Furthermore, we were one of the first studies to explore the impact of segmentation accuracy on the predictive power of the extracted radiomics features, through under- and over-segmentation study. Our models were trained using the HEad and neCK TumOR (HECKTOR) challenge data, and the best performing model achieved a concordance index (C-index) of 0.74 for the model utilising clinical information and multimodal CT and PET radiomics features, which compares favourably with the model that only used clinical information (C-index of 0.67). Our under- and over-segmentation study confirms that segmentation accuracy affects radiomics extraction, however, it affects PET and CT differently.

Authors: Furukawa, M; McGowan, DR; Papiez, BW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: Proceedings of the 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)
• Journal: ISBI
• Pages: 1-5
• Publication date: 2024-08-22
• Acceptance date: 2024-01-16
• Event title: 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024)
• Event location: Athens, Greece
• Event website: https://biomedicalimaging.org/2024/
• Event start date: 2024-05-27
• Event end date: 2024-05-30
• DOI: 10.1109/ISBI56570.2024.10635413
• EISSN: 1945-8452
• ISSN: 1945-7928
• EISBN: 9798350313338
• ISBN: 9798350313345
• Language: English
• Keywords: radiomics; neck; image segmentation; accuracy; computed tomography; feature extraction; data models; clinical diagnosis; head and neck cancer; medical imaging; PET/CT; multimodal