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Lookup NU author(s): Amar Rajgor, Dr Christopher Kui, Josh Cowley, Dr Colin GillespieORCiD, Professor Aileen MillORCiD, Professor Stephen Rushton, Professor Boguslaw ObaraORCiD, Dr Theophile Bigirumurame, Dr Khaled Kallas, Joseph O'Hara, David Hamilton
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2023 Cambridge University Press. All rights reserved.Objective: Advanced laryngeal cancers are clinically complex; there is a paucity of modern decision-making models to guide tumour-specific management. This pilot study aims to identify CT-based radiomic features that may predict survival and enhance prognostication. Methods: Pre-biopsy, contrast-enhanced CT scans were assembled from a retrospective cohort (n=72) with advanced laryngeal cancers (T3-T4). The LifeX software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and GLZLM_GLNU (tumour heterogeneity) were selected via LASSO-Cox regression and explored for prognostic potential. Results: A greater shape compacity (HR 2.89) and GLZLM_GLNU (HR 1.64) were significantly associated with worse 5-year disease-specific survival (p<0.05). Cox regression models yielded a superior C-index when incorporating radiomic features (0.759) versus clinicopathological variables alone (0.655). Conclusions: Two radiomic features were identified as independent prognostic biomarkers. A multi-center prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers.
Author(s): Rajgor AD, Kui C, McQueen A, Cowley J, Gillespie C, Mill A, Rushton S, Obara B, Bigirumurame T, Kallas K, O'Hara J, Aboagye E, Hamilton DW
Publication type: Article
Publication status: Published
Journal: Journal of Laryngology and Otology
Year: 2023
Pages: epub ahead of print
Online publication date: 14/12/2023
Acceptance date: 02/04/2018
Date deposited: 19/02/2024
ISSN (print): 0022-2151
ISSN (electronic): 1748-5460
Publisher: Cambridge University Press
URL: https://doi.org/10.1017/S0022215123002372
DOI: 10.1017/S0022215123002372
PubMed id: 38095096
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