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CT-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: A pilot study.

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.

Publication metadata

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


DOI: 10.1017/S0022215123002372

PubMed id: 38095096


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