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Computed tomography-based radiomic markers are independent prognosticators of survival in advanced laryngeal cancer: a pilot study.

Authors :
Rajgor, Amarkumar Dhirajlal
Kui, Christopher
McQueen, Andrew
Cowley, Josh
Gillespie, Colin
Mill, Aileen
Rushton, Stephen
Obara, Boguslaw
Bigirumurame, Theophile
Kallas, Khaled
O'Hara, James
Aboagye, Eric
Hamilton, David Winston
Source :
Journal of Laryngology & Otology. Jun2024, Vol. 138 Issue 6, p685-691. 7p.
Publication Year :
2024

Abstract

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 computed tomography-based radiomic features that may predict survival and enhance prognostication. Methods: Pre-biopsy, contrast-enhanced computed tomography scans were assembled from a retrospective cohort (n = 72) with advanced laryngeal cancers (T3 and T4). The LIFEx software was used for radiomic feature extraction. Two features: shape compacity (irregularity of tumour volume) and grey-level zone length matrix – grey-level non-uniformity (tumour heterogeneity) were selected via least absolute shrinkage and selection operator-based Cox regression and explored for prognostic potential. Results: A greater shape compacity (hazard ratio 2.89) and grey-level zone length matrix – grey-level non-uniformity (hazard ratio 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-centre prospective study is necessary for further exploration. Integrated radiomic models may refine the treatment of advanced laryngeal cancers. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00222151
Volume :
138
Issue :
6
Database :
Academic Search Index
Journal :
Journal of Laryngology & Otology
Publication Type :
Academic Journal
Accession number :
177227753
Full Text :
https://doi.org/10.1017/S0022215123002372