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Lookup NU author(s): David Hamilton, Amar RajgorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s), 2025.Objectives: Radiomics refers to converting medical images into high-quality quantitative data. This review examines applications of radiomics in vestibular schwannomas and future considerations for translation into clinical practice. Methods: The review was pre-registered on PROSPERO (ID: CRD42024579319). A comprehensive systematic review-informed search of OVID MEDLINE, EMBASE and Global Health online databases was undertaken. Keywords "acoustic neuroma"OR "vestibular schwannoma"OR "cerebellopontine angle tumour"OR "cerebellopontine tumour"OR "head and neck cancer"were combined with "radiomic"OR "signature"OR "machine learning"OR "artificial intelligence". Results: The studies (n=6) are categorised into two groups: Radiomics for Preoperative Decision-making (n=1) and Radiomics for Treatment Outcomes (n=5). Radiomic features are significantly associated with clinical outcomes. Radiomics-based predictive models are superior to expert vision. Conclusion: Radiomics has potential for improving multiple aspects of vestibular schwannoma care; however, lack of studies inhibits firm conclusions. Prospective studies are required to progress this field.
Author(s): Gill T, Hamilton DW, Rajgor AD
Publication type: Review
Publication status: Published
Journal: Journal of Laryngology and Otology
Year: 2025
Volume: 139
Issue: 8
Pages: 647-654
Print publication date: 01/08/2025
Online publication date: 27/03/2025
Acceptance date: 25/02/2025
ISSN (print): 0022-2151
ISSN (electronic): 1748-5460
Publisher: Cambridge University Press
URL: https://doi.org/10.1017/S0022215125000258
DOI: 10.1017/S0022215125000258
