Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

Frontiers in Oncology

Oncology

Tom Vercauteren¹  Navodini Wijethilake¹  Oscar MacCormac²  Jonathan Shapey² 

[1] School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom;School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom;Department of Neurosurgery, King’s College Hospital NHS Foundation Trust, London, United Kingdom;

关键词: extra-axial;    intracranial;    biomarker;    marker;    imaging;    growth;    tumor neoplasms;   

DOI  :  10.3389/fonc.2023.1131013

 received in 2022-12-24, accepted in 2023-03-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity.Systematic Review Registration: PROSPERO, CRD42022306922

【 授权许可】

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Copyright © 2023 Wijethilake, MacCormac, Vercauteren and Shapey