AUTHOR=Wijethilake Navodini , MacCormac Oscar , Vercauteren Tom , Shapey Jonathan 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1131013

DOI=10.3389/fonc.2023.1131013

ISSN=2234-943X

ABSTRACT=Extra-axial brain tumours are extra-cerebral tumours and are usually benign. The choice of treatment for extra-axial tumours is often dependent on the growth of the tumour and imaging plays a significant role in monitoring growth and clinical decision making. This motivates the investigation of imaging biomarkers for these tumours that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase and Medline were searched from 1st of January 2000 to 7th of 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 a total of 42 studies comprising of: 22 studies (50%) of patients with Meningioma; 17 studies (38.6%) of patients with Pituitary Tumours; 3 studies (6.8%) of patients with Vestibular Schwannomas and 2 studies with (4.5%) with Solitary Fibrous Tumours. The included studies were explicitly and narratively analyzed according to tumour type and the used imaging tool. 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 used machine learning (3/44). 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.