• Silva SantanaLaísLSchool of Medicine, University of São Paulo, São Paulo, Brazil., Borges Camargo DinizJordanaJDepartment of Neurology, Neurological Institute of Goiânia, Goiânia, Brazil., Luisa Mothé Glioche Gasparri, Alessandra Buccaran Canto, Sávio Batista Dos Reis, Iuri Santana Neville Ribeiro, Gadelha FigueiredoEbervalEDepartment of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil., and Paulo Mota TellesJoãoJDepartment of Neurology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. Electronic address: joao.telles@fm.usp.br..
• School of Medicine, University of São Paulo, São Paulo, Brazil.
• World Neurosurg. 2024 Jun 1; 186: 204218.e2204-218.e2.

BackgroundClassifying brain tumors accurately is crucial for treatment and prognosis. Machine learning (ML) shows great promise in improving tumor classification accuracy. This study evaluates ML algorithms for differentiating various brain tumor types.MethodsA systematic review and meta-analysis were conducted, searching PubMed, Embase, and Web of Science up to March 14, 2023. Studies that only investigated image segmentation accuracy or brain tumor detection instead of classification were excluded. We extracted binary diagnostic accuracy data, constructing contingency tables to derive sensitivity and specificity.ResultsFifty-one studies were included. The pooled area under the curve for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% confidence interval [CI]: 0.98-1.00) and 0.89, respectively. The pooled sensitivity and specificity for benign versus malignant tumors were 0.90 (95% CI: 0.85-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. The pooled sensitivity and specificity for low-grade versus high-grade gliomas were 0.99 (95% CI: 0.97-1.00) and 0.94, (95% CI: 0.79-0.99), respectively. Primary versus metastatic tumor identification yields sensitivity and specificity of 0.89, (95% CI: 0.83-0.93) and 0.87 (95% CI: 0.82-0.91), correspondingly. The differentiation of gliomas from pituitary tumors yielded the highest results among primary brain tumor classifications: sensitivity of 0.99 (95% CI: 0.99-1.00) and specificity of 0.99 (95% CI: 0.98-1.00).ConclusionsML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.Copyright © 2024 Elsevier Inc. All rights reserved.

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