Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis

World Neurosurg. 2024 Jun:186:204-218.e2. doi: 10.1016/j.wneu.2024.03.152. Epub 2024 Apr 3.

Abstract

Background: Classifying 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.

Methods: A 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.

Results: Fifty-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).

Conclusions: ML demonstrated excellent performance in classifying brain tumor images, with near-maximum area under the curves, sensitivity, and specificity.

Keywords: Brain tumors; Classification; Machine learning.

Publication types

  • Meta-Analysis
  • Systematic Review

MeSH terms

  • Brain Neoplasms* / classification
  • Brain Neoplasms* / diagnostic imaging
  • Brain Neoplasms* / pathology
  • Glioblastoma / classification
  • Glioblastoma / diagnostic imaging
  • Glioblastoma / pathology
  • Glioma / classification
  • Glioma / diagnostic imaging
  • Glioma / pathology
  • Humans
  • Machine Learning*
  • Sensitivity and Specificity