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

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 (AUC) for glioblastoma versus lymphoma and low-grade versus high-grade gliomas were 0.99 (95% 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). CONCLUSION Machine learning demonstrated excellent performance in classifying brain tumor images, with near-maximum AUCs, sensitivity and specificity.