Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning - Mohammad Amin Habibi; Reza Omid; Shafaq Asgarzade; Sadaf Derakhshandeh; Ali Soltani Farsani; Zohreh Tajabadi (Egyptian Journal of Neurosurgery)
Abstract Background The discrimination of glioblastoma and solitary metastasis brain tumor is challenging. Up now, several conventional and advanced imaging modalities were used for distinguishing between these tumors with different success rates. We systematically reviewed the studies reported the performance of machine learning (ML) algorithms for accurately discrimination of these two entities. Method The search was conducted from inception to 1 June, 2023, in PubMed/Medline, Embase, Scopus, and Web of Science to find out the studies investigated the performance of ML-based algorithm for differentiation of glioblastoma and metastatic brain tumor. Results This study included 28 studies comprising a total of 2,860 patients. The meta-analysis model results revealed a pooled sensitivity and specificity estimate of 0.83 [0.80–0.86] and 0.87 [0.83–0.90], respectively, indicating a commendable overall diagnostic accuracy across all the studies. ResNet50 and ResNet50-LSTM have shown promising results with single-study sensitivities of up to 88.9% and 88.2%, respectively. Furthermore, the integration of CNNs and RNNs has demonstrated improved performance compared to standalone models in a significant portion of the studies. The ROC curve area was 0.90, indicating high discriminative ability. The positive likelihood ratio was 6.2, and the negative likelihood ratio was 0.20, providing helpful information on how test results modified pretest probability. Conclusion ML applied to routine neuroimaging shows high diagnostic potential for glioblastoma detection. While more research is needed before clinical deployment, preliminary results are encouraging.