Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning
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...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Egyptian Journal of Neurosurgery |
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| Online Access: | https://doi.org/10.1186/s41984-025-00386-w |
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| author | Mohammad Amin Habibi Reza Omid Shafaq Asgarzade Sadaf Derakhshandeh Ali Soltani Farsani Zohreh Tajabadi |
| author_facet | Mohammad Amin Habibi Reza Omid Shafaq Asgarzade Sadaf Derakhshandeh Ali Soltani Farsani Zohreh Tajabadi |
| author_sort | Mohammad Amin Habibi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c511d61460f943deb503739a4cd32330 |
| institution | Kabale University |
| issn | 2520-8225 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Egyptian Journal of Neurosurgery |
| spelling | doaj-art-c511d61460f943deb503739a4cd323302025-02-09T12:25:11ZengSpringerOpenEgyptian Journal of Neurosurgery2520-82252025-02-0140111110.1186/s41984-025-00386-wDistinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learningMohammad Amin Habibi0Reza Omid1Shafaq Asgarzade2Sadaf Derakhshandeh3Ali Soltani Farsani4Zohreh Tajabadi5Department of Neurosurgery, Shariati Hospital, Tehran University of Medical ScienceCenter of Orthopedic Trans-Disiplinary Applied Research (COTAR), School of Medicine, Tehran University of Medical ScienceSchool of Medicine, Tabriz University of Medical SciencesSchool of Medicine, Tehran Medical Sciences, Islamic Azad UniversitySchool of Medicine, Tehran University of Medical SciencesDigestive Disease Research Institute, Shariati Hospital, Tehran University of Medical ScienceAbstract 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.https://doi.org/10.1186/s41984-025-00386-wDeep learningRadiomicsGBMGliomaMRIArtificial intelligence |
| spellingShingle | Mohammad Amin Habibi Reza Omid Shafaq Asgarzade Sadaf Derakhshandeh Ali Soltani Farsani Zohreh Tajabadi Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning Egyptian Journal of Neurosurgery Deep learning Radiomics GBM Glioma MRI Artificial intelligence |
| title | Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning |
| title_full | Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning |
| title_fullStr | Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning |
| title_full_unstemmed | Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning |
| title_short | Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning |
| title_sort | distinguishing glioblastoma from brain metastasis a systematic review and meta analysis on the performance of machine learning |
| topic | Deep learning Radiomics GBM Glioma MRI Artificial intelligence |
| url | https://doi.org/10.1186/s41984-025-00386-w |
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