{"title":"使用基于mri的人工智能预测儿童髓母细胞瘤的分子亚型：一项系统综述和荟萃分析。","authors":"Jiaying Liu, Zhenzhuang Zou, Yunfei He, Zhenfeng Guo, Changwei Yi, Bo Huang","doi":"10.1007/s00234-025-03759-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This meta-analysis aims to assess the diagnostic performance of artificial intelligence (AI) based on magnetic resonance imaging (MRI) in detecting molecular subtypes of pediatric medulloblastoma (MB) in children.</p><p><strong>Methods: </strong>A thorough review of the literature was performed using PubMed, Embase, and Web of Science to locate pertinent studies released prior to October 2024. Selected studies focused on the diagnostic performance of AI based on MRI in detecting molecular subtypes of pediatric MB. A bivariate random-effects model was used to calculate pooled sensitivity and specificity, both with 95% confidence intervals (CI). Study heterogeneity was assessed using I<sup>2</sup> statistics.</p><p><strong>Results: </strong>Among the 540 studies determined, eight studies (involving 1195 patients) were included. For the wingless (WNT), the combined sensitivity, specificity, and receiver operating characteristic curve (AUC) based on MRI were 0.73 (95% CI: 0.61-0.83, I<sup>2</sup> = 19%), 0.94 (95% CI: 0.79-0.99, I<sup>2</sup> = 93%), and 0.80 (95% CI: 0.77-0.83), respectively. For the sonic hedgehog (SHH), the combined sensitivity, specificity, and AUC were 0.64 (95% CI: 0.51-0.75, I<sup>2</sup> = 69%), 0.84 (95% CI: 0.80-0.88, I<sup>2</sup> = 54%), and 0.85 (95% CI: 0.81-0.88), respectively. For Group 3 (G3), the combined sensitivity, specificity, and AUC were 0.89 (95% CI: 0.52-0.98, I<sup>2</sup> = 82%), 0.70 (95% CI: 0.62-0.77, I<sup>2</sup> = 44%), and 0.88 (95% CI: 0.84-0.90), respectively. For Group 4 (G4), the combined sensitivity, specificity, and AUC were 0.77 (95% CI: 0.64-0.87, I<sup>2</sup> = 54%), 0.91 (95% CI: 0.68-0.98, I<sup>2</sup> = 80%), and 0.86 (95% CI: 0.83-0.89), respectively.</p><p><strong>Conclusions: </strong>MRI-based artificial intelligence shows high diagnostic performance in detecting molecular subtypes of pediatric MB. However, all included studies employed retrospective designs, which may introduce potential biases. More researches using external validation datasets are needed to confirm the results and assess their clinical applicability.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting molecular subtypes of pediatric medulloblastoma using MRI-based artificial intelligence: A systematic review and meta-analysis.\",\"authors\":\"Jiaying Liu, Zhenzhuang Zou, Yunfei He, Zhenfeng Guo, Changwei Yi, Bo Huang\",\"doi\":\"10.1007/s00234-025-03759-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>This meta-analysis aims to assess the diagnostic performance of artificial intelligence (AI) based on magnetic resonance imaging (MRI) in detecting molecular subtypes of pediatric medulloblastoma (MB) in children.</p><p><strong>Methods: </strong>A thorough review of the literature was performed using PubMed, Embase, and Web of Science to locate pertinent studies released prior to October 2024. Selected studies focused on the diagnostic performance of AI based on MRI in detecting molecular subtypes of pediatric MB. A bivariate random-effects model was used to calculate pooled sensitivity and specificity, both with 95% confidence intervals (CI). Study heterogeneity was assessed using I<sup>2</sup> statistics.</p><p><strong>Results: </strong>Among the 540 studies determined, eight studies (involving 1195 patients) were included. For the wingless (WNT), the combined sensitivity, specificity, and receiver operating characteristic curve (AUC) based on MRI were 0.73 (95% CI: 0.61-0.83, I<sup>2</sup> = 19%), 0.94 (95% CI: 0.79-0.99, I<sup>2</sup> = 93%), and 0.80 (95% CI: 0.77-0.83), respectively. For the sonic hedgehog (SHH), the combined sensitivity, specificity, and AUC were 0.64 (95% CI: 0.51-0.75, I<sup>2</sup> = 69%), 0.84 (95% CI: 0.80-0.88, I<sup>2</sup> = 54%), and 0.85 (95% CI: 0.81-0.88), respectively. For Group 3 (G3), the combined sensitivity, specificity, and AUC were 0.89 (95% CI: 0.52-0.98, I<sup>2</sup> = 82%), 0.70 (95% CI: 0.62-0.77, I<sup>2</sup> = 44%), and 0.88 (95% CI: 0.84-0.90), respectively. For Group 4 (G4), the combined sensitivity, specificity, and AUC were 0.77 (95% CI: 0.64-0.87, I<sup>2</sup> = 54%), 0.91 (95% CI: 0.68-0.98, I<sup>2</sup> = 80%), and 0.86 (95% CI: 0.83-0.89), respectively.</p><p><strong>Conclusions: </strong>MRI-based artificial intelligence shows high diagnostic performance in detecting molecular subtypes of pediatric MB. However, all included studies employed retrospective designs, which may introduce potential biases. More researches using external validation datasets are needed to confirm the results and assess their clinical applicability.</p>\",\"PeriodicalId\":19422,\"journal\":{\"name\":\"Neuroradiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroradiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00234-025-03759-y\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroradiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00234-025-03759-y","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Predicting molecular subtypes of pediatric medulloblastoma using MRI-based artificial intelligence: A systematic review and meta-analysis.
Background: This meta-analysis aims to assess the diagnostic performance of artificial intelligence (AI) based on magnetic resonance imaging (MRI) in detecting molecular subtypes of pediatric medulloblastoma (MB) in children.
Methods: A thorough review of the literature was performed using PubMed, Embase, and Web of Science to locate pertinent studies released prior to October 2024. Selected studies focused on the diagnostic performance of AI based on MRI in detecting molecular subtypes of pediatric MB. A bivariate random-effects model was used to calculate pooled sensitivity and specificity, both with 95% confidence intervals (CI). Study heterogeneity was assessed using I2 statistics.
Results: Among the 540 studies determined, eight studies (involving 1195 patients) were included. For the wingless (WNT), the combined sensitivity, specificity, and receiver operating characteristic curve (AUC) based on MRI were 0.73 (95% CI: 0.61-0.83, I2 = 19%), 0.94 (95% CI: 0.79-0.99, I2 = 93%), and 0.80 (95% CI: 0.77-0.83), respectively. For the sonic hedgehog (SHH), the combined sensitivity, specificity, and AUC were 0.64 (95% CI: 0.51-0.75, I2 = 69%), 0.84 (95% CI: 0.80-0.88, I2 = 54%), and 0.85 (95% CI: 0.81-0.88), respectively. For Group 3 (G3), the combined sensitivity, specificity, and AUC were 0.89 (95% CI: 0.52-0.98, I2 = 82%), 0.70 (95% CI: 0.62-0.77, I2 = 44%), and 0.88 (95% CI: 0.84-0.90), respectively. For Group 4 (G4), the combined sensitivity, specificity, and AUC were 0.77 (95% CI: 0.64-0.87, I2 = 54%), 0.91 (95% CI: 0.68-0.98, I2 = 80%), and 0.86 (95% CI: 0.83-0.89), respectively.
Conclusions: MRI-based artificial intelligence shows high diagnostic performance in detecting molecular subtypes of pediatric MB. However, all included studies employed retrospective designs, which may introduce potential biases. More researches using external validation datasets are needed to confirm the results and assess their clinical applicability.
期刊介绍：
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.
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