Predicting molecular subtypes of pediatric medulloblastoma using MRI-based artificial intelligence: A systematic review and meta-analysis.

IF 2.6 3区医学 Q2 CLINICAL NEUROLOGY

Neuroradiology Pub Date : 2025-09-12 DOI:10.1007/s00234-025-03759-y

Jiaying Liu, Zhenzhuang Zou, Yunfei He, Zhenfeng Guo, Changwei Yi, Bo Huang

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引用次数: 0

Abstract

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.

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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。

背景：本荟萃分析旨在评估基于磁共振成像（MRI）的人工智能（AI）在检测儿童髓母细胞瘤（MB）分子亚型中的诊断性能。方法：通过PubMed、Embase和Web of Science对2024年10月之前发表的相关研究进行全面的文献回顾。选定的研究集中于基于MRI的人工智能在检测儿童MB分子亚型方面的诊断性能。采用双变量随机效应模型计算合并敏感性和特异性，均为95%置信区间（CI）。采用I2统计量评估研究异质性。结果：在540项研究中，纳入8项研究（涉及1195例患者）。对于无翼（WNT），基于MRI的综合敏感性、特异性和受者工作特征曲线（AUC）分别为0.73 （95% CI: 0.61-0.83, I2 = 19%）、0.94 （95% CI: 0.79-0.99, I2 = 93%）和0.80 （95% CI: 0.77-0.83）。对于音猬（SHH），综合敏感性、特异性和AUC分别为0.64 （95% CI: 0.51-0.75, I2 = 69%）、0.84 （95% CI: 0.80-0.88, I2 = 54%）和0.85 （95% CI: 0.81-0.88）。3组（G3）的综合敏感性、特异性和AUC分别为0.89 （95% CI: 0.52-0.98, I2 = 82%）、0.70 （95% CI: 0.62-0.77, I2 = 44%）和0.88 （95% CI: 0.84-0.90）。对于第4组（G4），综合敏感性、特异性和AUC分别为0.77 （95% CI: 0.64-0.87, I2 = 54%）、0.91 （95% CI: 0.68-0.98, I2 = 80%）和0.86 （95% CI: 0.83-0.89）。结论：基于mri的人工智能在检测儿童MB分子亚型方面表现出较高的诊断性能。然而，所有纳入的研究均采用回顾性设计，这可能会引入潜在的偏差。需要更多使用外部验证数据集的研究来确认结果并评估其临床适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neuroradiology 医学-核医学

CiteScore

5.30

自引率

3.60%

发文量

214

审稿时长

4-8 weeks

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.