PDF(Pubmed)
Abstract：
UNASSIGNED: To assess the diagnostic accuracy of machine learning (ML)-based radiomics for predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma.
UNASSIGNED: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to 1 September 2023, was conducted to collect all articles investigating the diagnostic performance of ML for the prediction of IDH mutations in gliomas. Two reviewers independently screened all papers for eligibility. Methodological quality and risk of bias were assessed using the METhodological RadiomICs Score and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. The pooled sensitivity, specificity, and 95% confidence intervals were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained.
UNASSIGNED: In total, 14 original articles assessing 1740 patients with gliomas were included. The AUC of ML for predicting IDH mutation was 0.90 (0.87-0.92). The pooled sensitivity, specificity, and diagnostic odds ratio were 0.83 (0.71-0.90), 0.84 (0.74-0.90), and 25 (12,50) respectively. In subgroup analyses, modeling methods, glioma grade, and the combination of magnetic resonance imaging and clinical features affected the diagnostic performance in predicting IDH mutations in gliomas.
UNASSIGNED: ML-based radiomics demonstrated excellent diagnostic performance in predicting IDH mutations in gliomas. Factors influencing the diagnosis included the modeling methods employed, glioma grade, and whether the model incorporated clinical features.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#myprospero, PROSPERO registry (CRD 42023395444).
UNASSIGNED: A systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from inception to 1 September 2023, was conducted to collect all articles investigating the diagnostic performance of ML for the prediction of IDH mutations in gliomas. Two reviewers independently screened all papers for eligibility. Methodological quality and risk of bias were assessed using the METhodological RadiomICs Score and Quality Assessment of Diagnostic Accuracy Studies-2, respectively. The pooled sensitivity, specificity, and 95% confidence intervals were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained.
UNASSIGNED: In total, 14 original articles assessing 1740 patients with gliomas were included. The AUC of ML for predicting IDH mutation was 0.90 (0.87-0.92). The pooled sensitivity, specificity, and diagnostic odds ratio were 0.83 (0.71-0.90), 0.84 (0.74-0.90), and 25 (12,50) respectively. In subgroup analyses, modeling methods, glioma grade, and the combination of magnetic resonance imaging and clinical features affected the diagnostic performance in predicting IDH mutations in gliomas.
UNASSIGNED: ML-based radiomics demonstrated excellent diagnostic performance in predicting IDH mutations in gliomas. Factors influencing the diagnosis included the modeling methods employed, glioma grade, and whether the model incorporated clinical features.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/#myprospero, PROSPERO registry (CRD 42023395444).
摘要：
■评估基于机器学习（ML）的影像组学预测神经胶质瘤患者异柠檬酸脱氢酶（IDH）突变的诊断准确性。
■对PubMed的系统搜索，WebofScience,Embase,从开始到2023年9月1日的Cochrane图书馆收集了所有研究ML在预测神经胶质瘤中IDH突变方面的诊断性能的文章.两名审稿人独立筛选了所有论文的资格。分别使用METhodologyRadiomICs评分和诊断准确性研究质量评估2评估方法学质量和偏倚风险。汇集的敏感性，特异性，计算95%的置信区间，并获得受试者工作特征曲线下面积(AUC)。
■总共，包括14篇评估1740例胶质瘤患者的原始文章。预测IDH突变的ML的AUC为0.90(0.87-0.92)。汇集的敏感性，特异性，诊断比值比为0.83（0.71-0.90），0.84（0.74-0.90），和25（12,50）。在亚组分析中，建模方法，胶质瘤分级,磁共振成像和临床特征的结合影响了预测胶质瘤中IDH突变的诊断能力。
■基于ML的影像组学在预测神经胶质瘤中的IDH突变方面表现出优异的诊断性能。影响诊断的因素包括采用的建模方法，胶质瘤分级,以及该模型是否包含临床特征。
■https://www.crd.约克。AC.uk/PROSPERO/#myprospro,PROSPERO注册表（CRD42023395444）。
■对PubMed的系统搜索，WebofScience,Embase,从开始到2023年9月1日的Cochrane图书馆收集了所有研究ML在预测神经胶质瘤中IDH突变方面的诊断性能的文章.两名审稿人独立筛选了所有论文的资格。分别使用METhodologyRadiomICs评分和诊断准确性研究质量评估2评估方法学质量和偏倚风险。汇集的敏感性，特异性，计算95%的置信区间，并获得受试者工作特征曲线下面积(AUC)。
■总共，包括14篇评估1740例胶质瘤患者的原始文章。预测IDH突变的ML的AUC为0.90(0.87-0.92)。汇集的敏感性，特异性，诊断比值比为0.83（0.71-0.90），0.84（0.74-0.90），和25（12,50）。在亚组分析中，建模方法，胶质瘤分级,磁共振成像和临床特征的结合影响了预测胶质瘤中IDH突变的诊断能力。
■基于ML的影像组学在预测神经胶质瘤中的IDH突变方面表现出优异的诊断性能。影响诊断的因素包括采用的建模方法，胶质瘤分级,以及该模型是否包含临床特征。
■https://www.crd.约克。AC.uk/PROSPERO/#myprospro,PROSPERO注册表（CRD42023395444）。
