国际医学放射学杂志2026，Vol.49Issue(1)：39-43,89,6.DOI:10.19300/j.2026.L22231
基于MRI的人工智能模型对高级别胶质瘤和脑转移瘤分类诊断的效能及验证
Diagnostic performance and validation of an MRI-based artificial intelligence model for differentiating high-grade glioma from brain metastasis
摘要
Abstract
Objective To develop an artificial intelligence(AI)model based on contrast-enhanced T1-weighted(CE-T1WI)and T2 FLAIR MRI,and to validate and evaluate its diagnostic performance and clinical value in differentiating high-grade glioma(HGG)from brain metastasis.Methods A total of 272 patients with brain tumors confirmed by surgical pathology were retrospectively enrolled,including 143 cases of HGG and 129 cases of brain metastasis.Four radiologists with different levels of experience[two junior(2-3 years)and two mid-level(5-8 years)]were randomly assigned to two groups,AI-assisted test group with 2 radiologists+AI,and a non-AI control group with 2 radiologists only.All radiologists independently interpreted the MRI images of all patients in two rounds using a crossover design,including an AI-assisted test group and a non-AI control group,with a 3-week washout period between readings.Using pathology as the reference standard,diagnostic performance was compared using the DBMH method.The area under the receiver operating characteristic curve(AUC),sensitivity,specificity,and accuracy were calculated,and the performance of the AI model was compared with that of junior and mid-level radiologists.Results The AI model achieved a significantly higher AUC of 0.975(95%CI:0.956-0.993)for classification,with a sensitivity of 97.20%,specificity of 97.67%,and accuracy of 97.43%.For differentiating HGG from brain metastasis,the AUCs of the test group and control group were 0.934(95%CI:0.909-0.958)and 0.707(95%CI:0.645-0.768),respectively.Sensitivity was 98.08%versus 83.22%,specificity was 69.19%versus 52.13%,and accuracy was 84.38%versus 68.47%,respectively.Diagnostic performance in the AI-assisted test group was superior to that in the control group for both junior and mid-level radiologists.Conclusion The neural network-based AI model demonstrates excellent diagnostic performance in differentiating high-grade glioma from brain metastasis.It can improve the diagnostic accuracy of radiologists and provide valuable support for clinical diagnostic and therapeutic decision-making.关键词
脑转移瘤/高级别胶质瘤/磁共振成像/人工智能Key words
Brain malignant tumor/High grade glioma/Magnetic resonance imaging/Artificial intelligence分类
医药卫生引用本文复制引用
郑慧,赵倩茹,方慧,吕锟,张丹妮,曹鑫,鲍奕仿,耿道颖..基于MRI的人工智能模型对高级别胶质瘤和脑转移瘤分类诊断的效能及验证[J].国际医学放射学杂志,2026,49(1):39-43,89,6.基金项目
国家自然科学基金项目(82372048) （82372048）
上海市科学技术委员会项目(22TS1400900,23S31904100,24SF1904201) （22TS1400900,23S31904100,24SF1904201）
