Diagnostic performance and validation of an MRI-based artificial intelligence model for differentiating high-grade glioma from brain metastasis
ZHENG Hui
ZHAO Qianru
FANG Hui
LYU Kun
ZHANG Danni
CAO Xin
BAO Yifang
GENG Daoying
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.
Keywords：Brain malignant tumorHigh grade gliomaMagnetic resonance imagingArtificial intelligence
Publication Date：2026-01-15
Online Publishing Date：2026-02-02(First online date of this platform, not the publication date of the document)
Pages：6( 39-43,89 )