Journal of magnetic resonance imaging : JMRI · 2026
Outcome Assessment in Stroke Using Multiparametric MRI: Integrating Infarct Location, Radiomics, and Global Brain Frailty.
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Li JiaNan, Li JianRui, Huang LiJun, Wang LiYing, Xu LuSiYing, Zhao SiJia, Xiao Lulu, Cao ZeHong, Liu XiaoYu, Pan Liang, Chen Jie, Zhai Duchang, Cai Wu, Yin XinDao, Xing Wei, Shi Feng, Zhu WuSheng, Zhang QiRui, Lu GuangMing, Cheng XiaoQing
Аннотация
BACKGROUND: Accurate assessment of 90-day functional outcomes after anterior circulation large vessel occlusion (LVO) stroke remains challenging. Conventional models relying on a single data dimension have limited assessment power, suggesting that a multidimensional integration strategy could enhance evaluations.
PURPOSE: To develop and validate an interpretable machine learning model that integrates radiomics, infarct location, brain frailty, and clinical variables for assessing 90-day functional outcomes in LVO stroke.
STUDY TYPE: Retrospective.
POPULATION: 1051 patients with anterior circulation LVO stroke (mean age 63 ± 13 years; 722 males) from five centers (2018-2023). Eight hundred and seventy-five patients from four centers formed the training (n = 612) and internal validation (n = 263) cohorts, while 176 from the fifth center comprised the external validation cohort.
FIELD STRENGTH/SEQUENCE: T1-weighted spin-echo imaging (T1WI), T2-weighted spin-echo imaging (T2WI), T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging, and diffusion-weighted echo-planar imaging (DWI).
ASSESSMENT: Infarct volume and radiomic features were extracted from DWI. Infarct location was assessed using the Alberta Stroke Program Early CT Score. Brain frailty was evaluated using cortical/subcortical atrophy, white matter hyperintensity (WMH), and old infarcts. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection.
STATISTICAL TESTS: Chi-square, Fisher's exact, t-test, Mann-Whitney U, area under the receiver operating characteristic curve (AUC), DeLong test, decision curve analysis, calibration curves, sensitivity, specificity, positive predictive value, negative predictive value, F1 score. Significance level p < 0.05.
RESULTS: The fused model outperformed all single-dimension models (ΔAUC = 0.12-0.22), achieving AUCs of 0.87 (training), 0.84 (internal validation), and 0.86 (external validation). The fused model achieved a sensitivity and a specificity of 0.80 in the external validation cohort. Features with the highest mean absolute Shapley Additive Explanations (SHAP) values included lentiform nucleus lesion burden (SHAP = 0.083), WMH (SHAP = 0.080), and lesion burden in the M6 region (posterior middle cerebral artery territory; SHAP = 0.061).
DATA CONCLUSION: Integration of infarct location, brain frailty, radiomics, and clinical features improved the 90-day outcome assessment in anterior circulation LVO stroke, providing an interpretable tool for personalized prognosis.
TECHNICAL EFFICACY STAGE: 2.
PURPOSE: To develop and validate an interpretable machine learning model that integrates radiomics, infarct location, brain frailty, and clinical variables for assessing 90-day functional outcomes in LVO stroke.
STUDY TYPE: Retrospective.
POPULATION: 1051 patients with anterior circulation LVO stroke (mean age 63 ± 13 years; 722 males) from five centers (2018-2023). Eight hundred and seventy-five patients from four centers formed the training (n = 612) and internal validation (n = 263) cohorts, while 176 from the fifth center comprised the external validation cohort.
FIELD STRENGTH/SEQUENCE: T1-weighted spin-echo imaging (T1WI), T2-weighted spin-echo imaging (T2WI), T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging, and diffusion-weighted echo-planar imaging (DWI).
ASSESSMENT: Infarct volume and radiomic features were extracted from DWI. Infarct location was assessed using the Alberta Stroke Program Early CT Score. Brain frailty was evaluated using cortical/subcortical atrophy, white matter hyperintensity (WMH), and old infarcts. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection.
STATISTICAL TESTS: Chi-square, Fisher's exact, t-test, Mann-Whitney U, area under the receiver operating characteristic curve (AUC), DeLong test, decision curve analysis, calibration curves, sensitivity, specificity, positive predictive value, negative predictive value, F1 score. Significance level p < 0.05.
RESULTS: The fused model outperformed all single-dimension models (ΔAUC = 0.12-0.22), achieving AUCs of 0.87 (training), 0.84 (internal validation), and 0.86 (external validation). The fused model achieved a sensitivity and a specificity of 0.80 in the external validation cohort. Features with the highest mean absolute Shapley Additive Explanations (SHAP) values included lentiform nucleus lesion burden (SHAP = 0.083), WMH (SHAP = 0.080), and lesion burden in the M6 region (posterior middle cerebral artery territory; SHAP = 0.061).
DATA CONCLUSION: Integration of infarct location, brain frailty, radiomics, and clinical features improved the 90-day outcome assessment in anterior circulation LVO stroke, providing an interpretable tool for personalized prognosis.
TECHNICAL EFFICACY STAGE: 2.