Evaluación de Resultados en Ictus Mediante RM Multiparamétrica: Integración de la Localización del Infarto, la...

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.

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.

Retrospective.

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.

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).

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.

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.

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).

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.

2.