Artificial Intelligence–Assisted Evaluation of the Spatial Relationship between Brain Arteriovenous Malformations and the Corticospinal Tract to Predict Postsurgical Motor Defects - Jiao, Y.; Zhang, J.; Yang, X.; Zhan, T.; Wu, Z.; Li, Y.; Zhao, S.; Li, H.; Weng, J.; Huo, R.; Wang, J.; Xu, H.; et al. (American Journal of Neuroradiology, 2022)
BACKGROUND AND PURPOSE: Preoperative evaluation of brain AVMs is crucial for the selection of surgical candidates. Our goal was to use artificial intelligence to predict postsurgical motor defects in patients with brain AVMs involving motor-related areas.MATERIALS AND METHODS: Eighty-three patients who underwent microsurgical resection of brain AVMs involving motor-related areas were retrospectively reviewed. Four artificial intelligence–based indicators were calculated with artificial intelligence on TOF-MRA and DTI, including FN5mm/50mm (the proportion of fiber numbers within 5–50mm from the lesion border), FN10mm/50mm (the same but within 10–50mm), FP5mm/50mm (the proportion of fiber voxel points within 5–50mm from the lesion border), and FP10mm/50mm (the same but within 10–50mm). The association between the variables and long-term postsurgical motor defects was analyzed using univariate and multivariate analyses. Least absolute shrinkage and selection operator regression with the Pearson correlation coefficient was used to select the optimal features to develop the machine learning model to predict postsurgical motor defects. The area under the curve was calculated to evaluate the predictive performance.RESULTS: In patients with and without postsurgical motor defects, the mean FN5mm/50mm, FN10mm/50mm, FP5mm/50mm, and FP10mm/50mm were 0.24 (SD, 0.24) and 0.03 (SD, 0.06), 0.37 (SD, 0.27) and 0.06 (SD, 0.08), 0.06 (SD, 0.10) and 0.01 (SD, 0.02), and 0.10 (SD, 0.12) and 0.02 (SD, 0.05), respectively. Univariate and multivariate logistic analyses identified FN10mm/50mm as an independent risk factor for long-term postsurgical motor defects (P = .002). FN10mm/50mm achieved a mean area under the curve of 0.86 (SD, 0.08). The mean area under the curve of the machine learning model consisting of FN10mm/50mm, diffuseness, and the Spetzler-Martin score was 0.88 (SD, 0.07).CONCLUSIONS: The artificial intelligence–based indicator, FN10mm/50mm, can reflect the lesion-fiber spatial relationship and act as a dominant predictor for postsurgical motor defects in patients with brain AVMs involving motor-related areas.AIartificial intelligenceAUCarea under the curveBAVMbrain AVMCSTcorticospinal tractEuDXEuler Delta CrossingFNfiber numberFPproportion of fiber voxel pointsHDVLhemorrhagic presentation, diffuseness, deep venous drainage, and lesion-to-eloquence distanceLASSOleast absolute shrinkage and selection operatorLCDlesion-to-corticospinal tract distanceLRlogistic regressionM-AVMbrain AVM involving motor-related areasMDsmotor defectsMLmachine learningROCreceiver operating characteristicS-MSpetzler-Martin gradingXGBoostExtreme Gradient Boosting