Predictive Value of Cerebral Perfusion and Guanine Nucleotide-Binding Protein, Alpha-Stimulating Activity Polypeptide in Ischemic White Matter Lesions: A Machine Learning Approach

Ning Yu^1,2*Shuai Ma^3,4Zongkai Wu⁵Zhijie Dou^1,2Shengxian Jiao⁵Yajing Li¹Hebo Wang⁵Xiaoxuan Zhang^1*

• ¹Neurology Department, Affiliated Hospital of Chengde Medical University, Chengde, China
• ²Hebei Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde, Hebei Province, China
• ³Institute of Traditional Chinese Medicine, Chengde Medical University, Chengde, Shanghai Municipality, China
• ⁴Hebei Province Key Laboratory of Traditional Chinese Medicine Research and Development, Chengde, China
• ⁵Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei Province, China

Purpose: To assess the predictive value of guanine nucleotide-binding protein, alpha-stimulating activity polypeptide (GNAS) and cerebral perfusion in various vascular regions for the severity of ischemic white matter lesions (WMLs). Methods: Patients hospitalized at the Neurology Department of the Affiliated Hospital of Chengde Medical University between April and November 2023 were evaluated for ischemic cerebral WMLs using magnetic resonance imaging. In this retrospective cohort study, patients were classified into two groups: mild and severe, based on Fazekas scores. White matter perfusion was assessed using image segmentation of arterial spin labeling sequence images. Predictive variables were identified via This is a provisional file, not the final typeset article machine learning (ML). GNAS levels in peripheral blood were measured to explore their association with WML severity. Results: Among 85 patients (43 mild [24 males and 19 females], 42 severe [27 males and 15 females]), significant differences were observed in age (64.00±8.47 years vs. 68.38±10.85 years, p=0.041), cerebral atrophy (37.2% vs. 71.4%, p=0.002), and history of hypertension (41.7% vs. 77.0%, p=0.002). Corpus callosum perfusion was lower in the severe group (35.84±6.34 vs. 31.73±8.60 ml/[min·100 g], p=0.037). ML yielded 77.27% model accuracy. Although no significant difference in GNAS levels was observed (p = 0.375), a significant difference was noted in the Fazekas scores (p < 0.001). Conclusion: In patients with ischemic WMLs, factors such as age, sex, history of cerebral infarction, GNAS levels, and specific perfusion metrics are predictive of WML progression. Advanced imaging and ML improve detection. GNAS levels correlated with Fazekas scores, indicating their downregulation in the hypoperfused white matter.