ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Development and Validation of a Predictive Nomogram for Cerebral White Matter Hyperintensities: Insights from a Comprehensive Clinical and Laboratory Analysis
Provisionally accepted
- 1Baoding No 1 Central Hospital, Baoding, China
- 2Affiliated Hospital of Hebei University, Baoding, China
- 3Xiongan Xuanwu Hospital, Baoding, China
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Background: White matter hyperintensities (WMH) are key imaging markers of cerebral small vessel disease (CSVD), associated with cognitive decline and stroke risk. An accurate predictive model is needed for early risk assessment. Methods: This retrospective study utilized data from 587 patients undergoing cranial magnetic resonance imaging (MRI) at Hebei University's Neurology Department. A predictive model for WMH was developed using a combination of clinical and laboratory parameters through Least Absolute Shrinkage and Selection Operator (LASSO) regression and binary logistic regression analysis. The model's performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUC-ROC), calibration plots, and Decision Curve Analysis (DCA). Results: Key predictors included age, history of stroke, hypertension, triiodothyronine levels, albumin-globulin ratio, and homocysteine. The nomogram achieved an AUC of 0.783 (95% CI: 0.738–0.829) in the training cohort and 0.762 (95% CI: 0.690–0.834) in the validation cohort. Calibration and DCA confirmed the model's clinical applicability. Conclusion: This study presents a validated nomogram for predicting WMH, integrating clinical and biochemical markers. The model demonstrated robust predictive accuracy and potential for early risk stratification. Future studies should focus on multi-center validation and expanded risk factor inclusion.
Keywords: Cerebral small vessel disease (CSVD), white matter hyperintensities (WMH), PredictiveModeling, Neuroimaging, LASSO regression, nomogram, Risk Assessment
Received: 12 Jun 2025; Accepted: 29 Oct 2025.
Copyright: © 2025 Xu, Wang, Li, Hu, Chen, Jiang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ning Li, li8743@163.com
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