ArticleCNS neuroscience & therapeutics2026
An Integrated QSM-Radiomics Nomogram With Clinical and Imaging Markers for Stratifying Cognitive Impairment in Hypertension.
Article in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Integrated QSM-Radiomics Nomogram With Clinical and Imaging Markers for Stratifying Cognitive Impairment in Hypertension.CNS neuroscience & therapeutics · 2026Article
- Multimodal MRI Radiomics and Machine Learning Identify Node-Level Structural and Functional Alterations in HIV-Associated Asymptomatic Neurocognitive Impairment.Neuropsychiatric disease and treatment · 2026Article
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13 authors.
Funding
Abstract
backgroundHypertensive cognitive impairment is associated with increased iron deposition in deep gray matter. This study aimed to evaluate the potential clinical application of quantitative susceptibility mapping (QSM)-based radiomics for stratifying cognitive impairment in hypertensive patients.
methodsWe prospectively enrolled 178 hypertensive patients who underwent QSM examination and categorized them into cognitive impairment group and normal cognition group. The workflow included: (1) Precise 3D segmentation and susceptibility quantification of the basal ganglia; (2) Initial radiomics feature extraction from the target regions, followed by optimal feature selection via variance thresholding, maximum relevance minimum redundancy (mRMR) algorithm, and LASSO regression; (3) Construction of a multiparametric model integrating radiomics scores and clinical risk factors. The model's performance was assessed by receiver operating characteristic (ROC) curve and its clinical utility for stratification was further evaluated using decision curve analysis (DCA).
resultsThe multi-region radiomics model demonstrated superior diagnostic performance in the training cohort (AUC = 0.812), significantly outperforming the QSM and WMH models (AUCs = 0.620 and 0.688, respectively). In the validation cohort, clinically meaningful improvements were observed (ΔAUC = 0.117 and 0.136, respectively). The combined model, which integrated Radscore, susceptibility values, WMH scores, age, and education level, achieved the highest discriminability in both the training (AUC = 0.860) and validation cohorts (AUC = 0.872). DCA further indicated that the nomogram derived from the combined model provided the greatest net clinical benefit for stratifying hypertensive cognitive impairment.
conclusionA nomogram integrating QSM-based radiomics with clinical and imaging markers accurately stratified hypertensive cognitive impairment, offering an objective tool for early risk assessment.
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