Trial reportBrain and behavior2023
Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population.
Trial report in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
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Who cites it
4 citing papers in PubMed.
- Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population.Brain and behavior · 2023Trial
- Interpretable machine learning for cognitive impairment assessment: integration of clinical and radiomic white matter hyperintensities features.Journal of translational medicine · 2026Article
- Machine learning-based radiomics models for the prediction of metachronous liver metastases in patients with colorectal cancer: A multimodal study.Oncology letters · 2025Article
- Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease.Frontiers in neurology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aim to develop a radiomics model based on 3-dimensional (3D)-T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter.
methodsIn this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D-T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D-T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA).
resultsA total of 108 frontal lobe texture features were extracted from 3D-T1WI images. LASSO selected 58 radiomic features for the final model, including log-sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69-0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value.
conclusionsTextural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.
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