Habitat analysis of iron deposition in the basal ganglia for diagnosing cognitive impairment in chronic kidney disease: evidence from a case-control study

Abstract

Background: Chronic kidney disease induces alterations in the heterogeneity of iron deposition within the basal ganglia. Quantitative analysis of the heterogeneity of iron deposition within the basal ganglia may be valuable for diagnosing chronic kidney disease-related cognitive impairment.

Methods: In this prospective observational cohort study, quantitative susceptibility mapping (QSM) was performed in chronic kidney disease patients. Susceptibility values of each nucleus within the basal ganglia were measured. Radiomic features were extracted from habitats of the basal ganglia on QSM images. Habitat-based models for diagnosing cognitive impairment were constructed using the random forest algorithm. Logistic regression was employed to build the clinical model and the combined model. The performance of each model was evaluated by the receiver operating characteristic (ROC) analysis.

Results: A total of 146 patients (mean age, 51 ± 13 years; 92 male) were included, of which 79 had cognitive impairment. The two habitats-based model achieved an area under the curve of 0.926 (95% CI 0.842-1.000) on the test set, the highest among all prediction models. The two-habitat maps indicated that chronic kidney disease had two distinct patterns of impact on iron deposition in the basal ganglia region. The capability of the two habitats-based model to identify chronic kidney disease-related cognitive impairment was significantly superior to that of the susceptibility values measured in various nuclei (all p < 0.05).

Conclusions: This study innovatively applied a habitat-based quantitative analysis technique to QSM, successfully constructing a model that accurately diagnoses chronic kidney disease-related cognitive impairment.

Trial registration: This study was approved by the Beijing Friendship Hospital Ethics Board (ClinicalTrials.gov Identifier: NCTO5137470) and conducted in accordance with the Declaration of Helsinki ethical standards.

Keywords: Basal ganglia; Chronic kidney disease; Cognitive impairment; Quantitative susceptibility mapping; Radiomics.

Fig. 1

Summary of patients with CI and non-CI recruitment and exclusions. CI, cognitive impairment; CKD, chronic kidney disease; MoCA, Montreal Cognitive Assessment; QSM, quantitative susceptibility mapping

Fig. 2

This workflow provides a comprehensive approach to utilizing QSM and habitat-based radiomics for identifying CKD-related CI. First, obtain QSM images of CKD patients. Next, manually segment the basal ganglia on the QSM images and generate multiple habitats. Then, extract radiomic features from these segments. Subsequently, perform feature selection to construct models and evaluate their diagnostic performance. The ultimate goal is to develop a model that can accurately identify CKD-related CI for clinical application. CI, cognitive impairment; CKD, chronic kidney disease; QSM, quantitative susceptibility mapping

Fig. 3

(A) Calinski-Harabasz score and Silhouette coefficient plots used to determine the optimal number of habitats. (B) Two-habitat maps of CKD patients with and without CI, respectively. CI, cognitive impairment; CKD, chronic kidney disease; QSM, quantitative susceptibility mapping

Fig. 4

Beeswarm plot of the SHAP analysis of the two habitats-based model. SHAP, SHapley Additive exPlanations

Fig. 5

ROC curves of various CKD-related CI prediction models in the training set (A) and test set (B). The DCA of the Two habitats-based model, clinical model, and combined model in the test set (C). CI, cognitive impairment; CKD, chronic kidney disease; DCA, decision curve analysis