Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices

Boyu Chen; Ming Xu; Hongmei Yu; Jiachuan He; Yingmei Li; Dandan Song; Guo Guang Fan

doi:10.1186/s12967-023-04158-8

Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices

J Transl Med. 2023 May 8;21(1):310. doi: 10.1186/s12967-023-04158-8.

Authors

Boyu Chen¹, Ming Xu², Hongmei Yu³, Jiachuan He¹, Yingmei Li¹, Dandan Song¹, Guo Guang Fan⁴

Affiliations

¹ Department of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China.
² Shenyang University of Technology, No.111, Shenliao West Road, Shenyang, 110870, Liaoning, China.
³ Department of Neurology, The First Hospital of China Medical University, No. 155, Nanjing North Street, Shenyang, 110001, Liaoning, China.
⁴ Department of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China. fanguog@sina.com.

Abstract

Background: Cognitive dysfunction is the most common non-motor symptom in Parkinson's disease (PD), and timely detection of a slight cognitive decline is crucial for early treatment and prevention of dementia. This study aimed to build a machine learning model based on intra- and/or intervoxel metrics extracted from diffusion tensor imaging (DTI) to automatically classify PD patients without dementia into mild cognitive impairment (PD-MCI) and normal cognition (PD-NC) groups.

Methods: We enrolled PD patients without dementia (52 PD-NC and 68 PD-MCI subtypes) who were assigned to the training and test datasets in an 8:2 ratio. Four intravoxel metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), and two novel intervoxel metrics, local diffusion homogeneity (LDH) using Spearman's rank correlation coefficient (LDHs) and Kendall's coefficient concordance (LDHk), were extracted from the DTI data. Decision tree, random forest, and eXtreme gradient boosting (XGBoost) models based on individual and combined indices were built for classification, and model performance was assessed and compared via the area under the receiver operating characteristic curve (AUC). Finally, feature importance was evaluated using SHapley Additive exPlanation (SHAP) values.

Results: The XGBoost model based on a combination of the intra- and intervoxel indices achieved the best classification performance, with an accuracy of 91.67%, sensitivity of 92.86%, and AUC of 0.94 in the test dataset. SHAP analysis showed that the LDH of the brainstem and MD of the right cingulum (hippocampus) were important features.

Conclusions: More comprehensive information on white matter changes can be obtained by combining intra- and intervoxel DTI indices, improving classification accuracy. Furthermore, machine learning methods based on DTI indices can be used as alternatives for the automatic identification of PD-MCI at the individual level.

Keywords: Diffusion tensor imaging; Machine learning; Magnetic resonance imaging; Mild cognitive impairment; Parkinson’s disease.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cognitive Dysfunction* / complications
Cognitive Dysfunction* / diagnostic imaging
Decision Trees
Dementia*
Diffusion Tensor Imaging
Humans
Parkinson Disease* / complications
Parkinson Disease* / diagnostic imaging