Evidence map›Paper›PMID 37158918›Full record

ArticleJournal of translational medicine2023

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

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 5 pooled it
6.8field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 5 syntheses or guidelines pooled it, 33 citations in OpenAlex.

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  17. Sparse Deep Neural Network for Encoding and Decoding the Structural Connectome.IEEE journal of translational engineering in health and medicine · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Boyu ChenDepartment of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China.ORCID 0000-0002-0960-2329
Ming XuShenyang University of Technology, No.111, Shenliao West Road, Shenyang, 110870, Liaoning, China.
Hongmei YuDepartment of Neurology, The First Hospital of China Medical University, No. 155, Nanjing North Street, Shenyang, 110001, Liaoning, China.
Jiachuan HeDepartment of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China.
Yingmei LiDepartment of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China.
Dandan SongDepartment of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China.
Guo Guang FanDepartment of Radiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, 110001, Liaoning, China. fanguog@sina.com.
First Hospital of China Medical University · CNShenyang University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCognitive 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.

methodsWe 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.

resultsThe 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.

conclusionsMore 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.

Indexed as

Cognitive DysfunctionDementiaParkinson DiseaseDecision TreesDiffusion Tensor ImagingHumansDiffusion tensor imagingMachine learningMagnetic resonance imagingMild cognitive impairmentParkinson’s disease

Identifiers

PMID37158918
PMCPMC10165759
OpenAlexW4375954918

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.