Loading…

An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson’s disease

Cognitive impairment in Parkinson’s disease (PD) severely affects patients’ prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve...

Full description

Saved in:
Bibliographic Details
Published in:NPJ Parkinson's Disease 2023-08, Vol.9 (1), p.127-127, Article 127
Main Authors: Park, Chae Jung, Eom, Jihwan, Park, Ki Sung, Park, Yae Won, Chung, Seok Jong, Kim, Yun Joong, Ahn, Sung Soo, Kim, Jinna, Lee, Phil Hyu, Sohn, Young Ho, Lee, Seung-Koo
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cognitive impairment in Parkinson’s disease (PD) severely affects patients’ prognosis, and early detection of patients at high risk of dementia conversion is important for establishing treatment strategies. We aimed to investigate whether multiparametric MRI radiomics from basal ganglia can improve the prediction of dementia development in PD when integrated with clinical profiles. In this retrospective study, 262 patients with newly diagnosed PD (June 2008–July 2017, follow-up >5 years) were included. MRI radiomic features ( n  = 1284) were extracted from bilateral caudate and putamen. Two models were developed to predict dementia development: (1) a clinical model—age, disease duration, and cognitive composite scores, and (2) a combined clinical and radiomics model. The area under the receiver operating characteristic curve (AUC) were calculated for each model. The models’ interpretabilities were studied. Among total 262 PD patients (mean age, 68 years ± 8 [standard deviation]; 134 men), 51 (30.4%), and 24 (25.5%) patients developed dementia within 5 years of PD diagnosis in the training ( n  = 168) and test sets ( n  = 94), respectively. The combined model achieved superior predictive performance compared to the clinical model in training (AUCs 0.928 vs. 0.894, P  = 0.284) and test set (AUCs 0.889 vs. 0.722, P  = 0.016). The cognitive composite scores of the frontal/executive function domain contributed most to predicting dementia. Radiomics derived from the caudate were also highly associated with cognitive decline. Multiparametric MRI radiomics may have an incremental prognostic value when integrated with clinical profiles to predict future cognitive decline in PD.
ISSN:2373-8057
2373-8057
DOI:10.1038/s41531-023-00566-1