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

Authors
 Chae Jung Park  ;  Jihwan Eom  ;  Ki Sung Park  ;  Yae Won Park  ;  Seok Jong Chung  ;  Yun Joong Kim  ;  Sung Soo Ahn  ;  Jinna Kim  ;  Phil Hyu Lee  ;  Young Ho Sohn  ;  Seung-Koo Lee 
Citation
 NPJ PARKINSONS DISEASE, Vol.9(1) : 127, 2023-08 
Journal Title
NPJ PARKINSONS DISEASE
Issue Date
2023-08
Abstract
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.

© 2023. Springer Nature Limited.
Files in This Item:
T202305216.pdf Download
DOI
10.1038/s41531-023-66-1
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Yun Joong(김윤중) ORCID logo https://orcid.org/0000-0002-2956-1552
Kim, Jinna(김진아) ORCID logo https://orcid.org/0000-0002-9978-4356
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Park, Chae Jung(박채정) ORCID logo https://orcid.org/0000-0002-5567-8658
Sohn, Young Ho(손영호) ORCID logo https://orcid.org/0000-0001-6533-2610
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Phil Hyu(이필휴) ORCID logo https://orcid.org/0000-0001-9931-8462
Chung, Seok Jong(정석종) ORCID logo https://orcid.org/0000-0001-6086-3199
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/196380
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