Development and Validation of a Plasma FAM19A5 and MRI-Based Radiomics Model for Prediction of Parkinson's Disease and Parkinson's Disease With Depression

Xue-Ning Li; Da-Peng Hao; Mei-Jie Qu; Meng Zhang; An-Bang Ma; Xu-Dong Pan; Ai-Jun Ma

doi:10.3389/fnins.2021.795539

Development and Validation of a Plasma FAM19A5 and MRI-Based Radiomics Model for Prediction of Parkinson's Disease and Parkinson's Disease With Depression

Front Neurosci. 2021 Dec 17:15:795539. doi: 10.3389/fnins.2021.795539. eCollection 2021.

Authors

Xue-Ning Li¹, Da-Peng Hao², Mei-Jie Qu¹, Meng Zhang¹, An-Bang Ma³, Xu-Dong Pan^{1

4}, Ai-Jun Ma^{1

4}

Affiliations

¹ Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
² Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
³ Shanghai Xunshi Technology Co., Ltd., Shanghai, China.
⁴ Institute of Cerebrovascular, The Affiliated Hospital of Qingdao University, Qingdao, China.

Abstract

Background: Prediction and early diagnosis of Parkinson's disease (PD) and Parkinson's disease with depression (PDD) are essential for the clinical management of PD. Objectives: The present study aimed to develop a plasma Family with sequence similarity 19, member A5 (FAM19A5) and MRI-based radiomics nomogram to predict PD and PDD. Methods: The study involved 176 PD patients and 181 healthy controls (HC). Sandwich enzyme-linked immunosorbent assay (ELISA) was used to measure FAM19A5 concentration in the plasma samples collected from all participants. For enrolled subjects, MRI data were collected from 164 individuals (82 in the PD group and 82 in the HC group). The bilateral amygdala, head of the caudate nucleus, putamen, and substantia nigra, and red nucleus were manually labeled on the MR images. Radiomics features of the labeled regions were extracted. Further, machine learning methods were applied to shrink the feature size and build a predictive radiomics signature. The resulting radiomics signature was combined with plasma FAM19A5 concentration and other risk factors to establish logistic regression models for the prediction of PD and PDD. Results: The plasma FAM19A5 levels (2.456 ± 0.517) were recorded to be significantly higher in the PD group as compared to the HC group (2.23 ± 0.457) (P < 0.001). Importantly, the plasma FAM19A5 levels were also significantly higher in the PDD subgroup (2.577 ± 0.408) as compared to the non-depressive subgroup (2.406 ± 0.549) (P = 0.045 < 0.05). The model based on the combination of plasma FAM19A5 and radiomics signature showed excellent predictive validity for PD and PDD, with AUCs of 0.913 (95% CI: 0.861-0.955) and 0.937 (95% CI: 0.845-0.970), respectively. Conclusion: Altogether, the present study reported the development of nomograms incorporating radiomics signature, plasma FAM19A5, and clinical risk factors, which might serve as potential tools for early prediction of PD and PDD in clinical settings.

Keywords: Parkinson’s disease; Parkinson’s disease with depression; machine learning; plasma FAM19A5; radiomics.