Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

Journal: Journal of imaging informatics in medicine
Published Date: Jul 7, 2025

Abstract

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates among patients. Identifying biomarkers of PD progression could improve clinical monitoring and management. Radiomics, which facilitates data extraction from imaging for use in machine learning models, offers a promising approach to this challenge. This study investigated the use of multi-modality imaging, combining conventional magnetic resonance imaging (MRI) and dopamine transporter single photon emission computed tomography (DAT-SPECT), to predict motor progression in PD. Motor progression was measured by changes in the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor subscale scores. Radiomic features were selected from the midbrain region in MRI and caudate nucleus, putamen, and ventral striatum in DAT-SPECT. Patients were stratified into fast progression vs. slow progression based on change in MDS-UPDRS in follow-up. Various feature selection methods and machine learning classifiers were evaluated for each modality, and the best-performing models were combined into an ensemble. On the internal test set, the ensemble model, which integrated clinical information, T1WI, T2WI and DAT-SPECT achieved a ROC AUC of 0.93 (95% CI: 0.80-1.00), PR AUC of 0.88 (95%CI 0.61-1.00), accuracy of 0.85 (95% CI: 0.70-0.89), sensitivity of 0.72 (95% CI: 0.43-1.00), and specificity of 0.92 (95% CI: 0.77-1.00). On the external test set, the ensemble model outperformed single-modality models with a ROC AUC of 0.77 (95% CI: 0.53-0.93), PR AUC of 0.79 (95% CI: 0.56-0.95), accuracy of 0.68 (95% CI: 0.50-0.86), sensitivity of 0.53 (95% CI: 0.27-0.82), and specificity of 0.82 (95% CI: 0.55-1.00). In conclusion, this study developed an imaging-based model to identify baseline characteristics predictive of disease progression in PD patients. The findings highlight the strength of using multiple imaging modalities and integrating imaging data with clinical information to enhance the prediction of motor progression in PD.

Authors

  • Yuwei Dai
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Centre for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Centre of Zhejiang University, Hangzhou, China; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Maliha Imami
    Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Rong Hu
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Linmei Zhao
    Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA.
  • Daniel C Kargilis
    Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, 21287, United States of America.
  • Helen Zhang
    The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
  • Guangdi Yu
    Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.
  • Wei-Hua Liao
    From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (R.W.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology. Massachusetts General Hospital, Boston, Mass (K.C.); Warren Alpert Medical School at Brown University, Providence, RI (H.X.B., K.H., T.M.L.T., J.W.C., I.P.); Department of Radiology, Yongzhou Central Hospital, Yongzhou, China (L.B.S.); Department of Radiology, Changde Second People's Hospital, Changde, China (J.M.); Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China (X.L.J.); Department of Radiology, Loudi Central Hospital, Loudi, China (Q.H.Z.); Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China (P.F.H.); Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China (Y.H.L.); Department of Radiology, Yiyang City Center Hospital, Yiyang, China (F.X.F.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (R.Y.H.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (R.S.); and Department of Radiology, The First Hospital of Changsha, Changsha, China (Q.Z.Y.).
  • Zhicheng Jiao
  • Chengzhang Zhu
    School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
  • Li Yang
    Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.

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