Kevin H Leung1,2, Steven P Rowe3, Martin G Pomper4,3, Yong Du3. 1. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA. kleung8@jhmi.edu. 2. The Russell H. Morgan, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. kleung8@jhmi.edu. 3. The Russell H. Morgan, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. 4. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA.
Abstract
BACKGROUND: Diagnosis of Parkinson's disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [123I]ioflupane (DaTscan). Deep learning and ensemble methods have recently shown promise in medical image analysis. Therefore, this study aimed to develop a three-stage, deep learning, ensemble approach for prognosis in patients with PD. METHODS: Retrospective data of 198 patients with PD were retrieved from the Parkinson's Progression Markers Initiative database and randomly partitioned into the training, validation, and test sets with 118, 40, and 40 patients, respectively. The first and second stages of the approach extracted features from DaTscan and clinical measures of motor symptoms, respectively. The third stage trained an ensemble of deep neural networks on different subsets of the extracted features to predict patient outcome 4 years after initial baseline screening. The approach was evaluated by assessing mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson's correlation coefficient, and bias between the predicted and observed motor outcome scores. The approach was compared to individual networks given different data subsets as inputs. RESULTS: The ensemble approach yielded a MAPE of 18.36%, MAE of 4.70, a Pearson's correlation coefficient of 0.84, and had no significant bias indicating accurate outcome prediction. The approach outperformed individual networks not given DaTscan imaging or clinical measures of motor symptoms as inputs, respectively. CONCLUSION: The approach showed promise for longitudinal prognostication in PD and demonstrated the synergy of imaging and non-imaging information for the prediction task.
BACKGROUND: Diagnosis of Parkinson's disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [123I]ioflupane (DaTscan). Deep learning and ensemble methods have recently shown promise in medical image analysis. Therefore, this study aimed to develop a three-stage, deep learning, ensemble approach for prognosis in patients with PD. METHODS: Retrospective data of 198 patients with PD were retrieved from the Parkinson's Progression Markers Initiative database and randomly partitioned into the training, validation, and test sets with 118, 40, and 40 patients, respectively. The first and second stages of the approach extracted features from DaTscan and clinical measures of motor symptoms, respectively. The third stage trained an ensemble of deep neural networks on different subsets of the extracted features to predict patient outcome 4 years after initial baseline screening. The approach was evaluated by assessing mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson's correlation coefficient, and bias between the predicted and observed motor outcome scores. The approach was compared to individual networks given different data subsets as inputs. RESULTS: The ensemble approach yielded a MAPE of 18.36%, MAE of 4.70, a Pearson's correlation coefficient of 0.84, and had no significant bias indicating accurate outcome prediction. The approach outperformed individual networks not given DaTscan imaging or clinical measures of motor symptoms as inputs, respectively. CONCLUSION: The approach showed promise for longitudinal prognostication in PD and demonstrated the synergy of imaging and non-imaging information for the prediction task.
Entities:
Keywords:
DaTscan; Deep learning; Ensemble learning; Parkinson’s disease; Prognosis