Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole‐brain white matter

Abstract

This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). Methods: PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T 1-weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. Results: Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600.

Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60‐Month Follow‐Up Study

“…And radiomic features usually could be divided into four categories: intensity, shape, texture (gray‐level co‐occurrence matrix [GLCM], gray‐level run‐length matrix [GLRLM], and gray‐level size zone matrix [GLSZM]), and wavelet texture. It has been successfully applied to provide accurate diagnosis and evaluation of tumors, 19 psychiatric disorders, 20 and neurodegenerative diseases 21 …”

Artificial Intelligence in Translational Medicine

“…Similarly, similar procedures, extracting specific features from available data allowing the development of ML based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [268] is possible to predict the progression of the disorder employing serum cytokines [269], MRI [270], and walking tests [271], to estimate the state of PD, employing longitudinal data [272], to rate the main synthomps (resting tremor and bradykinesia) [273], to produce a correct diagnosis from EEG analysis [274,275] and from voice dataset [276,277], only for reporting some relevant works.…”

Artificial Intelligence in Translational Medicine

“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”