Prediction of Cognitive Decline in Parkinson's Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems.

Autor: Hosseinzadeh M; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V5E 3J7, Canada.; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran 14115111, Iran., Gorji A; Neuroscience and Artificial Intelligence Research Group (NAIRG), Student Research Committee, Hamadan University of Medical Sciences, Hamadan 6517838736, Iran., Fathi Jouzdani A; Neuroscience and Artificial Intelligence Research Group (NAIRG), Student Research Committee, Hamadan University of Medical Sciences, Hamadan 6517838736, Iran., Rezaeijo SM; Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz 6135715794, Iran., Rahmim A; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada.; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada., Salmanpour MR; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC V5E 3J7, Canada.; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2023 May 10; Vol. 13 (10). Date of Electronic Publication: 2023 May 10.
DOI: 10.3390/diagnostics13101691
Abstrakt: Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs).
Methods: 297 patients were selected from the Parkinson's Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing.
Results: For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively.
Conclusions: We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.
Databáze: MEDLINE
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