Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients

• Published in: BMC medical imaging Vol. 25; no. 1; pp. 227 - 11
• Main Authors: lian, Yandong, Xu, Yibin, Hu, Linlin, Wei, Yuguo, Wang, Zhaoge
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.07.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Aged; Algorithms; Alzheimer's disease; Artificial intelligence; Automation; Behavior disorders; Biomarkers; Brain; Brain - diagnostic imaging; Brain research; Cerebrospinal fluid; Data mining; Disease; Eye movements; Female; Group theory; Humans; Imaging; Instability; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medical imaging equipment; Medicine; Medicine & Public Health; Medicine, Preventive; Mental illness; Middle Aged; Movement disorders; Neurodegenerative diseases; Neuroimaging; Parkinson Disease - complications; Parkinson Disease - diagnostic imaging; Parkinson's disease; Patients; Performance evaluation; Posture; Prediction models; Preventive health services; Quantitative imaging and biomarkers; Radiology; Radiomics; Rapid eye movement sleep behavior disorder; Regression analysis; REM sleep; REM Sleep Behavior Disorder - diagnostic imaging; REM Sleep Behavior Disorder - etiology; Risk; Risk groups; ROC Curve; Sleep; Sleep disorders; Software; Stability; Substantia alba; Substantia grisea; Wavelet transforms; Taiwan; Biomarkers; Magnetic resonance imaging; Rapid eye movement sleep behavior disorder; Machine learning; Radiomics
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-025-01748-4

Background Traditional clinical diagnostic methods of rapid eye movement sleep behavior disorder (RBD) have certain limitations, especially in the early stages. This study aims to develop and validate an magnetic resonance imaging (MRI) radiomics-based machine learning classifier to accurately detect RBD patients with Parkinson’s disease (PD). Methods Data from 183 subjects, including 63 PD patients with RBD, sourced from the PPMI database were utilized in this study. The data were randomly divided into training (70%) and testing (30%) sets. Quantitative radiomic features of white matter, gray matter, and cerebrospinal fluid were extracted from whole-brain structural MRI images. Feature reduction was performed on the training set data to construct radiomics signatures. Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. The model exhibiting the best performance was selected, and receiver operating characteristic (ROC) curves were used to evaluate its performance in both the training and testing sets. Furthermore, based on the optimal cut-off value of the model, subjects were categorized into low- and high-risk groups, and differences in the actual number of RBD patients between the two sets were compared to assess the clinical effectiveness of the model. Results The radiomics signatures achieved areas under the curve (AUC) of 0.754 and 0.707 in the training and testing sets, respectively. Multi-factor logistic regression analysis revealed that postural instability was an independent predictor of PD-RBD. The random forest model, which integrated radiomics signatures with postural instability, demonstrated superior performance in predicting PD-RBD. Specifically, its AUCs in the training and testing sets were 0.917 and 0.882, with sensitivities of 0.933 and 0.889, and specificities of 0.786 and 0.722, respectively. Based on the optimal cut-off value of 0.3772, significant differences in the actual number of PD-RBD patients were observed between low-risk and high-risk groups in both the training and testing sets (P < 0.05). Conclusion MRI-based radiomic signatures have the potential to serve as biomarkers for PD-RBD. The random forest model, which integrates radiomic signatures with postural instability, and shows improved performance in identifying PD-RBD. This approach offers valuable insights for prognostic evaluation and preventive treatment strategies.