Lingli Li1,2, Wenliang Fan1,2, Jun Li1,2, Quanlin Li3, Jin Wang4, Yang Fan5, Tianhe Ye1,2, Jialun Guo4, Sen Li4, Youpeng Zhang4, Yongbiao Cheng4, Yong Tang4, Hanqing Zeng4, Lian Yang6,7, Zhaohui Zhu8. 1. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. 2. Hubei Key Laboratory of Molecular Imaging, Wuhan 430022, China. 3. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China. 4. Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. 5. Advanced Application China, GE Healthcare, Wuhan 430022, China. 6. Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. yanglian2003@163.com. 7. Hubei Key Laboratory of Molecular Imaging, Wuhan 430022, China. yanglian2003@163.com. 8. Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. zhuzhaohui316@163.com.
Abstract
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification. METHODS: 45 VED patients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VED patients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VED patients from healthy controls. RESULTS: Compared to healthy control subjects, VED patients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VED patients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%. CONCLUSIONS: Cortical volume and white matter (WM) microstructural changes were observed in VED patients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VED patients and healthy control subjects, as shown by machine learning analyses. KEY POINTS: • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VED patients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VED patients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI's clinical use.
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a machine learning classification. METHODS: 45 VEDpatients and 50 healthy controls were included. Voxel-based morphometry (VBM), tract-based spatial statistics (TBSS) and correlation analyses of VEDpatients and clinical variables were performed. The machine learning classification method was adopted to confirm its effectiveness in distinguishing VEDpatients from healthy controls. RESULTS: Compared to healthy control subjects, VEDpatients showed significantly decreased cortical volumes in the left postcentral gyrus and precentral gyrus, while only the right middle temporal gyrus showed a significant increase in cortical volume. Increased axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) values were observed in widespread brain regions. Certain regions of these alterations related to VEDpatients showed significant correlations with clinical symptoms and disorder durations. Machine learning analyses discriminated patients from controls with overall accuracy 96.7%, sensitivity 93.3% and specificity 99.0%. CONCLUSIONS: Cortical volume and white matter (WM) microstructural changes were observed in VEDpatients, and showed significant correlations with clinical symptoms and dysfunction durations. Various DTI-derived indices of some brain regions could be regarded as reliable discriminating features between VEDpatients and healthy control subjects, as shown by machine learning analyses. KEY POINTS: • Multimodal magnetic resonance imaging helps clinicians to assess patients with VED. • VEDpatients show cerebral structural alterations related to their clinical symptoms. • Machine learning analyses discriminated VEDpatients from controls with an excellent performance. • Machine learning classification provided a preliminary demonstration of DTI's clinical use.
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