Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning

  • Lingli Li
  • Wenliang Fan
  • Jun Li
  • Quanlin Li
  • Jin Wang
  • Yang Fan
  • Tianhe Ye
  • Jialun Guo
  • Sen Li
  • Youpeng Zhang
  • Yongbiao Cheng
  • Yong Tang
  • Hanqing Zeng
  • Lian Yang
  • Zhaohui Zhu
Neuro
  • 34 Downloads

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.

Keywords

Venous erectile dysfunction Multimode magnetic resonance imaging VBM TBSS Machine-learning classification 

Abbreviations

AD

Axial diffusivity

BPRS

Brief Psychiatric Rating Scale

FA

Fractional anisotropy

GM

Grey matter

HAMA

Hamilton Anxiety Rating Scale

HAMD

Hamilton Depression Rating Scale

IIEF-5

International Index of Erectile Function

MD

Mean diffusivity

NIH-CPSI

National Institutes of Health Chronic Prostatitis Symptom Index

PED

Psychogenic ED

PEDT

Premature Ejaculation Diagnostic Tool

RD

Radial diffusivity

SAS

Self-Rating Anxiety Scale

SDS

Self-Rating Depression Scale

TBSS

Tract-based spatial statistics

VBM

Voxel-based morphometry

VED

Venous erectile dysfunction

WM

White matter

Notes

Acknowledgements

We would like to thank the three anonymous reviewers for their helpful comments on an earlier version of this manuscript. We thank all participants in this study.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Lian Yang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained by the Medical Ethics Committee of the Union Hospital.

Methodology

• prospective

• case-control study/diagnostic study

• performed at one institution

Supplementary material

330_2018_5365_MOESM1_ESM.doc (2.8 mb)
ESM 1 (DOC 2877 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Lingli Li
    • 1
    • 2
  • Wenliang Fan
    • 1
    • 2
  • Jun Li
    • 1
    • 2
  • Quanlin Li
    • 3
  • Jin Wang
    • 4
  • Yang Fan
    • 5
  • Tianhe Ye
    • 1
    • 2
  • Jialun Guo
    • 4
  • Sen Li
    • 4
  • Youpeng Zhang
    • 4
  • Yongbiao Cheng
    • 4
  • Yong Tang
    • 4
  • Hanqing Zeng
    • 4
  • Lian Yang
    • 1
    • 2
  • Zhaohui Zhu
    • 4
  1. 1.Department of Radiology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China
  2. 2.Hubei Key Laboratory of Molecular ImagingWuhan 430022China
  3. 3.State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  4. 4.Department of Urology, Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China
  5. 5.Advanced Application ChinaGE HealthcareWuhan 430022China

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