Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.
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 pat...
| Published in: | European Radiology Vol. 28; no. 9; pp. 3789 - 3801 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Sep2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131115228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131115228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Sep2018 vid: 28 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131115228 131115228 NLM29600478 10.1007/s00330-018-5365-7 NLM29600478 131115228 ppf: 3789 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning. aug: au: Li, Lingli Fan, Wenliang Li, Jun Li, Quanlin Wang, Jin Fan, Yang Ye, Tianhe Guo, Jialun Li, Sen Zhang, Youpeng Cheng, Yongbiao Tang, Yong Zeng, Hanqing Yang, Lian Zhu, Zhaohui affil: Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China sug: subj: Brain Pathology Magnetic Resonance Imaging Methods Brain Mapping Methods Brain Diseases Erectile Dysfunction Diagnosis Brain Diagnostic Imaging Sensitivity and Specificity Brain Diseases Pathology Erectile Dysfunction Complications Reproducibility of Results Male Brain Diseases Complications Adult Scales Adult: 19-44 years Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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