Comparison of a SegNet-based Algorithm Estimating Epifascial Fibrosis

Lymphedema Clinical Trial

Official title:

Comparison of a SegNet-based Algorithm Quantitatively Estimating Epifascial Fibrosis in Three-dimensional Computed Tomography Images to the Clinical Lymphedema Grading Method

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04811677
• Other study ID #: UUH 2018-04-009
• Status: Completed
• Start date: January 1, 2018
• Est. completion date: March 30, 2019

Study information

• Verified date: December 2022
• Source: Chungnam National University Sejong Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

To approval for detecting lymphedema fibrosis before its progression, verification of CT-based quantification of suprafascial microscopic fibrosis has been tried.

Description:

In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted at a teaching university hospital. The protocol of this study was approved by the University Hospital Institutional Review Board and was registered at the Protocol Registration and Results System (PRS), www. clini caltr ials. gov (NCT04811677: https:// clini caltr ials. gov/ ct2/ show/ NCT04 811677? term= NCT04 81167 7& draw= 2& rank=1). All methods were performed in accordance with the relevant guidelines and regulations. The trial conformed to the tenets of the Declaration of Helsinki. Patients were included if they were clinically diagnosed with unilateral limb lymphedema and had undergone BEI analysis and CT scanning. The subjects provided written informed consent for publication of the case details. Data were collected as close to the CT scanning date as possible. Patients who were diagnosed with deep vein thrombosis, bilateral limb involvement, vascular disease, or local infection were excluded.

After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 27
• Est. completion date: March 30, 2019
• Est. primary completion date: March 30, 2019
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• The patients who were clinically diagnosed with unilateral limb lymphedema and who underwent multi-frequency bio-electric impedance (BEI) analysis and CT scanning.

Exclusion Criteria:

• The patients who were diagnosed with deep vein thrombosis, bilateral limbs involvement, vascular diseases or local infection were excluded.

Related Conditions & MeSH terms

• Lymphedema

Intervention

Other:
• radiology
image analysis

Locations

Country	Name	City	State
Korea, Republic of	Chungnam National University Sejong Hospital	Sejong

Sponsors (1)

Lead Sponsor	Collaborator
Chungnam National University Sejong Hospital

Country where clinical trial is conducted

Korea, Republic of, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	accuracy	a ratio between the correctly classified pixel and all the classified pixel in one label.	within 1 week after CT scanning
Secondary	First index	(P_(Fat in Affected)+P_(Fibrosis in Affected))/(P_(Fat in Unaffected)+P_(Fibrosis in Unaffected) ) " "	within 1 week after CT scanning