Comparison of a SegNet-based Algorithm Estimating Epifascial Fibrosis

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

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

Study Overview

• Status: Completed
• Conditions: Lymphedema
• Intervention / Treatment: Other: radiology

Detailed 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.

• Study Type: Observational
• Enrollment (Actual): 27

Contacts and Locations

Study Locations

• Korea, Republic of
  • Sejong, Korea, Republic of, 30099
    • Chungnam National University Sejong Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

The patients who were clinically diagnosed with limb lymphedema.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Cross-Sectional

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
accuracy	a ratio between the correctly classified pixel and all the classified pixel in one label.	within 1 week after CT scanning

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
First index	(P_(Fat in Affected)+P_(Fibrosis in Affected))/(P_(Fat in Unaffected)+P_(Fibrosis in Unaffected) ) " "	within 1 week after CT scanning

Collaborators and Investigators

• Sponsor: Chungnam National University Sejong Hospital
• Investigators: Principal Investigator: Chang Ho Hwang, Chungnam National University Sejong Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2018
• Primary Completion (Actual): March 30, 2019
• Study Completion (Actual): March 30, 2019

Study Registration Dates

• First Submitted: March 19, 2021
• First Submitted That Met QC Criteria: March 19, 2021
• First Posted (Actual): March 23, 2021

Study Record Updates

• Last Update Posted (Actual): December 20, 2022
• Last Update Submitted That Met QC Criteria: December 16, 2022
• Last Verified: December 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Lymphatic Diseases; Lymphedema
• Other Study ID Numbers: UUH 2018-04-009

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No