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Comparison of a SegNet-based Algorithm Estimating Epifascial Fibrosis

NCT ID: NCT04811677

Last Updated: 2022-12-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

27 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-01-01

Study Completion Date

2019-03-30

Brief Summary

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

Detailed Description

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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.

Conditions

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Lymphedema

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Interventions

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radiology

image analysis

Intervention Type OTHER

Eligibility Criteria

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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.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Chungnam National University Sejong Hospital

OTHER

Sponsor Role lead

Responsible Party

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Chang Ho Hwang, MD, PhD.

Chief director

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Chang Ho Hwang

Role: PRINCIPAL_INVESTIGATOR

Chungnam National University Sejong Hospital

Locations

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Chungnam National University Sejong Hospital

Sejong, , South Korea

Site Status

Countries

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South Korea

Provided Documents

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Document Type: Statistical Analysis Plan

View Document

Other Identifiers

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UUH 2018-04-009

Identifier Type: -

Identifier Source: org_study_id