LymphedemaNCT04811677
Comparison of a SegNet-based Algorithm Estimating Epifascial Fibrosis
An observational study of Lymphedema, run by Chungnam National University Sejong Hospital at 1 site in South Korea.
Trial summary
To approval for detecting lymphedema fibrosis before its progression, verification of CT-based quantification of suprafascial microscopic fibrosis has been tried. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Lymphedema
- What is being tested?
- radiology
- How many people
- 27 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Jan 2018to 30 Mar 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
Study population: The patients who were clinically diagnosed with limb lymphedema.
Who may be able to join
- The patients who were clinically diagnosed with unilateral limb lymphedema and who underwent multi-frequency bio-electric impedance (BEI) analysis and CT scanning.
Who cannot join
- The patients who were diagnosed with deep vein thrombosis, bilateral limbs involvement, vascular diseases or local infection were excluded.
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Chungnam National University Sejong Hospital
Sejong, 30099
About this study
Official title: 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.
Read the 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.
- Conditions
- Lymphedema
- Being tested
- radiology
- Medical subject headings
- Lymphedema
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
radiologyOther
image analysis
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy
a ratio between the correctly classified pixel and all the classified pixel in one label.
Measured: within 1 week after CT scanning
Secondary outcomes (1)
First index
(P_(Fat in Affected)+P_(Fibrosis in Affected))/(P_(Fat in Unaffected)+P_(Fibrosis in Unaffected) ) " "
Measured: within 1 week after CT scanning
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Mar 2021
- Study start1 Jan 2018
- Primary completion30 Mar 2019
- Study completion30 Mar 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for LymphedemaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 27 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chungnam National University Sejong Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chang Ho Hwang, MD, PhD., Chungnam National University Sejong Hospital
- Investigators
- Chang Ho Hwang, Principal investigator, Chungnam National University Sejong Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04811677
- Sponsor's protocol ID
- UUH 2018-04-009
- Status last verified
- Dec 2022
- First submitted
- 19 Mar 2021
- Last updated
- 20 Dec 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 20 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of a SegNet-based Algorithm Quantitatively Estimating Epifascial Fibrosis in Three-dimensional Computed Tomography Images to the Clinical Lymphedema Grading Method
- ClinicalTrials.gov ID
- NCT04811677
- Acronym / sponsor ID
- UUH 2018-04-009
- Conditions
- Lymphedema
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 27 participants (actual)
- Lead sponsor
- Chungnam National University Sejong HospitalUniversity, hospital or other
- Study start
- 1 Jan 2018 (actual)
- Primary completion
- 30 Mar 2019 (actual)
- Study completion
- 30 Mar 2019 (actual)
- First posted
- 23 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To approval for detecting lymphedema fibrosis before its progression, verification of CT-based quantification of suprafascial microscopic fibrosis has been tried.
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 design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- The patients who were clinically diagnosed with limb lymphedema.
Key inclusion criteria (1 listed)
- The patients who were clinically diagnosed with unilateral limb lymphedema and who underwent multi-frequency bio-electric impedance (BEI) analysis and CT scanning.
Key exclusion criteria (1 listed)
- The patients who were diagnosed with deep vein thrombosis, bilateral limbs involvement, vascular diseases or local infection were excluded.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| radiologyimage analysis | Other | – |
Primary outcome
accuracy
Time frame: within 1 week after CT scanning
a ratio between the correctly classified pixel and all the classified pixel in one label.
1 secondary outcome
First index
Time frame: within 1 week after CT scanning
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Dec 2022.
Related trials for Lymphedema
Not yet recruiting
Agentic AI App and Home Bioimpedance Monitoring for Endocrine Therapy Adherence and Lymphedema in Breast Cancer Survivors
Breast Neoplasm Malignant Primary, Lymphedema Arm
Active, not recruiting
The Effectiveness of Lymphatic Bypass Supermicrosurgery
Breast Cancer Related Lymphedema, Lymphedema Arm
Recruiting
Lymphovenous Bypass Procedure for Secondary Prevention of Lymphedema in Breast Cancer
Lymphedema Arm
RecruitingObservational study
A Prospective Natural History Study of Lymphatic Anomalies
Lymphatic Diseases, Lymphatic Abnormalities
Recruiting
The LYMPH Trial - Microsurgical Versus Conservative Treatment of Chronic Breast Cancer Associated Lymphedema
Lymphedema, Breast Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Chang Ho Hwang principal investigator
Chungnam National University Sejong Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lymphedema, run by Chungnam National University Sejong Hospital at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Dec 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
accuracy (time frame: within 1 week after CT scanning). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Dec 2022.