Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning (BCRL;ICG)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04824378
Recruitment Status : Recruiting
First Posted : April 1, 2021
Last Update Posted : April 1, 2021
Sponsor:
Information provided by (Responsible Party):
Peking University People's Hospital

Brief Summary:
Breast cancer related lymphedema (BCRL) is the most common complication after breast cancer surgery, which brings a heavy psychological and spiritual burden to patients. For a long time, the diagnosis and treatment of lymphedema has been a difficult point in domestic and foreign research. To a large extent, it is because most of the patients who come to see a doctor have already developed obvious lymphedema, and the internal lymphatic vessels have undergone pathological remodeling[1] Therefore, it is particularly important to detect early lymphedema and intervene in time through the use of sensitive screening tools. Indocyanine green (ICG) lymphangiography is a relatively new method, which can display superficial lymph flow in real time and quickly, and will not be affected by radioactivity [7]. In 2007, indocyanine green lymphography was used for the first time to evaluate the function of superficial lymphatic vessels. In 2011, Japanese scholars found skin reflux signs based on ICG lymphography data of 20 patients with lymphedema after breast cancer surgery, and they were roughly divided into three types according to their severity: splash, star cluster, and diffuse (Figure 1) [8]. Later, in 2016, a prospective study involving 196 people affirmed the value of ICG lymphography in the early diagnosis of lymphedema, and made the images of ICG lymphography more specific stages 0-5 [9], but The staging is still based on the three types of skin reflux symptoms found in a small sample clinical study in 2011, which is not completely applicable in actual clinical applications. In addition, when abnormal skin reflux symptoms appear on ICG lymphangiography, the pathophysiological changes that occur in the body lack research and exploration. Therefore, this research hopes to refine the image features of ICG lymphography through machine learning (deep learning), and establish a PKUPH model for diagnosing early lymphedema by staging the image features.

Condition or disease Intervention/treatment
Breast Cancer Related Lymphedema Deep Learning Other: No Intervention.

Layout table for study information
Study Type : Observational
Estimated Enrollment : 200 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning
Actual Study Start Date : October 1, 2016
Estimated Primary Completion Date : October 1, 2022
Estimated Study Completion Date : October 1, 2022

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Lymphedema

Group/Cohort Intervention/treatment
label 1
Baseline data measurement of this group of patients: arm circumference(positive) and ICG (positive).
Other: No Intervention.
No Intervention.Only learn ICG image features of different label groups

label 2
Baseline data measurement of this group of patients: arm circumference(negative) and ICG (positive).
Other: No Intervention.
No Intervention.Only learn ICG image features of different label groups

label 3
Baseline data measurement of this group of patients: arm circumference(negative) and ICG (negative).
Other: No Intervention.
No Intervention.Only learn ICG image features of different label groups




Primary Outcome Measures :
  1. Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning [ Time Frame: 2016-2022 ]
    Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   Yes
Sampling Method:   Probability Sample
Study Population
patients who have been admitted to the Breast Surgery Clinic due to the main complaint of upper extremity edema
Criteria

Inclusion Criteria:

  • From October 2016 to present, about 200 patients who have been admitted to the Breast Surgery Clinic due to the main complaint of upper extremity edema, are willing to accept ICG lymphography, arm circumference measurement, drainage measurement, bioelectrical impedance measurement, main complaint scale, etc. .

Exclusion Criteria:

  • Bilateral breast cancer; history of contrast agent allergy; arteriovenous thrombosis in the affected limb; regional lymph node recurrence; no informed consent; severe heart and brain diseases; primary lymphatic system disease (such as lymphatic leakage); unilateral only The limbs received ICG imaging.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04824378


Contacts
Layout table for location contacts
Contact: Siyao Liu, Dr +8618801229921 dr.liusiyao@pku.edu.cn

Locations
Layout table for location information
China, Beijing
Peking University People's Hospital Recruiting
Beijing, Beijing, China
Contact: liu siyao    +86 18801229921    doc_lsy@163.com   
Sponsors and Collaborators
Peking University People's Hospital
Investigators
Layout table for investigator information
Principal Investigator: Shu Wang, Dr Breast Center, Peking University People's Hospital, Beijing, China
Publications:
Layout table for additonal information
Responsible Party: Peking University People's Hospital
ClinicalTrials.gov Identifier: NCT04824378    
Other Study ID Numbers: PKUPH202102
First Posted: April 1, 2021    Key Record Dates
Last Update Posted: April 1, 2021
Last Verified: March 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: there is no plan to make individual participant data (IPD) available to other researchers

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
Layout table for MeSH terms
Breast Neoplasms
Lymphedema
Breast Cancer Lymphedema
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases
Lymphatic Diseases
Postoperative Complications
Pathologic Processes