NCT04824378: BCRL；ICG

Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning

Status unknown Last updated 1 April 2021

What this trial tests

trial testing No Intervention. in Breast Cancer Related Lymphedema in 200 participants. Status unknown.

Timeline

1 October 2016

Primary endpoint
1 October 2022

1 October 2022

Quick facts

Lead sponsor	Peking University People's Hospital
Status	Status unknown
Study type	OBSERVATIONAL
Enrollment	200
Start date	1 October 2016
Primary completion	1 October 2022
Estimated completion	1 October 2022
Sites	1 location across China

Drugs / interventions tested

• No Intervention.

Conditions studied

• Breast Cancer Related Lymphedema — all drugs for Breast Cancer Related Lymphedema →
• Deep Learning — all drugs for Deep Learning →

Sponsor

Peking University People's Hospital

Who can join

Eligibility, female only, with Breast Cancer Related Lymphedema or Deep Learning. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.