MedTrace Logo

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

NCT04824378 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2021-04-01

No results posted yet for this study

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.

Conditions

  • Breast Cancer Related Lymphedema
  • Deep Learning

Interventions

OTHER

No Intervention.

No Intervention.Only learn ICG image features of different label groups

Sponsors & Collaborators

  • Peking University People's Hospital

    lead OTHER

Principal Investigators

  • Shu Wang, Dr · Breast Center, Peking University People's Hospital, Beijing, China

Eligibility

Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2016-10-01
Primary Completion
2022-10-01
Completion
2022-10-01

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04824378 on ClinicalTrials.gov