Ignite Creation Date:
2024-05-06 @ 3:58 PM
Last Modification Date:
2024-10-26 @ 2:01 PM
Study NCT ID:
NCT04824378
Status:
UNKNOWN
Last Update Posted:
2021-04-01
First Post:
2021-03-27
Brief Title:
Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning
Sponsor:
Peking University Peoples Hospital
Organization:
Peking University Peoples Hospital
Study Overview
Official Title:
Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning
Status:
UNKNOWN
Status Verified Date:
2021-03
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
BCRLICG
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 remodeling1 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None