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

Study Overview
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Brief Summary
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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.

Official Title
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Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning

Conditions
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Breast Cancer Related Lymphedema, Deep Learning

Intervention / Treatment
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* Other: No Intervention.


Participation Criteria
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Eligibility Criteria
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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.
Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| label 1<br>Baseline data measurement of this group of patients: arm circumference（positive） and ICG (positive). | Other: No Intervention.<br>* No Intervention.Only learn ICG image features of different label groups<br>|
| label 2<br>Baseline data measurement of this group of patients: arm circumference（negative） and ICG (positive). | Other: No Intervention.<br>* No Intervention.Only learn ICG image features of different label groups<br>|
| label 3<br>Baseline data measurement of this group of patients: arm circumference（negative） and ICG (negative). | Other: No Intervention.<br>* No Intervention.Only learn ICG image features of different label groups<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning | Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning | 2016-2022 |