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

NCT04824378 | Unknown

• 1 other identifier: PKUPH202102
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

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.

Conditions

Breast Cancer Related Lymphedema; Deep Learning

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (3)

label 1

Baseline data measurement of this group of patients: arm circumference（positive） and ICG (positive).

Other: No Intervention.

label 2

Baseline data measurement of this group of patients: arm circumference（negative） and ICG (positive).

Other: No Intervention.

label 3

Baseline data measurement of this group of patients: arm circumference（negative） and ICG (negative).

Other: No Intervention.

Interventions

No Intervention. OTHER

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

label 1; label 2; label 3

Eligibility Criteria

• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• 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. .

You may not qualify if:

• 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.

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, China

RECRUITING

Related Publications (4)

• Beek MA, te Slaa A, van der Laan L, Mulder PG, Rutten HJ, Voogd AC, Luiten EJ, Gobardhan PD. Reliability of the Inverse Water Volumetry Method to Measure the Volume of the Upper Limb. Lymphat Res Biol. 2015 Jun;13(2):126-30. doi: 10.1089/lrb.2015.0011.

  PMID: 26091408 BACKGROUND

• Shi S, Lu Q, Fu MR, Ouyang Q, Liu C, Lv J, Wang Y. Psychometric properties of the Breast Cancer and Lymphedema Symptom Experience Index: The Chinese version. Eur J Oncol Nurs. 2016 Feb;20:10-6. doi: 10.1016/j.ejon.2015.05.002. Epub 2015 Jun 9.

  PMID: 26071198 BACKGROUND

• Mihara M, Hara H, Araki J, Kikuchi K, Narushima M, Yamamoto T, Iida T, Yoshimatsu H, Murai N, Mitsui K, Okitsu T, Koshima I. Indocyanine green (ICG) lymphography is superior to lymphoscintigraphy for diagnostic imaging of early lymphedema of the upper limbs. PLoS One. 2012;7(6):e38182. doi: 10.1371/journal.pone.0038182. Epub 2012 Jun 4.

  PMID: 22675520 BACKGROUND

• Yamamoto T, Yamamoto N, Doi K, Oshima A, Yoshimatsu H, Todokoro T, Ogata F, Mihara M, Narushima M, Iida T, Koshima I. Indocyanine green-enhanced lymphography for upper extremity lymphedema: a novel severity staging system using dermal backflow patterns. Plast Reconstr Surg. 2011 Oct;128(4):941-947. doi: 10.1097/PRS.0b013e3182268cd9.

  PMID: 21681123 BACKGROUND

MeSH Terms

Conditions

Breast Cancer Lymphedema

Condition Hierarchy (Ancestors)

Lymphedema; Lymphatic Diseases; Hemic and Lymphatic Diseases; Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Shu Wang, Dr

  Breast Center, Peking University People's Hospital, Beijing, China

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Siyao Liu, Dr

CONTACT

+8618801229921; dr.liusiyao@pku.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 27, 2021
• First Posted: April 1, 2021
• Study Start: October 1, 2016
• Primary Completion: October 1, 2022
• Study Completion: October 1, 2022
• Last Updated: April 1, 2021

Record last verified: 2021-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)