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

Deep Learning Clinical Trial

— BCRL;ICG  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04824378
• Other study ID #: PKUPH202102
• Status: Recruiting
• Start date: October 1, 2016
• Est. completion date: October 1, 2022

Study information

• Verified date: March 2021
• Source: Peking University People's Hospital
• Contact: Siyao Liu, Dr
• Phone: +8618801229921
• Email: dr.liusiyao[@]pku.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: October 1, 2022
• Est. primary completion date: October 1, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer Lymphedema
• Breast Cancer Related Lymphedema
• Breast Neoplasms
• Deep Learning
• Lymphedema

Intervention

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

Locations

Country	Name	City	State
China	Peking University People's Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Peking University People's Hospital

Country where clinical trial is conducted

China, 

References & 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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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