Breast Cancer Related LymphedemaBCRL；ICGNCT04824378
Study on Classification Method of Indocyanine Green Lymphography Based on Deep Learning
An observational study of Breast Cancer Related Lymphedema and Deep Learning, run by Peking University People's Hospital at 1 site in China.
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. Read more about this study
- Who is it for?
- Any age, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer Related Lymphedema, Deep Learning
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2016to 1 Oct 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Women only
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: patients who have been admitted to the Breast Surgery Clinic due to the main complaint of upper extremity edema
Who may be able to join
- 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. .
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University People's Hospital
Beijing, Beijing Municipality
liu siyao, +86 18801229921, doc_lsy@163.com
About this study
Official title: Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning
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
- Being tested
- No Intervention.
- Medical subject headings
- Breast Cancer Lymphedema
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No Intervention.Other
No Intervention.Only learn ICG image features of different label groups
| Study group | Role | What they receive |
|---|---|---|
| label 1 | Baseline data measurement of this group of patients: arm circumference（positive） and ICG (positive). Receives: Other: No Intervention. | |
| label 2 | Baseline data measurement of this group of patients: arm circumference（negative） and ICG (positive). Receives: Other: No Intervention. | |
| label 3 | Baseline data measurement of this group of patients: arm circumference（negative） and ICG (negative). Receives: Other: No Intervention. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 2016-2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2021
- Study start1 Oct 2016
- Primary completion1 Oct 2022 (estimated)
- Study completion1 Oct 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Breast Cancer Related LymphedemaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Shu Wang, Dr, Principal investigator, Breast Center, Peking University People's Hospital, Beijing, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04824378
- Sponsor's protocol ID
- PKUPH202102
- Acronym
- BCRL；ICG
- Status last verified
- Mar 2021
- First submitted
- 27 Mar 2021
- Last updated
- 1 Apr 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: there is no plan to make individual participant data (IPD) available to other researchers
Publications
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.
PubMed 26091408Shi 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.
PubMed 26071198Mihara 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.
PubMed 22675520Yamamoto 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.
PubMed 21681123
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study on Classification Method of Indocyanine Green Lymphography in Diagnosing Breast Cancer-related Lymphedema Based on Deep Learning
- ClinicalTrials.gov ID
- NCT04824378
- Acronym / sponsor ID
- BCRL；ICG / PKUPH202102
- Conditions
- Breast Cancer Related Lymphedema, Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 1 Oct 2016 (actual)
- Primary completion
- 1 Oct 2022 (estimated)
- Study completion
- 1 Oct 2022 (estimated)
- First posted
- 1 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| label 1 | – | Other: No Intervention.Baseline data measurement of this group of patients: arm circumference（positive） and ICG (positive). |
| label 2 | – | Other: No Intervention.Baseline data measurement of this group of patients: arm circumference（negative） and ICG (positive). |
| label 3 | – | Other: No Intervention.Baseline data measurement of this group of patients: arm circumference（negative） and ICG (negative). |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients who have been admitted to the Breast Surgery Clinic due to the main complaint of upper extremity edema
Key inclusion criteria (1 listed)
- 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. .
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No Intervention.No Intervention.Only learn ICG image features of different label groups | Other | label 1, label 2, label 3 |
Primary outcome
Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning
Time frame: 2016-2022
Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2021.
- Background: PubMed 26091408Beek 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.
- Background: PubMed 26071198Shi 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.
- Background: PubMed 22675520Mihara 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.
- Background: PubMed 21681123Yamamoto 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.
- Individual participant dataSharing planned: No. there is no plan to make individual participant data (IPD) available to other researchers
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Shu Wang, Dr principal investigator
Breast Center, Peking University People's Hospital, Beijing, China
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer Related Lymphedema and Deep Learning, run by Peking University People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 1 Apr 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Siyao Liu, Dr (+8618801229921, dr.liusiyao@pku.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Establish a PKUPH model for the diagnosis of lymphedema by ICG based on deep learning (time frame: 2016-2022). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04824378, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Apr 2021.