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Trial Title:
Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node.
NCT ID:
NCT05623280
Condition:
Breast Cancer
Sentinel Lymph Node
Conditions: Official terms:
Breast Neoplasms
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Other
Intervention:
Intervention type:
Drug
Intervention name:
Indocyanine green
Description:
Injection around the areola with 2-4 points Indocyanine green with 2ml of 1.25mg/mL
Arm group label:
Indocyanine green
Other name:
ICG
Summary:
The purpose of this study is to analysis the fluorescence image of the breast sentinel
lymph node (SLN) using Indocyanine green (ICG). Moreover, to investigate whether an
artificial intelligence protocol was suitable for identifying metastatic status of SLN
during the surgery, and evaluate the diagnosis consistency of the AI technique and
pathological examinations for lymph node with and without metastasis.
Detailed description:
Assessment of the sentinel lymph node (SLN) in patients with early stage breast cancer is
vital in selecting the appropriate surgical approach. But identification of metastatic
LNs within the fibro-adipose tissue of the fossa axillaris specimen remains a challenge.
Recently, indocyanine green (ICG) and methylene blue are commonly used in clinical
practice. ICG as a fluorescent dyes, has effectiveness in mapping SLNs during surgery.
Surgeons can follow the fluorescence display to detect SLN, and simultaneously capture
real-time fluorescent video images. Several other groups has been demonstrated that the
usage of ICG fluorescent surgical navigation system to detect SLNs in breast cancer
patients is technically feasible. But no study investigate the variability between
fluorescent images of breast sentinel lymph node with and without metastasis in the
existing paper. Deep learning (DL) artificial intelligence (AI) algorithms in medical
imaging are rapidly expanding.
In this study, the investigators aim to develop and validate an easy-to-use artificial
intelligence prediction model to intraoperatively identify the sentinel lymph node
metastasis status. Furthermore, to explore whether this independent and parallel
intraoperative lymph node assessment workflow can provide rapid and accurate skull base
on lymph node fluorescent images analysis, meanwhile detecting occult lymph node (micro-)
metastasis, using optical imaging and artificial intelligence.
Criteria for eligibility:
Study pop:
Participants were recruited from Xiang'An Hospital of Xiamen University, between November
30, 2022, and November 30, 2024.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Patients aged 18-70 years female.
- The preoperative core needle biopsy or open surgical excision biopsy diagnosis as
breast cancer.
- No clinical examination of suspicious axillary lymph node-positive.
- Preoperative clinical or radiologic evidence without distant metastases (M0).
- The patient has good compliance with the planned protocol during the study and
signed informed consent.
Exclusion Criteria:
- Pregnancy, breastfeeding.
- Allergy to ICG.
- Former operation or radiotherapy in the axilla or breast or thoracic wall in the
same side of breast cancer.
- Psychiatric or cognitive impairment.
Gender:
Female
Gender based:
Yes
Gender description:
women aged 18-70 years
Minimum age:
18 Years
Maximum age:
70 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine
Address:
City:
Xiamen
Zip:
361000
Country:
China
Status:
Recruiting
Contact:
Last name:
Xueqi Fan, MD
Phone:
19859202604
Email:
fanxq@stu.xmu.edu.cn
Start date:
November 1, 2021
Completion date:
December 1, 2024
Lead sponsor:
Agency:
Xiang'an Hospital of Xiamen University
Agency class:
Other
Source:
Xiang'an Hospital of Xiamen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05623280
