Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node.

Breast Cancer Clinical Trial

Official title:

Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node in Patients With Breast Cancer.

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05623280
• Other study ID #: Xiang'an Hospital of XMU
• Status: Recruiting
• Start date: November 1, 2021
• Est. completion date: December 1, 2024

Study information

• Verified date: November 2022
• Source: Xiang'an Hospital of Xiamen University
• Contact: Xueqi Fan, MD
• Phone: 19859202604
• Email: fanxq[@]stu.xmu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 40
• Est. completion date: December 1, 2024
• Est. primary completion date: December 1, 2024
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years to 70 Years

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms
• Sentinel Lymph Node

Intervention

Drug:
• Indocyanine green
Injection around the areola with 2-4 points Indocyanine green with 2ml of 1.25mg/mL

Locations

Country	Name	City	State
China	Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine	Xiamen	Fujian

Sponsors (1)

Lead Sponsor	Collaborator
Xiang'an Hospital of Xiamen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnosis of lymph node metastasis	The lymph node metastasis (LNM) status was determined based on the pathological diagnosis of the surgical specimens.	Participants will be followed for the duration of hospital stay, an expected average of 3 months