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

NCT ID: NCT05623280

Last Updated: 2022-11-28

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 40 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-11-01
• Study Completion Date: 2024-12-01

Brief 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.

Conditions

Breast Cancer; Sentinel Lymph Node

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

Indocyanine green

Injection around the areola with 2-4 points Indocyanine green with 2ml of 1.25mg/mL; Achieve Intraoperative fluorescence images by Near-Infrared I ( NIR-I ) fluorescence imaging instrument.

Indocyanine green

Intervention Type: DRUG

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

Interventions

Indocyanine green

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

Intervention Type: DRUG

Other Intervention Names

ICG

Eligibility 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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Xiang'an Hospital of Xiamen University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Xueqi Fan, MD

Role: PRINCIPAL_INVESTIGATOR

School of Medicine, Xiamen University

Locations

Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine

Xiamen, Fujian, China

Site Status: RECRUITING

Countries

China

Central Contacts

Xueqi Fan, MD

Role: CONTACT

fanxq@stu.xmu.edu.cn

19859202604

Facility Contacts

Xueqi Fan, MD

Role: primary

fanxq@stu.xmu.edu.cn

19859202604

Other Identifiers

Xiang'an Hospital of XMU

Identifier Type: -

Identifier Source: org_study_id