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

Recruiting

• Conditions: Sentinel Lymph Node; Breast Cancer
• Interventions: Drug: Indocyanine green

• Registration Number: NCT05623280
• Lead Sponsor: Xiang'an Hospital of Xiamen University

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.

Study Timeline

• Study Start: 2021-11-01
• Status Verified: 2022-11
• Study First Submitted: 2022-11-13
• Study First Submitted QC: 2022-11-13
• Study First Posted: 2022-11-21
• Last Update Submitted: 2022-11-21
• Last Update Posted: 2022-11-28
• Primary Completion: 2024-12-01
• Study Completion: 2024-12-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 40

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Indocyanine green	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.

Primary Outcome Measures

Name	Time	Method
Diagnosis of lymph node metastasis	Participants will be followed for the duration of hospital stay, an expected average of 3 months	The lymph node metastasis (LNM) status was determined based on the pathological diagnosis of the surgical specimens.

Trial Locations

Locations (1)

Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine; 🇨🇳 Xiamen, Fujian, China