Deep Learning-Based Intraoperative Dual-tracer Video Analysis of Sentinel Lymph Node Mapping for Metastasis Prediction in cN0 Papillary Thyroid Carcinoma

Summary

The goal of this observational study is to learn if a computer program (deep learning) can accurately predict lymph node spread in adults with papillary thyroid cancer who have no signs of lymph node involvement before surgery (called cN0). The main questions it aims to answer are:

* Can video analysis of lymph node mapping during surgery predict if cancer has spread to lymph nodes beyond the first-draining (sentinel) lymph node?
* Can this prediction help surgeons decide how much tissue to remove during surgery?

During surgery, participants will receive an injection of two special dyes (carbon nanoparticles and indocyanine green) near the thyroid tumor. These dyes travel through the lymphatic system and help surgeons see the lymph nodes. A special camera records a video of how the dyes move and light up the lymph nodes.

Researchers will use computer programs to analyze these videos along with other medical information (such as ultrasound results and tumor characteristics) to predict whether cancer has spread to additional lymph nodes. The predictions will be compared against the actual results from tissue samples examined after surgery.

Participants will receive standard thyroid cancer surgery. The study does not change the surgical treatment. The video recording adds no extra risk to participants.

Conditions

• Papillary Thyroid Carcinoma

Interventions

DIAGNOSTIC_TEST

Indocyanine green (ICG) sentinel lymph node mapping

Intraoperative sentinel lymph node mapping using indocyanine green (ICG) with near-infrared fluorescence imaging. Preparation: ICG powder (25 mg) is dissolved in 10 ml sterile water to achieve a concentration of 2.5 mg/ml. Administration: 0.2 ml of ICG solution is injected at multiple points around the thyroid tumor under real-time ultrasound guidance using a precision multi-point stereotactic injection technique. Visualization: A near-infrared fluorescence imaging system (excitation wavelength 750-800 nm, emission wavelength 820 nm) is used to visualize lymphatic channels and identify sentinel lymph nodes in real time during surgery. The sentinel lymph node is defined as the first lymph node that shows fluorescence signal after tracer injection.

DIAGNOSTIC_TEST

Carbon nanoparticle (CNs) sentinel lymph node mapping

Intraoperative sentinel lymph node mapping using carbon nanoparticle suspension with visual identification. Preparation: Carbon nanoparticle suspension is used at the commercial concentration of 50 mg/ml. Administration: 0.2 ml of carbon nanoparticle suspension is injected at multiple points around the thyroid tumor under real-time ultrasound guidance using a precision multi-point stereotactic injection technique. Visualization: Carbon nanoparticles (diameter 150 nm) selectively enter lymphatic channels and accumulate in lymph nodes, producing visible black staining. Surgeons identify sentinel lymph nodes by direct visual inspection of black-stained nodes. The sentinel lymph node is defined as the first lymph node that shows black staining after tracer injection.

DIAGNOSTIC_TEST

Dual-tracer (ICG combined with CNs) sentinel lymph node mapping

Intraoperative sentinel lymph node mapping using combined indocyanine green and carbon nanoparticles with near-infrared fluorescence imaging and visual identification. Preparation: 0.1 ml of ICG solution (2.5 mg/ml) is mixed with 0.1 ml of carbon nanoparticle suspension (50 mg/ml) to form a 0.2 ml dual-tracer composite agent. Administration: The mixed tracer is injected at multiple points around the thyroid tumor under real-time ultrasound guidance using a precision multi-point stereotactic injection technique. Visualization: Near-infrared fluorescence imaging captures real-time lymphatic flow dynamics (ICG component), while black staining provides durable visual lymph node identification (CNs component). Video recording documents the entire sentinel lymph node visualization process for at least 5 minutes at 1920x1080 resolution. Deep learning analysis: In this group, video recordings are analyzed using nine deep learning models to extract spatiotemporal features and predict second

Sponsors & Collaborators

• First Affiliated Hospital of Chongqing Medical University

  lead OTHER

Principal Investigators

• Xinliang Su, MD,PhD · First Affiliated Hospital of Chongqing Medical University

• Han Gao, MD,PhD · Women and Children's Hospital of Chongqing Medical University

• Xinliang Su, MD,PhD · First Affiliated Hospital of Chongqing Medical University

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-04-01

Primary Completion

2024-10-31

Completion

2024-10-31

Countries

• China

Study Locations