Conceptio › Archive › ClinicalTrials.gov
ClinicalTrials.gov public full text Public Domain

Image-Guided Platform-Assisted Sentinel Lymph Node Retrieval in Early Gastric Cancer

Beijing Friendship Hospital · 2026
ClinicalTrials.gov · Datasets · License: Public Domain · 2026
Open Source ↗
This prospective, single-center, randomized controlled trial aims to evaluate the clinical effectiveness of an Image-Guided Platform assisted by a deep learning model for sentinel lymph node (SLN) retrieval in patients with early gastric cancer undergoing laparoscopic-endoscopic cooperative surgery combined with sentinel lymph node navigation surgery (LECS-SNNS). A total of 36 eligible participants will be randomly assigned in a 1:1 ratio to either the Image-Guided Platform group or the Traditional Palpation group. Both groups will undergo standardized indocyanine green fluorescence-guided sentinel lymphatic basin dissection and initial retrieval of fluorescent sentinel lymph nodes. In the experimental group, the remaining sentinel lymphatic basin (SLB) tissue will be examined using the Image-Guided Platform, which integrates radiographic and optical imaging with a deep learning model to identify and localize suspected lymph nodes. In the control group, LN retrieval will be performed using traditional visual inspection and palpation. The primary objective is to determine whether Image-Guided Platform-assisted LN retrieval increases the total number of SLNs retrieved intraoperatively compared with Traditional Palpation. Secondary objectives include assessment of LN retrieval efficiency, pathological findings, additional LN identification during permanent pathological examination, concordance between intraoperative frozen-section and permanent pathological examination, and other procedural, pathological, technical, and clinical outcomes relevant to the feasibility and performance of the Image-Guided Platform and deep learning model.
Conceptio Open Archive

Analyze & explore related research in Conceptio

Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.

This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗

Related documents

Record · ID 1127280
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive