Novel Humanized Ferritin-Based NIR Fluorescent Probe for Identifying Sentinel Lymph Node Metastasis in Early Gastric Cancer
NCT07294638 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 47
Last updated 2025-12-19
Summary
With the widespread adoption of early cancer screening, the proportion of early gastric cancer (EGC) in China has been gradually increasing. The primary treatments for EGC are endoscopic and surgical interventions. For EGC invading the submucosal layer, the lymph node metastasis rate is approximately 20%. In clinical practice, subtotal gastrectomy combined with D2 lymphadenectomy is commonly performed to achieve radical tumor resection, resulting in up to 80% of patients undergoing unnecessary lymph node dissection. Sentinel lymph nodes (SLNs), which are the first potential sites along the lymphatic drainage pathway from the primary tumor to receive cancer cells, can effectively represent the status of lymphatic metastasis. As gastric cancer surgery evolves toward minimally invasive, precise, and individualized approaches, there is an urgent clinical need for a safe and effective SLN mapping technique to intraoperatively distinguish between benign and malignant SLNs, thereby avoiding unnecessary lymphadenectomy and improving patient prognosis.
Currently, indocyanine green (ICG) is the only clinically approved near-infrared (NIR) fluorescent dye and is relatively widely used. However, it lacks targeting specificity, diffuses too rapidly, and has difficulty identifying micrometastases in SLNs. Therefore, we aimed to enhance ICG's targeting ability toward metastatic tumor cells to increase fluorescence signal intensity in malignant SLNs, enabling intraoperative differentiation between benign and malignant SLNs and reducing unnecessary lymph node dissection. Considering the high heterogeneity of tumor cells and the molecular diversity among metastatic foci within malignant SLNs, our team innovatively proposed a multi-target probe design to improve targeting capability against heterogeneous tumor cells. Based on molecular imaging technology combined with near-infrared (NIR) imaging, we developed a humanized ferritin-based probe (VE/CX-FTn) targeting metastatic lymph nodes. Previous studies have confirmed the effective identification of metastatic lymph nodes by the VE/CX-FTn probe.
To further validate the effectiveness of this probe in identifying SLN metastasis in early gastric cancer with a low lymph node metastasis rate, this study plans to use residual early gastric cancer tissues obtained from post-surgical resections to evaluate the probe's imaging capability for SLNs. Furthermore, a prospective clinical sample cohort study will be conducted to verify its diagnostic efficacy for metastatic lymph nodes of varying sizes. The aim is to demonstrate that our developed probe can guide the identification of SLNs in early gastric cancer and assist in determining the presence of SLN metastasis, thereby reducing unnecessary lymphadenectomy and improving patient prognosis.
This study employs a novel NIR fluorescent molecular probe, VE/CX-FTn-ICG, based on humanized ferritin, with the objective of investigating its effectiveness in distinguishing between benign and malignant sentinel lymph nodes in early gastric cancer. Using this probe, we have already demonstrated that VE/CX-FTn-ICG enables precise differentiation between benign and malignant lymph nodes in animal models. The probe specifically binds to tumor cells, exhibiting high targeting specificity and imaging capability, thereby providing real-time and accurate intraoperative imaging of SLNs for early gastric cancer surgery. A further goal of this study is to validate the application efficacy of this targeted probe in distinguishing between benign and malignant SLNs using ex vivo human early gastric cancer tissue samples. This aims to provide a reliable auxiliary tool for intraoperative SLN biopsy in early gastric cancer, assisting in avoiding unnecessary lymph node resection for patients, and ultimately improving surgical outcomes and patient prognosis.
Conditions
- Gastric Cancer
- Molecular Imaging
Interventions
- DIAGNOSTIC_TEST
-
VE/CX-FTn-ICG injection solution
The freshly resected gastric cancer specimens were submucosally injection with VE/CX-FTn-ICG solution and underwent fluorescence imaging.
Sponsors & Collaborators
-
Nanfang Hospital, Southern Medical University
lead OTHER
Eligibility
- Min Age
- 18 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2025-07-15
- Primary Completion
- 2026-07-01
- Completion
- 2026-07-07
Countries
- China
Study Locations
More Related Trials
-
Explainable Machine Learning for Predicting Early Gastric Cancer
NCT07047937 ·Status: ENROLLING_BY_INVITATION
-
Confocal Laser Endomicroscopy for in Vivo Molecular Imaging of Gastric Cancer by Targeting MG7-Ag
NCT01366586 ·Status: COMPLETED
-
Early Stage Lung Cancer Screening With Low-dose Computed Tomographic
NCT02898441 ·Status: UNKNOWN ·Phase: NA
-
A Prospective Clinical Study to Validate a Preoperative Risk Scoring Model for LNM in GC Patients
NCT06339307 ·Status: COMPLETED
-
A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
NCT06829147 ·Status: RECRUITING
-
Diagnosis of Gastric Lesions From Exhaled Breath and Saliva
NCT01420588 ·Status: COMPLETED
-
Novel Detection System for Lung Cancer Curative Effect Monitoring
NCT02666755 ·Status: UNKNOWN
-
Development and Prospective Validation of a Digital Pathology-based Artificial Intelligence Diagnostic Model for Pan-cancer Lymphatic Metastasis
NCT06517979 ·Status: RECRUITING
-
Precision Diagnosis for Intraoperative Frozen Section of Early Stage Lung Cancer
NCT02941003 ·Status: UNKNOWN ·Phase: NA
-
Mechanistic Study on the Diagnosis of Esophageal Cancer Lymph Node Metastasis Using Spectral CT, Multimodal MRI, FAPI PET-CT, Pathology, and AI Evaluation System
NCT06818214 ·Status: NOT_YET_RECRUITING
-
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
NCT06632886 ·Status: RECRUITING ·Phase: NA
-
Multimodal Deep Learning for Lymph Node Metastasis Prediction and Physician Performance Assessment in T1 Gastric Cancer
NCT07124754 ·Status: RECRUITING
-
Validation of High-Throughput Large-Format Tissue Preprocessing for Lung and Colorectal Cancer
NCT07239063 ·Status: RECRUITING
-
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
NCT06831357 ·Status: RECRUITING
-
Prospective Screening Programme for Malignant Tumors
NCT04230200 ·Status: UNKNOWN
-
Precision Diagnosis and Therapy for Early Stage Lung Cancer
NCT02936804 ·Status: UNKNOWN ·Phase: NA
-
CT Body Composition Enhances Survival Risk Stratification
NCT07109271 ·Status: COMPLETED
-
Deep Learning Signature for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
NCT05736991 ·Status: UNKNOWN
-
Association Between VEGF-C and miRNA and Clinical Non-small Cell Lung Cancer and Esophagus Squamous Cell Carcinoma
NCT01240369 ·Status: UNKNOWN
-
Application of 68Ga-labeled ACE2 Targeting Probe PET/CT Imaging in Tracing ACE2 Expression and Diagnosis of Lung Cancer
NCT06194630 ·Status: RECRUITING ·Phase: EARLY_PHASE1
-
Artificial Intelligence-assisted Screening of Malignant Pigmented Tumors on the Ocular Surface
NCT05645341 ·Status: COMPLETED
-
Hematological Dynamic Scores for Predicting Survival and Treatment Response for Advanced Gastric Cancer After Neoadjuvant Therapy
NCT06573307 ·Status: COMPLETED
-
Methylation-specific PCR Test for Early Screening and Early Diagnosis of Nasopharyngeal Carcinoma
NCT06367049 ·Status: COMPLETED
-
Robotic-Assisted Navigation for Lung Nodule Localization: A Non-Inferiority Study
NCT07055997 ·Status: COMPLETED ·Phase: NA
-
Artificial Intelligence for Pathology Diagnosis and Prognosis Prediction of Lung Nodule Using Smartphone Photos
NCT07098884 ·Status: RECRUITING