an AI-Driven Four-Tier Invasion Depth Classification for Early Esophageal Cancer
NCT07605663 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 890
Last updated 2026-05-26
Summary
The goal of this retrospective observational study is to evaluate whether preoperative endoscopic imaging can accurately assess tumor invasion depth in patients with early esophageal neoplasia undergoing endoscopic submucosal dissection (ESD).
The main question it aims to answer is:"How accurately can preoperative multimodal endoscopic imaging predict histopathological invasion depth in early esophageal squamous cell carcinoma and high-grade intraepithelial neoplasia?" If there is a comparison group: Not applicable (no intervention or arm comparison was specified; analyses are based on diagnostic performance against postoperative pathology as the reference standard).
Participants will:
* Be retrospectively identified from two tertiary hospitals in China;
* Have pathologically confirmed early esophageal squamous cell carcinoma or high-grade intraepithelial neoplasia treated with ESD;
* Have complete postoperative pathology data including invasion depth, lesion size, location, lymphovascular invasion, and margin status;
* Have preoperative high-quality endoscopic images (white-light imaging, narrow-band imaging, iodine staining, and blue laser imaging);
* Undergo retrospective image-pathology correlation analysis to assess diagnostic performance of invasion depth assessment.
Conditions
Interventions
- PROCEDURE
-
ESD
Endoscopic submucosal dissection (ESD) is a minimally invasive endoscopic technique used for en bloc resection of superficial gastrointestinal neoplasms. The procedure is performed under conscious sedation or general anesthesia using a therapeutic endoscope. After lesion characterization and marking of the resection margins, a submucosal injection solution (e.g., saline mixed with epinephrine, dye, or viscous agents such as hyaluronic acid) is administered to lift the lesion from the muscularis propria. A circumferential mucosal incision is then made using an endoscopic knife, followed by meticulous submucosal dissection to separate the lesion from the underlying muscle layer. Hemostasis is achieved throughout the procedure using coagulation forceps or hemostatic devices as needed. The lesion is removed en bloc whenever possible, and the resected specimen is retrieved for histopathological evaluation. Post-resection inspection of the artificial ulcer is performed to assess for bleeding
- PROCEDURE
-
Surgery
Surgery after ESD
Sponsors & Collaborators
-
Fudan University
lead OTHER
Eligibility
- Min Age
- 40 Years
- Max Age
- 80 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2020-01-01
- Primary Completion
- 2026-06-01
- Completion
- 2026-06-01
Countries
- China
Study Locations
More Related Trials
-
Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
NCT07050576 ·Status: RECRUITING
-
Automatic Diagnosis of Early Esophageal Squamous Neoplasia Using pCLE With AI
NCT04136236 ·Status: COMPLETED
-
Development and Application of AI-Based Therapeutic Strategies for Esophageal Cancer Integrating Multimodal Imaging and Digital Pathology
NCT07203690 ·Status: NOT_YET_RECRUITING
-
AI-Based Risk Prediction Model for Upper Digestive Tract Cancer
NCT07605312 ·Status: NOT_YET_RECRUITING
-
Multimodal Deep Learning for Predicting Treatment Response to Neoadjuvant Chemoimmunotherapy in Esophageal Cancer
NCT07063901 ·Status: RECRUITING
-
The Value of Indocyanine Green-guided Near-infrared Fluorescence Technology in Tracing Sentinel Lymph Nodes During Esophagectomy
NCT07303231 ·Status: NOT_YET_RECRUITING ·Phase: PHASE3
-
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-Driven Cancer Diagnosis and Prediction With EHR
NCT06791473 ·Status: RECRUITING
-
Multicenter Observational Study of Multimodal AI for Upper GI Mesenchymal Tumor Diagnosis
NCT07078136 ·Status: RECRUITING
-
Recurrence and Prognosis Prediction Model for Gastric Cancer
NCT07243847 ·Status: COMPLETED
-
AI-Assisted Detection and Staging of Gastric Cancer Using Contrast-Enhanced CT
NCT07250347 ·Status: RECRUITING
-
Development and Validation of a Deep Learning System for Nasopharyngeal Carcinoma Using Endoscopic Images
NCT05627310 ·Status: UNKNOWN
-
Development of a Clinical Decision Support System With Artificial Intelligence for Cancer Care
NCT04675138 ·Status: RECRUITING
-
Predicting Gastric Cancer Response to Chemo With Multimodal AI Model
NCT06451393 ·Status: RECRUITING
-
Prediction of Lymphatic Metastasis in Esophageal Cancer by CT Radiomics
NCT03237130 ·Status: COMPLETED
-
Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
NCT04563416 ·Status: UNKNOWN
-
Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma
NCT06256185 ·Status: COMPLETED ·Phase: NA
-
Prediction of Targeted Therapy Efficacy in EGFR-mutant Lung Cancer Patients Using AI-based Multimodal Data
NCT07287904 ·Status: NOT_YET_RECRUITING
-
The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma:A Single Center, Case-control, Diagnostic Study
NCT05819099 ·Status: NOT_YET_RECRUITING
-
Detection and Biopsy Guidance of Nasopharyngeal Carcinoma Based on Artificial Intelligence and Endoscopic Images
NCT05202626 ·Status: RECRUITING
-
Explainable Machine Learning for Predicting Early Gastric Cancer
NCT07047937 ·Status: ENROLLING_BY_INVITATION
-
Study on the Staging and Prognosis Model of Bladder Cancer
NCT06565923 ·Status: ACTIVE_NOT_RECRUITING
-
Research on Intelligent Screening and Decision-making for Neoadjuvant Therapy in Locally Advanced Gastric Cancer Based on Multi-omics Integration
NCT06396143 ·Status: RECRUITING
-
Prospective Validation of an AI Model for Predicting Liver Metastasis in Colorectal Cancer
NCT07392567 ·Status: RECRUITING
-
Prospective, Randomized Controlled Study to Evaluate the Effect of Artificial Intelligence Assisted Optical Diagnosis of Advanced Adenomas
NCT05568992 ·Status: COMPLETED ·Phase: NA