Clinical trial · Observational
Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
Deep Learning and Radiomics for Prediction of Lymph Node Metastasis in Early-stage Esophageal Squamous Cell Carcinoma
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study aims to develop a predictive model using deep learning and radiomics to assess the likelihood of lymph node metastasis in patients with early-stage esophageal squamous cell carcinoma (ESCC). Lymph node metastasis is a critical factor in determining the treatment approach and prognosis for ESCC patients. By analyzing medical imaging data, we hope to create a non-invasive method that can assist doctors in making more accurate treatment decisions. This research could improve patient outcomes by enabling earlier and more tailored interventions.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| ESCC | — | UNRESOLVED | — |
| Lymph Node Metastasis | — | UNRESOLVED | — |
| Radiomics | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- A
- description
- A total of 400 patients with early-stage ESCC from our center were divided into training and test sets.
- interventionNames
- Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
- label
- B
- description
- A total of 100 patients with early-stage ESCC from other center were defined as external validation
- interventionNames
- Diagnostic Test: The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
Primary outcomes (1)
- measure
- AUC(the area under the curve) values of the model
- timeFrame
- 4 years
- description
- The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC).
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Patients with pathologically confirmed early-stage (T1) ESCC * Preoperative contrast-enhanced CT data within 2 weeks before surgery * Without any treatment before surgical resection Exclusion Criteria: * Patients who underwent neoadjuvant therapy or endoscopic treatment * Insufficient CT imaging or poor CT quality * Incomplete pathology results * Presence of metastatic disease
References
Publications (0)
Data not yet available