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
The First Affiliated Hospital of Anhui Medical University
500 participants
May 1, 2024
OBSERVATIONAL
Conditions
Summary
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.
Eligibility
Inclusion Criteria3
- 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 Criteria4
- Patients who underwent neoadjuvant therapy or endoscopic treatment
- Insufficient CT imaging or poor CT quality
- Incomplete pathology results
- Presence of metastatic disease
Interventions
The predictive performance of the model was validated in the test set. The optimal prediction model was determined based on the AUC and ACC. To assess the robustness of the chosen model, ROC analysis was conducted on the external validation set.
Locations(1)
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NCT07050576