Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma

ConditionsESCC Lymph Node Metastasis Radiomics

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

AgeNot listed

SponsorThe First Affiliated Hospital of Anhui Medical University

About this trial

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 criteria

This trial does not accept healthy volunteers

Qualifiers

Patients with pathologically confirmed early-stage (T1) ESCC

Preoperative contrast-enhanced CT data within 2 weeks before surgery

Without any treatment before surgical resection

Disqualifiers

Patients who underwent neoadjuvant therapy or endoscopic treatment

Insufficient CT imaging or poor CT quality

Incomplete pathology results

Presence of metastatic disease

Trial population

The radiomics features that affects the prediction of LNM in early-stage ESCC. All patients with early-stage ESCC from the hospitals

Trial design

Design model

Case-control

Time perspective

Retrospective

Treatments tested in this trial

The prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
Diagnostic test
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.

Treatment groups

500 Participants

are divided into 2 treatment groups

Group A: A1 intervention

Group B: B1 intervention

Trial outcomes

Primary outcomes

AUC(the area under the curve) values of the model

The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC).

Time frame

4 years

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The First Affiliated Hospital of Anhui Medical University

Lead sponsor

Source ClinicalTrials.gov

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