ESCC, Lymph Node Metastasis, Radiomics
Conditions
Keywords
ESCC, Lymph node metastasis, radiomics
Brief 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.
Interventions
DIAGNOSTIC_TESTThe prediction model of lymph node metastasis in early esophageal squamous cell carcinoma
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.
Sponsors
The First Affiliated Hospital of Anhui Medical University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC(the area under the curve) values of the model | 4 years | The performance and clinical relevance of the models were assessed by analyzing the area under the curve (AUC). |
Countries
China
Contacts
Primary ContactHao Zheng, MD
Outcome results
None listed