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.
Study Type
OBSERVATIONAL
Enrollment
500
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.
The First Affiliated Hospital of Anhui Medical University
Hefei, Anhui, China
RECRUITINGAUC(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
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.