This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.
Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Diagnosis of other malignancies.
Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
Incomplete clinical data.
Poor-quality CT imaging.
Patients diagnosed with esophageal squamous cell carcinoma who received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy, with available contrast-enhanced chest CT scans before and after neoadjuvant treatment, and who underwent surgical resection.
Cohort
Retrospective
The high-throughput extraction of large amounts of quantitative image features from medical images
Diagnostic testThe high-throughput extraction of large amounts of quantitative image features from medical images
Pathological Complete Response (pCR) Rate
The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery.
Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV)
Evaluation of the deep learning model's predictive performance using receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
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