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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.
This multicenter retrospective study will collect chest CT images and clinical data from patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery following neoadjuvant immunochemotherapy between January 2019 and July 2025. Deep learning features will be extracted from the CT images to develop a predictive model of pathological complete response (pCR). The model's performance will be evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Additionally, SHapley Additive exPlanations (SHAP) analysis will be employed to quantify the contribution of CT imaging features to the model's predictions. This study aims to improve early identification of responders to neoadjuvant immunochemotherapy and support personalized treatment strategies for ESCC patients.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ESCC Patients Undergoing Neoadjuvant Immunochemotherapy and Surgery | Patients with esophageal squamous cell carcinoma treated with neoadjuvant immunochemotherapy followed by surgery. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| The high-throughput extraction of large amounts of quantitative image features from medical images | Diagnostic Test | The high-throughput extraction of large amounts of quantitative image features from medical images |
| Measure | Description | Time Frame |
|---|---|---|
| Pathological Complete Response (pCR) Rate | The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery. | Assessed at the time of surgery, within 1 month post-treatment. |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | At the time of model validation, approximately one year on average after the completion of the research. |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yangkai Li, MD, PhD | Contact | +8613995516396 | doclyk@163.com | |
| Lin Zhou, MSc | Contact | zhoul0928@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Yangkai Li, MD, PhD | Tongji Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | Recruiting | Wuhan | Hubei | 430030 | China |
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| ID | Term |
|---|---|
| D000077277 | Esophageal Squamous Cell Carcinoma |
| ID | Term |
|---|---|
| D002294 | Carcinoma, Squamous Cell |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D018307 | Neoplasms, Squamous Cell |
| D004938 | Esophageal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D006258 | Head and Neck Neoplasms |
| D004066 | Digestive System Diseases |
| D004935 | Esophageal Diseases |
| D005767 | Gastrointestinal Diseases |
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