Deep Learning, Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response
Conditions
Keywords
Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Deep Learning, Pathological Complete Response
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTThe high-throughput extraction of large amounts of quantitative image features from medical images
The high-throughput extraction of large amounts of quantitative image features from medical images
Sponsors
Tongji Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Pathologically confirmed esophageal squamous cell carcinoma (ESCC). 2. Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy. 3. Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment. 4. Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Exclusion criteria
1. Diagnosis of other malignancies. 2. Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy. 3. Incomplete clinical data. 4. Poor-quality CT imaging.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pathological Complete Response (pCR) Rate | Assessed at the time of surgery, within 1 month post-treatment. | The proportion of patients achieving complete pathological remission after neoadjuvant immunochemotherapy followed by surgery. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV) | At the time of model validation, approximately one year on average after the completion of the research. | 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). |
Countries
China
Contacts
Primary ContactYangkai Li, MD, PhD
Backup ContactLin Zhou, MSc
Outcome results
None listed