Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Pathological Complete Response (pCR) Rate
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •Diagnosis of other malignancies.
- •Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- •Incomplete clinical data.
- •Poor-quality CT imaging.
结局指标
主要结局
Pathological Complete Response (pCR) Rate
时间窗: 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.
次要结局
- 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.)