Development and Validation of a Machine Learning Model Based on Clinical and MRI Features for Predicting Pathological Complete Response in Rectal Cancer Following Neoadjuvant Chemoradiotherapy
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 320
- 试验地点
- 1
- 主要终点
- Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
研究概览
简要总结
This study aims to develop and validate a robust machine learning-based prediction model utilizing baseline clinical data and magnetic resonance imaging (MRI) features. The objective is to preoperatively predict the probability of achieving a pathological complete response (pCR) in patients with locally advanced rectal cancer (CRC) following neoadjuvant chemoradiotherapy (nCRT).
详细描述
This study aims to develop and validate a predictive model based on pre-neoadjuvant clinical, laboratory, and magnetic resonance imaging (MRI) features to estimate the probability of pathological complete response (pCR) in rectal cancer patients after neoadjuvant chemoradiotherapy (nCRT). This retrospective study will enroll patients who received nCRT followed by radical resection at Peking University People's Hospital between December 2017 and October 2025 as the development cohort. Least Absolute Shrinkage and Selection Operator (LASSO) regression will be used for feature selection, and machine learning algorithms will be applied to construct the prediction model. Model performance will be comprehensively evaluated using the receiver operating characteristic (ROC) curve, precision-recall curve, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis will be performed to enhance model interpretability. The final model is expected to provide an individualized pCR prediction tool to guide clinical decision-making for rectal cancer patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 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
- •Patients with histopathologically confirmed rectal adenocarcinoma;
- •Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer;
- •Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy;
- •Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available.
排除标准
- •Previous history of other malignant tumors;
- •Incomplete clinical data;
- •Underwent emergency surgery during nCRT;
- •Complicated with systemic infection or hematological diseases.
结局指标
主要结局
Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
时间窗: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
The primary endpoint is the occurrence of pCR, assessed by two independent pathologists using the AJCC/CAP Tumor Regression Grade (TRG) system. TRG 0 (no viable cancer cells, only fibrosis or mucin pools) is defined as a positive outcome (pCR). TRG 1 to 3 are combined and defined as a negative outcome (non-pCR). The predictive performance of the model will be evaluated utilizing several metrics including the Area Under the ROC Curve (AUC), Precision-Recall (PR) curve, Calibration curve, and Decision Curve Analysis (DCA).
次要结局
- Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis(At the completion of model development and validation)
- Area under the receiver operating characteristic curve (AUC) of the prediction model(At the completion of model development and validation)
- Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model(At the completion of model development and validation)
- Calibration curve of the prediction model(At the completion of model development and validation)
- Net benefit of the model quantified by decision curve analysis (DCA)(At the completion of model development and validation)
研究者
Hongpeng Jiang
docter
Peking University People's Hospital