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).
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.
Study Type
OBSERVATIONAL
Enrollment
320
No interventions
Peking University People's Hospital
Beijing, Beijing Municipality, China
Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
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).
Time frame: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
Area under the receiver operating characteristic curve (AUC) of the prediction model
To evaluate the discrimination performance of the model for pCR prediction
Time frame: At the completion of model development and validation
Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model
To evaluate the diagnostic accuracy of the model at the optimal cut-off value
Time frame: At the completion of model development and validation
Calibration curve of the prediction model
To evaluate the consistency between the predicted pCR probability and the actual observed pCR rate
Time frame: At the completion of model development and validation
Net benefit of the model quantified by decision curve analysis (DCA)
To evaluate the clinical utility of the model across different threshold probabilities
Time frame: At the completion of model development and validation
Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis
To interpret the contribution of each predictor to the model prediction
Time frame: At the completion of model development and validation
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