Machine Learning, Neoadjuvant Chemoradiotherapy, Pathological Complete Response, Rectal Cancers
Conditions
Brief summary
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).
Detailed description
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.
Interventions
DIAGNOSTIC_TESTNo interventions
No interventions
Sponsors
Peking University People's Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Patients with histopathologically confirmed rectal adenocarcinoma; 2. Clinical stage cT3-4, or cN+, or M1 advanced rectal cancer; 3. Received standardized neoadjuvant chemoradiotherapy or neoadjuvant chemotherapy; 4. Underwent total mesorectal excision (TME) after the completion of neoadjuvant therapy, with complete postoperative pathological data available.
Exclusion criteria
1. Previous history of other malignant tumors; 2. Incomplete clinical data; 3. Underwent emergency surgery during nCRT; 4. Complicated with systemic infection or hematological diseases.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG) | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area under the receiver operating characteristic curve (AUC) of the prediction model | At the completion of model development and validation | To evaluate the discrimination performance of the model for pCR prediction |
| Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model | At the completion of model development and validation | To evaluate the diagnostic accuracy of the model at the optimal cut-off value |
| Calibration curve of the prediction model | At the completion of model development and validation | To evaluate the consistency between the predicted pCR probability and the actual observed pCR rate |
| Net benefit of the model quantified by decision curve analysis (DCA) | At the completion of model development and validation | To evaluate the clinical utility of the model across different threshold probabilities |
| Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis | At the completion of model development and validation | To interpret the contribution of each predictor to the model prediction |
Countries
China
Contacts
STUDY_CHAIRHong-Peng Jiang, docter
Peking University People's Hospital
Outcome results
None listed