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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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No interventions | Diagnostic Test | No interventions |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery). |
| Measure | Description | Time Frame |
|---|---|---|
| Area under the receiver operating characteristic curve (AUC) of the prediction model | To evaluate the discrimination performance of the model for pCR prediction | At the completion of model development and validation |
| Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model |
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Inclusion Criteria:
Exclusion Criteria:
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Patients diagnosed with locally advanced or metastatic rectal cancer who underwent standard radical surgery following neoadjuvant chemoradiotherapy
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| Name | Affiliation | Role |
|---|---|---|
| Hong-Peng Jiang, docter | Peking University People's Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Peking University People's Hospital | Beijing | Beijing Municipality | 100044 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 29582537 | Background | Kong JC, Guerra GR, Warrier SK, Lynch AC, Michael M, Ngan SY, Phillips W, Ramsay G, Heriot AG. Prognostic value of tumour regression grade in locally advanced rectal cancer: a systematic review and meta-analysis. Colorectal Dis. 2018 Jul;20(7):574-585. doi: 10.1111/codi.14106. Epub 2018 May 8. | |
| 20692872 | Background |
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| ID | Term |
|---|---|
| D012004 | Rectal Neoplasms |
| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
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To evaluate the diagnostic accuracy of the model at the optimal cut-off value |
| 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 | 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 | 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 | At the completion of model development and validation |
| Maas M, Nelemans PJ, Valentini V, Das P, Rodel C, Kuo LJ, Calvo FA, Garcia-Aguilar J, Glynne-Jones R, Haustermans K, Mohiuddin M, Pucciarelli S, Small W Jr, Suarez J, Theodoropoulos G, Biondo S, Beets-Tan RG, Beets GL. Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol. 2010 Sep;11(9):835-44. doi: 10.1016/S1470-2045(10)70172-8. Epub 2010 Aug 6. |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |