Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning
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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Locations
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Beijing Municipality
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Beijing, Beijing Municipality, China, 100044
- Peking University People's Hospital
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- Previous history of other malignant tumors;
- Incomplete clinical data;
- Underwent emergency surgery during nCRT;
- Complicated with systemic infection or hematological diseases.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG)
Time Frame: Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
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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).
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Evaluated during routine histopathological examination of the resected surgical specimen immediately following radical surgery (typically within 1 to 2 weeks post-surgery).
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Area under the receiver operating characteristic curve (AUC) of the prediction model
Time Frame: At the completion of model development and validation
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To evaluate the discrimination performance of the model for pCR prediction
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At the completion of model development and validation
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Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction model
Time Frame: At the completion of model development and validation
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To evaluate the diagnostic accuracy of the model at the optimal cut-off value
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At the completion of model development and validation
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Calibration curve of the prediction model
Time Frame: At the completion of model development and validation
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To evaluate the consistency between the predicted pCR probability and the actual observed pCR rate
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At the completion of model development and validation
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Net benefit of the model quantified by decision curve analysis (DCA)
Time Frame: At the completion of model development and validation
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To evaluate the clinical utility of the model across different threshold probabilities
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At the completion of model development and validation
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Variable importance quantified by SHapley Additive exPlanations (SHAP) analysis
Time Frame: At the completion of model development and validation
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To interpret the contribution of each predictor to the model prediction
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At the completion of model development and validation
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Chair: Hong-Peng Jiang, docter, Peking University People's Hospital
Publications and helpful links
General Publications
- 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.
- 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.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2026PHB131-001
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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