Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning

March 29, 2026 updated by: Hongpeng Jiang, Peking University People's Hospital

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

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).

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

320

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Beijing Municipality
      • Beijing, Beijing Municipality, China, 100044
        • Peking University People's Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients diagnosed with locally advanced or metastatic rectal cancer who underwent standard radical surgery following neoadjuvant chemoradiotherapy

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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).
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).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under the receiver operating characteristic curve (AUC) of the prediction model
Time Frame: At the completion of model development and validation
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
Time Frame: At the completion of model development and validation
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
Time Frame: At the completion of model development and validation
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)
Time Frame: At the completion of model development and validation
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
Time Frame: At the completion of model development and validation
To interpret the contribution of each predictor to the model prediction
At the completion of model development and validation

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking University People's Hospital

Investigators

  • Study Chair: Hong-Peng Jiang, docter, Peking University People's Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 4, 2026

Primary Completion (Estimated)

April 25, 2026

Study Completion (Estimated)

May 10, 2026

Study Registration Dates

First Submitted

March 29, 2026

First Submitted That Met QC Criteria

March 29, 2026

First Posted (Actual)

April 3, 2026

Study Record Updates

Last Update Posted (Actual)

April 3, 2026

Last Update Submitted That Met QC Criteria

March 29, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026PHB131-001

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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