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NCT Number: NCT07509632

Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning

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).

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Rectal Cancers Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Machine Learning Neoadjuvant Chemoradiotherapy Neoplasms Neoplasms by Site Pathological Complete Response Rectal Diseases Rectal Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University People's Hospital

Beijing, Beijing Municipality, 100044, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

No Interventions

Diagnostic Test

No interventions

Primary outcomes

  1. 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).

Secondary outcomes

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

Sponsors and collaborators

Lead sponsor

Peking University People's Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Apr 3, 2026
Registry last updated
Apr 3, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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