NCT07509632

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

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 320 (estimated)
• Sponsor: Peking University People's Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Rectal Cancers
• Pathological Complete Response
• Neoadjuvant Chemoradiotherapy
• Machine Learning

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	No interventions	No interventions

Timeline

• Start date: 2026-02-04
• Primary completion: 2026-04-25
• Completion: 2026-05-10
• First posted: 2026-04-03
• Last updated: 2026-04-03

Locations

1 site across 1 country: China