Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning

NCT07509632 · tracked via the Priya Life Science China tracker

Sponsor

Peking University People's Hospital

Phase

Observational

Started

2026-02-04

Last updated

2026-04-03

Condition(s) studied

Rectal CancersPathological Complete ResponseNeoadjuvant ChemoradiotherapyMachine Learning

Investigational drug(s) / intervention(s)

No interventions

No interventions: No interventions

Study 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).

Eligibility

Sex

ALL

Min age

18 Years

Max age

—

Healthy volunteers

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.

Primary outcome measure(s)

Pathological Complete Response (pCR) defined by Tumor Regression Grade (TRG) — 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).

Trial sites (1)

Facility	City	Region	Status
Peking University People's Hospital	Beijing	Beijing Municipality

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Official registry record

This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.

View NCT07509632 on ClinicalTrials.gov ↗ ← All trials in China