Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI

Recruiting

• Conditions: Rectal Cancer

• Registration Number: NCT05523245
• Lead Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

Brief Summary

Establish a deep learning model based on multi-parameter magnetic resonance imaging to predict the efficacy of neoadjuvant therapy for locally advanced rectal cancer.This study intends to combine DCE with conventional MRI images for DL, establish a multi-parameter MRI model for predicting the efficacy of CRT, and compare it with the DL and non-artificial quantitative MRI diagnostic model constructed by conventional MRI to evaluate the role of DL in MRI predicting CRT. And this study also tries to build a DL platform to assess the efficacy of LARC neoadjuvant radiotherapy and chemotherapy, accurately assess patients' complete respose (pCR) after CRT, and provide an important basis for guiding clinical decision-making.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-06-24
• Status Verified: 2022-08
• Study First Submitted: 2022-08-29
• Study First Submitted QC: 2022-08-29
• Last Update Submitted: 2022-08-29
• Study First Posted: 2022-08-31
• Last Update Posted: 2022-08-31
• Primary Completion: 2025-12
• Study Completion: 2026-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1700

Inclusion Criteria

• Pathologically proved rectal adenocarcinoma
• The first MRI diagnosis was locally advanced rectal cancer (LARC)
• Age 18-70
• Underwent magnetic resonance examinations twice
• Preoperative neoadjuvant chemoradiotherapy was completed
• Complete total mesangial resection of rectal cancer and postoperative pathological examination
• Informed consent and signed informed consent form

Exclusion Criteria

• Poor magnetic resonance image quality, such as severe artifacts
• Cases complicated with intestinal obstruction or perforation requiring emergency surgical treatment
• Previous treatment for rectal cancer
• A history of other malignant tumors
• A history of abdominal and pelvic surgery
• Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response	baseline and pre-operation	The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.

Secondary Outcome Measures

Name	Time	Method
The positive predictive value of models in prediction tumor response	baseline and pre-operation	The positive predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The negative predictive value of models in prediction tumor response	baseline and pre-operation	The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The specificity of models in prediction tumor response	baseline and pre-operation	The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
The sensitivity of models in prediction tumor response	baseline and pre-operation	The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.

Trial Locations

Locations (4)

The First Affiliated Hospital of Jinan University; 🇨🇳 Guangzhou, Guangdong, China

Sixth Affiliated Hospital, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China

Fifth Affiliated Hospital, Sun Yat-sen University; 🇨🇳 Zhuhai, Guangdong, China

The Second Affiliated Hospital of Guangzhou Medical University; 🇨🇳 Guangzhou, Guangdong, China