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

Rectal Cancer Clinical Trial

— DLARC  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05523245
• Other study ID #: 2021ZSLYEC-478
• Status: Recruiting
• Start date: June 24, 2022
• Est. completion date: December 2026

Study information

• Verified date: August 2022
• Source: Sixth Affiliated Hospital, Sun Yat-sen University
• Contact: Xiaochun Meng
• Phone: 13719166488
• Email: mengxch3[@]mail.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1700
• Est. completion date: December 2026
• Est. primary completion date: December 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

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

Related Conditions & MeSH terms

• Rectal Cancer
• Rectal Neoplasms

Locations

Country	Name	City	State
China	Sixth Affiliated Hospital, Sun Yat-sen University	Guangzhou	Guangdong
China	The First Affiliated Hospital of Jinan University	Guangzhou	Guangdong
China	The Second Affiliated Hospital of Guangzhou Medical University	Guangzhou	Guangdong
China	Fifth Affiliated Hospital, Sun Yat-sen University	Zhuhai	Guangdong

Sponsors (4)

Lead Sponsor	Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University	Fifth Affiliated Hospital, Sun Yat-Sen University, First Affiliated Hospital of Jinan University, Second Affiliated Hospital of Guangzhou Medical University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response	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.	baseline and pre-operation
Secondary	The specificity of models in prediction tumor response	The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.	baseline and pre-operation
Secondary	The sensitivity of models in prediction tumor response	The sensitivity of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.	baseline and pre-operation
Secondary	The positive predictive value of models in prediction tumor response	The positive predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.	baseline and pre-operation
Secondary	The negative predictive value of models in prediction tumor response	The negative predictive value of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.	baseline and pre-operation