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

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.

Study Overview

• Status: Recruiting
• Conditions: Rectal Cancer

• Study Type: Observational
• Enrollment (Anticipated): 1700

Contacts and Locations

• Study Contact: Name: Xiaochun Meng; Phone Number: 13719166488; Email: mengxch3@mail.sysu.edu.cn
• Study Contact Backup: Name: Peiyi Xie; Phone Number: 13724071514; Email: xiepy6@mail.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China
      • Not yet recruiting
      • The First Affiliated Hospital of Jinan University
    • Guangzhou, Guangdong, China
      • Recruiting
      • Sixth Affiliated Hospital, Sun Yat-sen University
      • Contact: Xiaochun Meng; Phone Number: 13719166488; Email: mengxch3@mail.sysu.edu.cn
    • Guangzhou, Guangdong, China
      • Not yet recruiting
      • The second Affiliated Hospital of Guangzhou Medical University
    • Zhuhai, Guangdong, China
      • Not yet recruiting
      • Fifth Affiliated Hospital, Sun Yat-Sen University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 70 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and radical surgery

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
complete response; Patients receiving neoadjuvant therapy achieved pathological complete response before LARC.
non complete response; Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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
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
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

Collaborators and Investigators

• Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
• Collaborators: Fifth Affiliated Hospital, Sun Yat-Sen University; First Affiliated Hospital of Jinan University; Second Affiliated Hospital of Guangzhou Medical University

Study record dates

Study Major Dates

• Study Start (Actual): June 24, 2022
• Primary Completion (Anticipated): December 1, 2025
• Study Completion (Anticipated): December 1, 2026

Study Registration Dates

• First Submitted: August 29, 2022
• First Submitted That Met QC Criteria: August 29, 2022
• First Posted (Actual): August 31, 2022

Study Record Updates

• Last Update Posted (Actual): August 31, 2022
• Last Update Submitted That Met QC Criteria: August 29, 2022
• Last Verified: August 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Intestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Colorectal Neoplasms; Rectal Neoplasms
• Other Study ID Numbers: 2021ZSLYEC-478

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No