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

NCT05523245 | Recruiting

• 1 other identifier: 2021ZSLYEC-478
• Study Type: observational
• Target: 1,700
• Locations: 1 country
• Sites: 4

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.

Conditions

Rectal Cancer

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• The specificity of models in prediction tumor response

  baseline and pre-operation

• The sensitivity of models in prediction tumor response

  baseline and pre-operation

• The positive predictive value of models in prediction tumor response

  baseline and pre-operation

• The negative predictive value of models in prediction tumor response

  baseline and pre-operation

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• Clinical suspicion or colonoscopic pathology of rectal cancer
• Age over 18 years
• Informed consent and signed informed consent form

You may not qualify if:

• Poor magnetic resonance image quality, such as severe artifacts
• Previous treatment for rectal cancer
• History or combination of other malignant tumours
• Not Locally Advanced Rectal Cancer (LARC)
• Not received neoadjuvant therapy or not completed neoadjuvant therapy
• No surgery
• Time interval between MRI and surgery was more than 2 weeks
• Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons

Sponsors & Collaborators

• Sixth Affiliated Hospital, Sun Yat-sen University: lead
• Fifth Affiliated Hospital, Sun Yat-Sen University: collaborator
• Second Affiliated Hospital of Guangzhou Medical University: collaborator
• First Affiliated Hospital of Jinan University: collaborator

Study Sites (4)

Sixth Affiliated Hospital, Sun Yat-sen University

Guangzhou, Guangdong, China

RECRUITING

The First Affiliated Hospital of Jinan University

Guangzhou, Guangdong, China

NOT YET RECRUITING

The Second Affiliated Hospital of Guangzhou Medical University

Guangzhou, Guangdong, China

NOT YET RECRUITING

Fifth Affiliated Hospital, Sun Yat-sen University

Zhuhai, Guangdong, China

NOT YET RECRUITING

MeSH Terms

Conditions

Rectal Neoplasms

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Diseases; Rectal Diseases

Central Study Contacts

Xiaochun Meng

CONTACT

13719166488; mengxch3@mail.sysu.edu.cn

Peiyi Xie

CONTACT

13724071514; xiepy6@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: The Director of Diagnostic Radiology Department

Study Record Dates

• First Submitted: August 29, 2022
• First Posted: August 31, 2022
• Study Start: June 24, 2022
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2027
• Last Updated: April 23, 2026

Record last verified: 2026-04

Locations

CN (4)