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Trial Title:
Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
NCT ID:
NCT05523245
Condition:
Rectal Cancer
Conditions: Official terms:
Rectal Neoplasms
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
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.
Criteria for eligibility:
Study pop:
Patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy and
radical surgery
Sampling method:
Non-Probability Sample
Criteria:
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
Gender:
All
Minimum age:
18 Years
Maximum age:
70 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Sixth Affiliated Hospital, Sun Yat-sen University
Address:
City:
Guangzhou
Country:
China
Status:
Recruiting
Contact:
Last name:
Xiaochun Meng
Phone:
13719166488
Email:
mengxch3@mail.sysu.edu.cn
Facility:
Name:
The First Affiliated Hospital of Jinan University
Address:
City:
Guangzhou
Country:
China
Status:
Not yet recruiting
Facility:
Name:
The Second Affiliated Hospital of Guangzhou Medical University
Address:
City:
Guangzhou
Country:
China
Status:
Not yet recruiting
Facility:
Name:
Fifth Affiliated Hospital, Sun Yat-sen University
Address:
City:
Zhuhai
Country:
China
Status:
Not yet recruiting
Start date:
June 24, 2022
Completion date:
December 2026
Lead sponsor:
Agency:
Sixth Affiliated Hospital, Sun Yat-sen University
Agency class:
Other
Collaborator:
Agency:
Fifth Affiliated Hospital, Sun Yat-Sen University
Agency class:
Other
Collaborator:
Agency:
Second Affiliated Hospital of Guangzhou Medical University
Agency class:
Other
Collaborator:
Agency:
First Affiliated Hospital of Jinan University
Agency class:
Other
Source:
Sixth Affiliated Hospital, Sun Yat-sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05523245
