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NCT04918992 Clinical Trial

Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers

Pelvic Cancer Clinical Trial

— MRI-RP-2021  

Official title:
Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers

Clinical Trial Details — Status: Not yet recruiting

Administrative data
NCT number NCT04918992
Other study ID # MRI-RP
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date June 22, 2021
Est. completion date August 1, 2024

Study information
Verified date June 2021
Source Sixth Affiliated Hospital, Sun Yat-sen University
Contact Xinjuan Fan, MD
Phone +86 13602442569
Email fanxjuan@mail.sysu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

In this study, investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy. By the system, whether the participants achieve the radiation proctitis will be identified based on the radiomics features extracted from the post radiotherapy Magnetic Resonance Imaging (MRI) . The predictive power to discriminate the radiation proctitis individuals from non-radiation proctitis patients, will be validated in this multicenter, prospective clinical study.

Description:

This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the radiation proctitis in patients with pelvic cancers based on the post-radiotherapy Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as pelvic cancers will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. Patients with pelvic cancers who received radiotherapy will be enrolled and their post-radiotherapy MRI images will be used to predict their radiation proctitis or not. The clinical symptoms, endoscopic findings, imaging and histopathology as a standard. The predictive efficacy will be tested in this multicenter, prospective clinical study.

Recruitment information / eligibility
Status Not yet recruiting
Enrollment 400
Est. completion date August 1, 2024
Est. primary completion date June 1, 2024
Accepts healthy volunteers
Gender All
Age group 18 Years to 75 Years
Eligibility Inclusion Criteria: - pathologically diagnosed as pelvic tumours - intending to receive or undergoing radiotherapy - MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed after radiotherapy Exclusion Criteria: - insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts) - incomplete radiotherapy

Study Design

Related Conditions & MeSH terms

Intervention

Diagnostic Test:
• Artificial Intelligence
investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy

Locations
Country Name City State
China the Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou Guangdong
China the Sixth Affiliated Hospital of Sun Yat-sen University GuangZhou Guangdong
China Sir Run Run Shaw Hospital HangZhou Zhejiang
China The Third Affiliated Hospital of Kunming Medical College Kunming Yunnan

Sponsors (1)
Lead Sponsor Collaborator
Sixth Affiliated Hospital, Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Other The sensitivity of AI prediction system in prediction the radiation proctitis candidates The sensitivity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy baseline
Primary The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy baseline
Secondary The specificity of AI prediction system in prediction radiation proctitis The specificity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy baseline
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