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

• Conditions: Pelvic Cancer

• Registration Number: NCT04918992
• Lead Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

Brief 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.

Detailed 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.

Study Timeline

• Status Verified: 2021-06
• Study First Submitted: 2021-06-02
• Study First Submitted QC: 2021-06-08
• Last Update Submitted: 2021-06-08
• Study First Posted: 2021-06-09
• Last Update Posted: 2021-06-09
• Study Start: 2021-06-22
• Primary Completion: 2024-06-01
• Study Completion: 2024-08-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 400

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

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis	baseline	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

Secondary Outcome Measures

Name	Time	Method
The specificity of AI prediction system in prediction radiation proctitis	baseline	The specificity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy

Trial Locations

Locations (3)

the Sixth Affiliated Hospital of Sun Yat-sen University; 🇨🇳 GuangZhou, Guangdong, China

The Third Affiliated Hospital of Kunming Medical College; 🇨🇳 Kunming, Yunnan, China

Sir Run Run Shaw Hospital; 🇨🇳 HangZhou, Zhejiang, China