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

NCT ID: NCT04918992

Last Updated: 2021-06-09

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-22
• Study Completion Date: 2024-08-01

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.

Conditions

Pelvic Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

Artificial Intelligence

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Sixth Affiliated Hospital, Sun Yat-sen University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Xinjuan Fan, MD

Role: STUDY_CHAIR

Sixth Affiliated Hospital, Sun Yat-sen University

Weidong Han, MD

Role: PRINCIPAL_INVESTIGATOR

Sir Run Run Shaw Hospital

Zhenhui Li, MD

Role: PRINCIPAL_INVESTIGATOR

The Third Affiliated Hospital of Kunming Medical College.

Locations

the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, China

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

Countries

China

Central Contacts

Xinjuan Fan, MD

Role: CONTACT

fanxjuan@mail.sysu.edu.cn

+86 13602442569

Facility Contacts

Xinjuan Fan, MD

Role: primary

Xinjuan Fan, MD

Role: primary

fanxjuan@mail.sysu.edu.cn

+86 13602442569

Zhenhui Li, MD

Role: primary

lizhenhui621@163.com

+86 13698736132

Weidong Han, MD

Role: primary

hanwd@zju.edu.cn

+86 13819124503

Other Identifiers

MRI-RP

Identifier Type: -

Identifier Source: org_study_id