Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers (MRI-RP-2021)

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Pelvic Cancer
• Intervention / Treatment: Diagnostic test: Artificial Intelligence

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 Type: Observational
• Enrollment (Anticipated): 400

Contacts and Locations

• Study Contact: Name: Xinjuan Fan, MD; Phone Number: +86 13602442569; Email: fanxjuan@mail.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • GuangZhou, Guangdong, China, 510655
      • The sixth affiliated hospital of Sun Yat-Sen University
      • Contact: Xinjuan Fan, MD; Phone Number: +86 13602442569; Email: fanxjuan@mail.sysu.edu.cn
    • Guangzhou, Guangdong, China, 510000
      • The sixth affiliated hospital of Sun Yat-Sen University
      • Contact: Xinjuan Fan, MD
  • Yunnan
    • Kunming, Yunnan, China, 650000
      • The Third Affiliated Hospital of Kunming Medical College
  • Zhejiang
    • HangZhou, Zhejiang, China, 310000
      • Sir Run Run Shaw Hospital
      • Contact: Weidong Han, MD; Phone Number: +86 13819124503; Email: hanwd@zju.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

pelvic cancers who underwent radiotherapy will be enrolled in our study.

Description

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 Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
• Investigators: Principal Investigator: Zhenhui Li, MD, The Third Affiliated Hospital of Kunming Medical College.; Study Chair: Xinjuan Fan, MD, Sixth Affiliated Hospital, Sun Yat-sen University; Principal Investigator: Weidong Han, MD, Sir Run Run Shaw Hospital

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): June 22, 2021
• Primary Completion (ANTICIPATED): June 1, 2024
• Study Completion (ANTICIPATED): August 1, 2024

Study Registration Dates

• First Submitted: June 2, 2021
• First Submitted That Met QC Criteria: June 8, 2021
• First Posted (ACTUAL): June 9, 2021

Study Record Updates

• Last Update Posted (ACTUAL): June 9, 2021
• Last Update Submitted That Met QC Criteria: June 8, 2021
• Last Verified: June 1, 2021

More Information

Terms related to this study

• Keywords: Artificial Intelligence; radiation proctitis; pelvic cancers
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Diseases; Gastroenteritis; Intestinal Diseases; Rectal Diseases; Pelvic Neoplasms; Proctitis
• Other Study ID Numbers: MRI-RP

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No