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
• Status: Not yet recruiting
• 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
• 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
• 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

Related Conditions & MeSH terms

• Pelvic Cancer
• Pelvic Neoplasms
• Proctitis

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