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

NCT04918992 | Unknown

• 1 other identifier: MRI-RP
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 4

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.

Conditions

Pelvic Cancer

Keywords

radiation proctitis; pelvic cancers; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• The specificity of AI prediction system in prediction radiation proctitis

  baseline

Other Outcomes (1)

• The sensitivity of AI prediction system in prediction the radiation proctitis candidates

  baseline

Interventions

Artificial Intelligence DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

• 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

You may not qualify if:

• insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts)
• incomplete radiotherapy

Sponsors & Collaborators

• Sixth Affiliated Hospital, Sun Yat-sen University: lead

Study Sites (4)

the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510000, China

Location

the Sixth Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, 510655, China

Location

The Third Affiliated Hospital of Kunming Medical College

Kunming, Yunnan, 650000, China

Location

Sir Run Run Shaw Hospital

Hangzhou, Zhejiang, 310000, China

Location

MeSH Terms

Conditions

Pelvic Neoplasms

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Xinjuan Fan, MD

  Sixth Affiliated Hospital, Sun Yat-sen University

  STUDY CHAIR

• Weidong Han, MD

  Sir Run Run Shaw Hospital

  PRINCIPAL INVESTIGATOR

• Zhenhui Li, MD

  The Third Affiliated Hospital of Kunming Medical College.

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xinjuan Fan, MD

CONTACT

+86 13602442569; fanxjuan@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 2, 2021
• First Posted: June 9, 2021
• Study Start: June 22, 2021
• Primary Completion: June 1, 2024
• Study Completion: August 1, 2024
• Last Updated: June 9, 2021

Record last verified: 2021-06

Locations

CN (4)