Clinical trial NCT04918992: Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
Official title: Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Pelvic Cancer
Keywords: radiation proctitis
pelvic cancers
Artificial Intelligence
Lead sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Sponsor class: OTHER
First posted: 2021-06-09
Enrollment: 400
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: pelvic cancers who underwent radiotherapy will be enrolled in our study.

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.

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

Primary outcomes:
measure: The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis
timeFrame: baseline
Secondary outcomes:
measure: The specificity of AI prediction system in prediction radiation proctitis
timeFrame: baseline
Other outcomes:
measure: The sensitivity of AI prediction system in prediction the radiation proctitis candidates
timeFrame: baseline
Locations:
city: Guangzhou
country: China
facility: the Sixth Affiliated Hospital of Sun Yat-sen University
state: Guangdong
city: GuangZhou
country: China
facility: the Sixth Affiliated Hospital of Sun Yat-sen University
state: Guangdong
city: Kunming
country: China
facility: The Third Affiliated Hospital of Kunming Medical College
state: Yunnan
city: HangZhou
country: China
facility: Sir Run Run Shaw Hospital
state: Zhejiang
Study officials:
affiliation: Sixth Affiliated Hospital, Sun Yat-sen University
name: Xinjuan Fan, MD
role: STUDY_CHAIR
affiliation: Sir Run Run Shaw Hospital
name: Weidong Han, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Third Affiliated Hospital of Kunming Medical College.
name: Zhenhui Li, MD
role: PRINCIPAL_INVESTIGATOR
References: