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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Pelvic Cancer

Intervention / Treatment
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* Diagnostic Test: Artificial Intelligence


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Diagnostic Test: Artificial Intelligence|investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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radiation proctitis, pelvic cancers, Artificial Intelligence