Pelvic Cancer
Conditions
Keywords
radiation proctitis, pelvic cancers, Artificial Intelligence
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.
Interventions
DIAGNOSTIC_TESTArtificial Intelligence
investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy
Sponsors
Sixth Affiliated Hospital, Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis | baseline | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The specificity of AI prediction system in prediction radiation proctitis | baseline | The specificity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy |
Other
| Measure | Time frame | Description |
|---|---|---|
| The sensitivity of AI prediction system in prediction the radiation proctitis candidates | baseline | The sensitivity of AI prediction system in identifying the radiation proctitis candidates from non-radiation proctitis individuals among pelvic cancers underwent radiotherapy |
Countries
China
Contacts
Primary ContactXinjuan Fan, MD
Outcome results
None listed