Pelvic CancerMRI-RP-2021NCT04918992
Post Radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
An observational study of Pelvic Cancer, run by Sixth Affiliated Hospital, Sun Yat-sen University at 4 sites in China.
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… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Pelvic Cancer
- What is being tested?
- Artificial Intelligence
- How many people
- 400 participants
- Where
- 4 sitesChina
- Study dates
- 22 Jun 2021to 1 Aug 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- All sexes
- Adult, Older adult
Study population: pelvic cancers who underwent radiotherapy will be enrolled in our study.
Who may be able to join
- 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
Who cannot join
- insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts)
- incomplete radiotherapy
Trial locations and contacts
4 sites in 1 country.
China4 sites
the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510000
Xinjuan Fan, MD
the Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510655
Xinjuan Fan, MD, +86 13602442569, fanxjuan@mail.sysu.edu.cn
Sir Run Run Shaw Hospital
Hangzhou, Zhejiang, 310000
Weidong Han, MD, +86 13819124503, hanwd@zju.edu.cn
The Third Affiliated Hospital of Kunming Medical College
Kunming, Yunnan, 650000
Zhenhui Li, MD, +86 13698736132, lizhenhui621@163.com
About this study
Official title: Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
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.
Read the 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.
- Conditions
- Pelvic Cancer
- Being tested
- Artificial Intelligence
- Keywords
- radiation proctitis, pelvic cancers, Artificial Intelligence
- Medical subject headings
- Pelvic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial IntelligenceDiagnostic test
investigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: baseline
Secondary outcomes (1)
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
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2021
- Study start22 Jun 2021 (estimated)
- Primary completion1 Jun 2024 (estimated)
- Study completion1 Aug 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pelvic CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xinjuan Fan, MD, Study chair, Sixth Affiliated Hospital, Sun Yat-sen UniversityWeidong Han, MD, Principal investigator, Sir Run Run Shaw HospitalZhenhui Li, MD, Principal investigator, The Third Affiliated Hospital of Kunming Medical College.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04918992
- Sponsor's protocol ID
- MRI-RP
- Acronym
- MRI-RP-2021
- Status last verified
- Jun 2021
- First submitted
- 2 Jun 2021
- Last updated
- 9 Jun 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Post-radiotherapy MRI Based AI System to Predict Radiation Proctitis for Pelvic Cancers
- ClinicalTrials.gov ID
- NCT04918992
- Acronym / sponsor ID
- MRI-RP-2021 / MRI-RP
- Conditions
- Pelvic Cancer
- Keywords
- radiation proctitis, pelvic cancers, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 22 Jun 2021 (estimated)
- Primary completion
- 1 Jun 2024 (estimated)
- Study completion
- 1 Aug 2024 (estimated)
- First posted
- 9 Jun 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- pelvic cancers who underwent radiotherapy will be enrolled in our study.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- insufficient imaging quality of MRI (e.g., lack of sequence, motion artifacts)
- incomplete radiotherapy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligenceinvestigators utilize a Artificial Intelligence (AI) supportive system to predict radiation proctitis for patients with pelvic cancers underwent radiotherapy | Diagnostic test | – |
Primary outcome
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis
Time frame: 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
1 secondary outcome
The specificity of AI prediction system in prediction radiation proctitis
Time frame: baseline
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2021.
- Individual participant dataSharing planned: Undecided
Related trials for Pelvic Cancer
Not yet recruitingPhase 1/2
RYZ711 Diagnostic Platform Study
Prostate Cancer (Adenocarcinoma), Adenocarcinoma of the Prostate
RecruitingPhase 2/3
Testing the Addition of MEDI4736 (Durvalumab) to Chemotherapy Before Surgery for Patients With High-Grade Upper Urinary Tract Cancer
Renal Pelvis and Ureter Urothelial Carcinoma
RecruitingPhase 2/3
Testing the Role of DNA Released From Tumor Cells Into the Blood in Guiding the Use of Immunotherapy After Surgical Removal of the Bladder, Kidney, Ureter, and Urethra for Urothelial Cancer Treatment, MODERN Study
Muscle Invasive Bladder Urothelial Carcinoma, Muscle Invasive Renal Pelvis Urothelial Carcinoma
Active, not recruitingPhase 3
Testing the Addition of the Anti-cancer Drug, Cabozantinib, to the Usual Immunotherapy Treatment, Avelumab, in Patients With Metastatic Urothelial Cancer, MAIN-CAV Study
Advanced Bladder Urothelial Carcinoma, Advanced Renal Pelvis Urothelial Carcinoma
Active, not recruitingPhase 3
Testing MK-3475 (Pembrolizumab) After Surgery for Localized Muscle-Invasive Bladder Cancer and Locally Advanced Urothelial Cancer
Localized Renal Pelvis and Ureter Urothelial Carcinoma, Locally Advanced Bladder Urothelial Carcinoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xinjuan Fan, MD study chair
Sixth Affiliated Hospital, Sun Yat-sen University
Weidong Han, MD principal investigator
Sir Run Run Shaw Hospital
Zhenhui Li, MD principal investigator
The Third Affiliated Hospital of Kunming Medical College.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pelvic Cancer, run by Sixth Affiliated Hospital, Sun Yat-sen University at 4 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xinjuan Fan, MD (+86 13602442569, fanxjuan@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system in prediction radiation proctitis (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jun 2021.