Bladder CancerNCT05096533
The Application Value of Artificial Intelligence in MRI Precision Diagnosis and Treatment of Bladder Cancer
An observational study of Bladder Cancer, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
Trial summary
This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bladder Cancer
- How many people
- 150 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 1 Jan 2023
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients receive MRI at each study center and undergo the operation.
Who may be able to join
- Preoperative examination prompts the patient to be bladder cancer;
- There is no limit on the gender;
- The age of 18 years old or more;
- Can provide preoperative MRI images;
- Agree to provide personal basic clinical information and pathological and imaging data for scientific research, and sign informed consent;
- Agree to provide monitoring results during follow-up monitoring for recurrence.
Who cannot join
- Patient was unable to provide preoperative MRI images, including MRI images after neoadjuvant therapy and before surgery；
- Patients with incomplete pathological information of samples were unable to provide accurate staging and grading information；
- Patients cannot be operated on due to their own reasons: severe heart failure, acute myocardial infarction, severe heart and lung diseases, etc., they cannot tolerate normal surgical treatment;
- Patients who had recently undergone surgery (e.g., TURBT) prior to MRI examination；
- The researcher thinks there are any conditions that may impair the subject or cause the subject to fail to meet or perform study requirements;
- Patients unable to provide written informed consent.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The first affiliated hospital of Nanjing Medical University
Nanjing, Jiangsu, 210000
Qiang Lu, 13505196501, dxhlvqiang@163.com
Xiao Yang, 13951813528, yangxiao2915@163.com
About this study
Official title: Prospective Multi-center Clinical Study on the Application Value of Artificial Intelligence in MRI Precision Diagnosis and Treatment of Bladder Cancer
This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
Read the detailed description
Preliminary research: This research is multi-disciplinary joint research by combining artificial intelligence with magnetic resonance, it can make the preoperative determination of bladder cancer stage more accurate and guides the clinician worker's treatment plan. At present, It has been constructed that an artificial intelligence model based on preoperative magnetic resonance images to predict staging and patient prognosis. We built a staging prediction model through deep learning artificial intelligence network, and collected magnetic resonance image data and related postoperative pathological data of patients, afterwards, We followed 576 patients on the basis of staging model construction. By obtaining OS, PFS, and RFS of patients, a part was randomly selected as a training set for training the deep learning network model. The other part is used as a test set to verify its accuracy. This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
- Conditions
- Bladder Cancer
- Keywords
- Bladder Cancer, MRI, Artificial intelligence
- Medical subject headings
- Urinary Bladder Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
To explore the application value of artificial intelligence in the precise diagnosis and treatment of bladder tumor, and to improve the accuracy of MRI diagnosis of bladder cancer stage and grade through artificial intelligence.
2、Through Concordance analysis of artificial intelligence diagnosis assay results with gold standard results of surgery, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of artificial intelligence diagnosis before the operation.
Measured: 1 year
Secondary outcomes (1)
Overall survival
The correlation between artificial intelligence model and OS in bladder cancer patients was analyzed to preliminarily verify the potential ability of artificial intelligence model results in predicting the prognosis of bladder cancer patients.
Measured: 3 years after surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Oct 2021
- Study start1 Jan 2021
- Primary completion1 May 2022 (estimated)
- Study completion1 Jan 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Bladder CancerStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital with Nanjing Medical UniversityThe sponsor's other registered studies.
- 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
- 150 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Nanjing University of Aeronautics and Astronautics
- Responsible party
- Sponsor
- Investigators
- Qiang Lv, MD，PHD, Principal investigator, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05096533
- Sponsor's protocol ID
- 2021-SR-409
- Status last verified
- Oct 2021
- First submitted
- 7 Oct 2021
- Last updated
- 27 Oct 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Panebianco V, Narumi Y, Altun E, Bochner BH, Efstathiou JA, Hafeez S, Huddart R, Kennish S, Lerner S, Montironi R, Muglia VF, Salomon G, Thomas S, Vargas HA, Witjes JA, Takeuchi M, Barentsz J, Catto JWF. Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System). Eur Urol. 2018 Sep;74(3):294-306. doi: 10.1016/j.eururo.2018.04.029. Epub 2018 May 10.
PubMed 29755006Wang H, Luo C, Zhang F, Guan J, Li S, Yao H, Chen J, Luo J, Chen L, Guo Y. Multiparametric MRI for Bladder Cancer: Validation of VI-RADS for the Detection of Detrusor Muscle Invasion. Radiology. 2019 Jun;291(3):668-674. doi: 10.1148/radiol.2019182506. Epub 2019 Apr 23.
PubMed 31012814Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 2020 Oct;38(10):2329-2347. doi: 10.1007/s00345-019-03000-5. Epub 2019 Nov 5.
PubMed 31691082
Study at a glance
From the registry record, last updated by the sponsor on 27 Oct 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Multi-center Clinical Study on the Application Value of Artificial Intelligence in MRI Precision Diagnosis and Treatment of Bladder Cancer
- ClinicalTrials.gov ID
- NCT05096533
- Acronym / sponsor ID
- 2021-SR-409
- Conditions
- Bladder Cancer
- Keywords
- Bladder Cancer, MRI, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical UniversityUniversity, hospital or other
- Collaborators
- Nanjing University of Aeronautics and Astronautics
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 1 May 2022 (estimated)
- Study completion
- 1 Jan 2023 (estimated)
- First posted
- 27 Oct 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.
This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
Detailed description
Preliminary research: This research is multi-disciplinary joint research by combining artificial intelligence with magnetic resonance, it can make the preoperative determination of bladder cancer stage more accurate and guides the clinician worker's treatment plan. At present, It has been constructed that an artificial intelligence model based on preoperative magnetic resonance images to predict staging and patient prognosis. We built a staging prediction model through deep learning artificial intelligence network, and collected magnetic resonance image data and related postoperative pathological data of patients, afterwards, We followed 576 patients on the basis of staging model construction. By obtaining OS, PFS, and RFS of patients, a part was randomly selected as a training set for training the deep learning network model. The other part is used as a test set to verify its accuracy. This study was a prospective, multicenter observational clinical study, A total of 150 patients with bladder malignant tumor who was admitted to the urology department of each center for treatment and underwent electric resection or radical cystectomy were planned to be enrolled. In order to analyze the sensitivity、specificity and accuracy of artificial intelligence in predicting postoperative pathological staging, Patients who entered the group were followed up for 3 years, then, we analyzed the correlation between artificial intelligence prediction results and patient OS PFS RFS. It was preliminarily verified that the results of the artificial intelligence model have the potential to predict the prognosis of patients with bladder cancer.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients receive MRI at each study center and undergo the operation.
Key inclusion criteria (6 listed)
- Preoperative examination prompts the patient to be bladder cancer;
- There is no limit on the gender;
- The age of 18 years old or more;
- Can provide preoperative MRI images;
- Agree to provide personal basic clinical information and pathological and imaging data for scientific research, and sign informed consent;
Key exclusion criteria (6 listed)
- Patient was unable to provide preoperative MRI images, including MRI images after neoadjuvant therapy and before surgery；
- Patients with incomplete pathological information of samples were unable to provide accurate staging and grading information；
- Patients cannot be operated on due to their own reasons: severe heart failure, acute myocardial infarction, severe heart and lung diseases, etc., they cannot tolerate normal surgical treatment;
- Patients who had recently undergone surgery (e.g., TURBT) prior to MRI examination；
- The researcher thinks there are any conditions that may impair the subject or cause the subject to fail to meet or perform study requirements;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To explore the application value of artificial intelligence in the precise diagnosis and treatment of bladder tumor, and to improve the accuracy of MRI diagnosis of bladder cancer stage and grade through artificial intelligence.
Time frame: 1 year
2、Through Concordance analysis of artificial intelligence diagnosis assay results with gold standard results of surgery, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of artificial intelligence diagnosis before the operation.
1 secondary outcome
Overall survival
Time frame: 3 years after surgery
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Oct 2021.
- Background: PubMed 29755006Panebianco V, Narumi Y, Altun E, Bochner BH, Efstathiou JA, Hafeez S, Huddart R, Kennish S, Lerner S, Montironi R, Muglia VF, Salomon G, Thomas S, Vargas HA, Witjes JA, Takeuchi M, Barentsz J, Catto JWF. Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System). Eur Urol. 2018 Sep;74(3):294-306. doi: 10.1016/j.eururo.2018.04.029. Epub 2018 May 10.
- Background: PubMed 31012814Wang H, Luo C, Zhang F, Guan J, Li S, Yao H, Chen J, Luo J, Chen L, Guo Y. Multiparametric MRI for Bladder Cancer: Validation of VI-RADS for the Detection of Detrusor Muscle Invasion. Radiology. 2019 Jun;291(3):668-674. doi: 10.1148/radiol.2019182506. Epub 2019 Apr 23.
- Background: PubMed 31691082Suarez-Ibarrola R, Hein S, Reis G, Gratzke C, Miernik A. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urol. 2020 Oct;38(10):2329-2347. doi: 10.1007/s00345-019-03000-5. Epub 2019 Nov 5.
- Individual participant dataSharing planned: No
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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.
Qiang Lv, MD，PHD principal investigator
The First Affiliated Hospital with Nanjing Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bladder Cancer, run by The First Affiliated Hospital with Nanjing Medical University at 1 site 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 6 inclusion and 6 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 27 Oct 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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 Lingkai Cai (+86 15206213500, lingkaicai1996@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To explore the application value of artificial intelligence in the precise diagnosis and treatment of bladder tumor, and to improve the accuracy of MRI diagnosis of bladder cancer stage and grade through artificial intelligence. (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05096533, which also lists 3 linked publications.
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 27 Oct 2021.