The Application Value of Artificial Intelligence in MRI Precision Diagnosis and Treatment of Bladder Cancer

October 19, 2021 updated by: The First Affiliated Hospital with Nanjing Medical University

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.

Study Overview

Status

Recruiting

Conditions

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 Type

Observational

Enrollment (Anticipated)

150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Jiangsu
      • Nanjing, Jiangsu, China, 210000
        • Recruiting
        • the First Affiliated Hospital of Nanjing Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients receive MRI at each study center and undergo the operation.

Description

Inclusion Criteria:

  1. Preoperative examination prompts the patient to be bladder cancer;
  2. There is no limit on the gender;
  3. The age of 18 years old or more;
  4. Can provide preoperative MRI images;
  5. Agree to provide personal basic clinical information and pathological and imaging data for scientific research, and sign informed consent;
  6. Agree to provide monitoring results during follow-up monitoring for recurrence.

Exclusion Criteria:

  1. Patient was unable to provide preoperative MRI images, including MRI images after neoadjuvant therapy and before surgery；
  2. Patients with incomplete pathological information of samples were unable to provide accurate staging and grading information；
  3. 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;
  4. Patients who had recently undergone surgery (e.g., TURBT) prior to MRI examination；
  5. The researcher thinks there are any conditions that may impair the subject or cause the subject to fail to meet or perform study requirements;
  6. Patients unable to provide written informed consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall survival
Time Frame: 3 years after surgery
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.
3 years after surgery

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
recurrence-free survival
Time Frame: 3 years after surgery
The correlation between artificial intelligence model and RFS 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.
3 years after surgery
progression-free survival
Time Frame: 3 years after surgery
The correlation between artificial intelligence model and PFS 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.
3 years after surgery

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The First Affiliated Hospital with Nanjing Medical University

Collaborators

Nanjing University of Aeronautics and Astronautics

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2021

Primary Completion (Anticipated)

May 1, 2022

Study Completion (Anticipated)

January 1, 2023

Study Registration Dates

First Submitted

October 7, 2021

First Submitted That Met QC Criteria

October 19, 2021

First Posted (Actual)

October 27, 2021

Study Record Updates

Last Update Posted (Actual)

October 27, 2021

Last Update Submitted That Met QC Criteria

October 19, 2021

Last Verified

October 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2021-SR-409

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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