MedDataX Logo

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

NCT ID: NCT05096533

Last Updated: 2021-10-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

150 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-01-01

Study Completion Date

2023-01-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

See the medical conditions and disease areas that this research is targeting or investigating.

Bladder Cancer

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

Bladder Cancer MRI Artificial intelligence

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Nanjing University of Aeronautics and Astronautics

UNKNOWN

Sponsor Role collaborator

The First Affiliated Hospital with Nanjing Medical University

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Qiang Lv, MD，PHD

Role: PRINCIPAL_INVESTIGATOR

The First Affiliated Hospital with Nanjing Medical University

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

The first affiliated hospital of Nanjing Medical University

Nanjing, Jiangsu, China

Site Status RECRUITING

Countries

Review the countries where the study has at least one active or historical site.

China

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Lingkai Cai

Role: CONTACT

Phone: +86 15206213500

Email: [email protected]

Qiang Lv, MD，PHD

Role: CONTACT

Phone: +86 13505196501

Email: [email protected]

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

Qiang Lu

Role: primary

Xiao Yang

Role: backup

References

Explore related publications, articles, or registry entries linked to this study.

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.

Reference Type BACKGROUND
PMID: 29755006 (View on PubMed)

Wang 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.

Reference Type BACKGROUND
PMID: 31012814 (View on PubMed)

Suarez-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.

Reference Type BACKGROUND
PMID: 31691082 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

2021-SR-409

Identifier Type: -

Identifier Source: org_study_id