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

NCT05096533 | Unknown

• 1 other identifier: 2021-SR-409
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief 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. 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

Outcome Measures

Primary Outcomes (1)

• 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.

  1 year

Secondary Outcomes (1)

• Overall survival

  3 years after surgery

Other Outcomes (2)

• recurrence-free survival

  3 years after surgery

• progression-free survival

  3 years after surgery

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Affiliated Hospital with Nanjing Medical University: lead
• Nanjing University of Aeronautics and Astronautics: collaborator

Study Sites (1)

The first affiliated hospital of Nanjing Medical University

Nanjing, Jiangsu, 210000, China

RECRUITING

Related Publications (3)

• 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.

  PMID: 29755006 BACKGROUND

• 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.

  PMID: 31012814 BACKGROUND

• 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.

  PMID: 31691082 BACKGROUND

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Study Officials

• Qiang Lv, MD，PHD

  The First Affiliated Hospital with Nanjing Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Lingkai Cai

CONTACT

+86 15206213500; lingkaicai1996@163.com

Qiang Lv, MD，PHD

CONTACT

+86 13505196501; doctorlvqiang@njmu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 7, 2021
• First Posted: October 27, 2021
• Study Start: January 1, 2021
• Primary Completion: May 1, 2022
• Study Completion: January 1, 2023
• Last Updated: October 27, 2021

Record last verified: 2021-10

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)