Multi-parametric MRI in Patients of Bladder Cancer

April 10, 2024 updated by: The First Affiliated Hospital with Nanjing Medical University

Knowledge-guided Causal Diagnostic Network for the Detection of Muscle-invasive Bladder Cancer With Single T2-weighted Imaging

Accurate preoperative detection of muscle-invasive bladder cancer remains a clinical challenge. The investigators aimed to develop and validate a knowledge-guided causal diagnostic network for the detection of muscle-invasive bladder cancer with multiparametric magnetic resonance imaging(MRI).

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Patients who underwent bladder MRI were retrospectively collected at three centers between January 2013 and September 2023. The investigators first constructed a nnUNet to segment causal region where muscle-invasive bladder cancer may occur. Subsequently, the investigators explored a causal network based on a modified ResNet3d-18 by striking a fine balance between nnUNet awareness and a self-supervised learning (SSL) model, which steered model to emulate diagnostic acumen of expert in staging muscle-invasive bladder cancer at MRI. Model was trained in center 1, and independently tested in center 1, center 2 and center 3. Ablation test was performed among all 13 Ablation-Test models using either single or multi-parametric MRI. Benefit was tested in six radiologists using vesical imaging-reporting and data system (VI-RADS) versus network-adjusted VI-RADS.

Study Type

Observational

Enrollment (Estimated)

1000

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
      • Nanjing, China, 210029
        • Recruiting
        • Yu-Dong Zhang
        • 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

Sampling Method

Probability Sample

Study Population

Preoperative multi-parameter magnetic resonance imaging is essential to ascertain whether patients with bladder cancer exhibit muscular invasion, facilitating the selection of appropriate treatment options.

Description

Inclusion Criteria:

  • Urothelial carcinoma of the bladder confirmed by final histopathology ②Received a standard contrast-enhanced 3.0T mpMRI before surgery ③All tumors within patients included were resected and received pathologic examination separately

Exclusion Criteria:

①Absence of surgical interventions

②With inadequate image quality or with inadequate pathology for analysis

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
muscle-invasive bladder cancer
The postoperative pathology was muscle-invasive bladder cancer
Other: magnetic resonance imaging
Patients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery
non-muscle-invasive bladder cancer
The postoperative pathology was non-muscle-invasive bladder cancer
Other: magnetic resonance imaging
Patients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Muscle-invasive bladder cancer
Time Frame: one month
The artificial intelligence diagnosis results, based on preoperative MRI, indicated muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
one month
Non-muscle-invasive bladder cancer
Time Frame: one month
The artificial intelligence diagnosis results, based on preoperative MRI, indicated non-muscle-invasive bladder cancer. Subsequently, this preoperative diagnosis was compared with the postoperative pathological diagnosis to evaluate the diagnostic performance of the artificial intelligence.
one month

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

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)

July 1, 2021

Primary Completion (Estimated)

May 30, 2024

Study Completion (Estimated)

June 30, 2024

Study Registration Dates

First Submitted

April 6, 2024

First Submitted That Met QC Criteria

April 10, 2024

First Posted (Actual)

April 12, 2024

Study Record Updates

Last Update Posted (Actual)

April 12, 2024

Last Update Submitted That Met QC Criteria

April 10, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022-SR-471

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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