Multi-parametric MRI in Patients of Bladder Cancer

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06362330
• Other study ID #: 2022-SR-471
• Status: Recruiting
• Start date: July 1, 2021
• Est. completion date: June 30, 2024

Study information

• Verified date: April 2024
• Source: The First Affiliated Hospital with Nanjing Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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).

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: June 30, 2024
• Est. primary completion date: May 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• Urothelial carcinoma of the bladder confirmed by final histopathology ?Received a standard contrast-enhanced 3.0T mpMRI before surgery ?All tumors within patients included were resected and received pathologic examination separately Exclusion Criteria: ?Absence of surgical interventions ?With inadequate image quality or with inadequate pathology for analysis

Related Conditions & MeSH terms

• Artificial Intelligence
• Muscle-invasive Bladder Cancer
• Urinary Bladder Neoplasms

Intervention

Other:
• magnetic resonance imaging
Patients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery

Locations

Country	Name	City	State
China	Yu-Dong Zhang	Nanjing

Sponsors (1)

Lead Sponsor	Collaborator
The First Affiliated Hospital with Nanjing Medical University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Muscle-invasive bladder cancer	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
Primary	Non-muscle-invasive bladder cancer	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