Multi-parametric MRI in Patients of Bladder Cancer

Recruiting

• Conditions: Muscle-invasive Bladder Cancer; Artificial Intelligence
• Interventions: Other: magnetic resonance imaging

• Registration Number: NCT06362330
• Lead Sponsor: The First Affiliated Hospital with Nanjing Medical University

Brief 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).

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 Timeline

• Study Start: 2021-07-01
• Status Verified: 2024-04
• Study First Submitted: 2024-04-06
• Study First Submitted QC: 2024-04-10
• Last Update Submitted: 2024-04-10
• Study First Posted: 2024-04-12
• Last Update Posted: 2024-04-12
• Primary Completion: 2024-05-30
• Study Completion: 2024-06-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
muscle-invasive bladder cancer	magnetic resonance imaging	The postoperative pathology was muscle-invasive bladder cancer
non-muscle-invasive bladder cancer	magnetic resonance imaging	The postoperative pathology was non-muscle-invasive bladder cancer

Primary Outcome Measures

Name	Time	Method
Non-muscle-invasive bladder cancer	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.
Muscle-invasive bladder cancer	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.

Trial Locations

Locations (1)

Yu-Dong Zhang; 🇨🇳 Nanjing, China