Multi-parametric MRI in Patients of Bladder Cancer

Nanjing Medical University

Status

Enrolling

Conditions

Muscle-invasive Bladder Cancer

Artificial Intelligence

Treatments

Other: magnetic resonance imaging

Study type

Observational

Funder types

Other

Identifiers

NCT06362330

2022-SR-471

Details and patient eligibility

About

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).

Full 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.

Enrollment

1,000 estimated patients

Sex

All

Volunteers

No Healthy Volunteers

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