Artificial Intelligence, Muscle-invasive Bladder Cancer
Conditions
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.
Interventions
OTHERmagnetic resonance imaging
Patients of bladder cancer underwent multiparameter magnetic resonance imaging before surgery
Sponsors
The First Affiliated Hospital with Nanjing Medical University
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
| 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. |
Countries
China
Outcome results
None listed