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Updated: Sep 8, 2025
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Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
Published on: March 29, 2019
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Development and Validation of a Preoperative MRI Habitat Radiomics Model to Predict Variant Histology in Bladder
Lingmin Kong1, Yanjin Qin1, Hui Li2
1Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Journal of Magnetic Resonance Imaging : JMRI
|August 20, 2025
Summary
A new MRI-based model, VHRisk Score (VHRiS), accurately identifies aggressive bladder cancer variant histology. This tool aids in risk stratification and predicting disease-free survival and treatment response.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Bladder cancer (BCa) with variant histology (VH) presents an aggressive clinical course.
- VH is associated with poor prognosis and resistance to neoadjuvant treatment (NAT).
- Preoperative identification of VH is crucial for tailoring treatment strategies.
Purpose of the Study:
- To develop and validate a multiparametric MRI-based ensemble model for identifying VH in BCa.
- To assess the model's association with disease-free survival (DFS) and NAT response.
Main Methods:
- Retrospective analysis of 620 BCa patients across four centers.
- Development of the VHRisk Score (VHRiS) model using MRI data (T2W, DWI, DCE-T1W).
- Validation of the model on internal and external datasets; evaluation of prognostic value in dedicated DFS and NAT cohorts.
Main Results:
- The VHRiS model demonstrated high accuracy in identifying VH (AUCs ranging from 0.895 to 0.974).
- Low-risk patients (VHRiS ≥ 0.863) showed significantly longer DFS (4.20 vs. 3.08 months).
- Low-risk patients had a higher pathological complete response (pCR) rate (64% vs. 33%) after NAT.
Conclusions:
- The VHRiS model is a robust tool for identifying VH in bladder cancer.
- VHRiS offers a potential method for risk stratification and prognosis prediction in BCa patients.
- This MRI-based approach can inform personalized treatment decisions for BCa.
