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Updated: Jul 23, 2025
05:19
Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
Published on: March 29, 2019
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MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment.
Bianca Boca1,2, Cosmin Caraiani2, Teodora Telecan3,4
1Department of Radiology, "George Emil Palade", University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540139 Targu Mures, Romania.
Diagnostics (Basel, Switzerland)
|July 14, 2023
Summary
Magnetic resonance imaging (MRI)-based radiomics shows potential for bladder cancer (BCa) characterization and prognosis. Further standardization and validation are needed before clinical application of these quantitative imaging biomarkers.
Area of Science:
- Radiology
- Oncology
- Medical Imaging Analysis
Background:
- Magnetic resonance imaging (MRI) with VI-RADS is standard for bladder cancer (BCa) staging.
- VI-RADS has limitations including interobserver variability and lack of cellularity information.
- Radiomics offers a quantitative approach to overcome these limitations.
Purpose of the Study:
- To systematically review studies on MRI-based radiomics for bladder cancer.
- To assess the application of radiomics in BCa staging, grading, and treatment response.
Main Methods:
- Systematic literature review following PRISMA principles.
- Searched PubMed, Scopus, and Web of Science for "bladder cancer", "magnetic resonance imaging", "radiomics", and "textural analysis".
- Included 26 eligible studies out of 1092 retrieved papers.
Main Results:
- Radiomics primarily used for preoperative tumor staging (13 studies), grade prediction (9 studies), and prognosis/treatment response (4 studies).
- Most models utilized second-order features from filtered images and underwent validation in 16 studies.
- Average radiomics quality score was 11.7, with significant variability.
Conclusions:
- MRI-based radiomics shows promise as a quantitative imaging biomarker for BCa characterization and prognosis.
- Standardization of image preprocessing, feature extraction, and external validation are crucial for clinical implementation.
- Further research is needed to refine radiomics models for routine clinical use.
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