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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors
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Multiparametric MRI Radiomics for the Prediction of Histologic Grade in Bladder Cancer.

Anup Selvaraju1, Durgesh Kumar Dwivedi2, Naveen Kumar Patbamniya3

  • 1Department of Radiodiagnosis and Interventional Radiology, All India, Institute of Medical Sciences, New Delhi, India.

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This study developed an MRI radiomics model to predict bladder cancer grade non-invasively. The model, incorporating dynamic contrast-enhanced MRI and T2-weighted imaging features, achieved high accuracy, aiding pre-operative assessment.

Keywords:
Machine learningMagnetic resonance imagingOncologyRadiomicsUrinary bladder cancer

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Histological grade is crucial for bladder cancer management and prognosis.
  • Accurate, non-invasive pre-operative grading can significantly improve patient outcomes.
  • Multiparametric MRI (mpMRI) offers potential for non-invasive tumor assessment.

Purpose of the Study:

  • To develop and validate an MRI-based radiomics model for pre-operative prediction of bladder cancer histological grade.
  • To assess the feasibility of using radiomics features from mpMRI for non-invasive tumor grading.

Main Methods:

  • A prospective study involving 45 bladder cancer patients undergoing mpMRI scans.
  • Extraction of radiomics features from T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI).
  • Development of predictive models using variable clustering, ROC analysis, and multivariable analysis with cross-validation.

Main Results:

  • The final model incorporated DCE-MRI features (GLCM-inverse variance, Major Axis Length) and age, achieving an AUC of 0.91 (95% CI 0.82-1.00).
  • DCE-MRI features and patient age were significantly associated with high-grade bladder cancer.
  • The model demonstrated high predictive performance in bootstrap validation.

Conclusions:

  • An mpMRI radiomics approach shows significant potential as a non-invasive tool for pre-operative grading of bladder cancer.
  • This method can aid in treatment planning and prognosis assessment.
  • Further validation in larger cohorts is warranted.