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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

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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 (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Dec 23, 2025

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
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Characterization of solid renal neoplasms using MRI-based quantitative radiomics features.

Daniela Said1,2, Stefanie J Hectors1,3,4, Eric Wilck3

  • 1BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Abdominal Radiology (New York)
|April 26, 2020
PubMed
Summary

Machine learning models combined with MRI radiomics and qualitative analysis can effectively characterize renal masses. This approach aids in differentiating renal cell carcinomas from benign lesions and classifying RCC subtypes.

Keywords:
HistogramMagnetic resonance imagingRadiomicsRenal cell carcinomaRenal massTexture

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Solid renal neoplasms require accurate characterization for effective management.
  • Distinguishing renal cell carcinomas (RCCs) from benign lesions and classifying RCC subtypes is clinically significant.
  • Magnetic resonance imaging (MRI) offers detailed anatomical and functional information for renal mass assessment.

Purpose of the Study:

  • To evaluate the diagnostic performance of MRI-based radiomics features and machine learning (ML) models in characterizing solid renal neoplasms.
  • To compare the diagnostic value of radiomics features with qualitative radiologic evaluation.
  • To assess the combined utility of radiomics and qualitative assessment for renal mass characterization.

Main Methods:

  • Retrospective analysis of 125 patients with solid renal neoplasms who underwent preoperative MRI.
  • Qualitative (signal, enhancement) and quantitative radiomics (histogram, texture) analyses were performed on various MRI sequences.
  • Machine learning (Random Forest) models were developed and validated for differentiating RCCs from benign lesions and classifying RCC subtypes (ccRCC, pRCC).

Main Results:

  • Significant qualitative and radiomics features with Area Under the Curve (AUC) ranging from 0.62 to 0.90 were identified.
  • A combined model achieved an AUC of 0.73 for differentiating RCC from benign lesions.
  • Radiomics models showed an AUC of 0.77 for diagnosing clear cell RCC (ccRCC), and qualitative features achieved an AUC of 0.74 for diagnosing papillary RCC (pRCC).

Conclusions:

  • Machine learning models integrating MRI-based radiomics and qualitative assessment demonstrate value in characterizing renal masses.
  • This multimodal approach aids in differentiating malignant from benign renal lesions.
  • The findings suggest potential for improved diagnostic accuracy in classifying RCC subtypes.