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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

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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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Related Experiment Video

Updated: Jul 21, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Published on: March 21, 2025

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Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation.

Sithin Thulasi Seetha1,2, Enrico Garanzini3, Chiara Tenconi4,5

  • 1Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy.

Journal of Personalized Medicine
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

Simulating segmentation variations using in silico contour generation helps identify stable radiomic features for personalized cancer imaging. Pre-filtering strategies significantly improve feature robustness against segmentation differences.

Keywords:
multi-parametric MRIprostateradiomics

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Area of Science:

  • Radiomics and Medical Imaging
  • Computational Pathology
  • Oncological Biomarker Development

Background:

  • Radiomic feature stability is crucial for developing reliable imaging biomarkers in oncology.
  • Segmentation variability among human annotators can compromise the reproducibility of radiomic features.
  • Personalized oncological strategies rely on robust and stable imaging biomarkers.

Purpose of the Study:

  • To develop an in silico method for simulating segmentation variations to assess radiomic feature stability.
  • To identify stable radiomic features that are robust to segmentation differences.
  • To evaluate the impact of pre-filtering strategies on radiomic feature stability.

Main Methods:

  • Generated 15 synthetic contours by perturbing ground-truth prostate gland segmentations on multi-parametric MRI (T2w, ADC, SUB-DCE).
  • Extracted 1224 radiomic features using Pyradiomics and assessed stability with ICC(1,1).
  • Compared stable features across internal and external populations and investigated filtering strategies.

Main Results:

  • Segmentation variations significantly impacted radiomic feature stability.
  • Pre-filtering strategies substantially improved feature robustness: T2w (81% vs 36%), ADC (94% vs 36%), SUB-DCE (93% vs 43%).
  • Identified robust features that remained stable across internal and external datasets.

Conclusions:

  • In silico simulation of segmentation variations is effective for evaluating radiomic feature stability.
  • Pre-filtering strategies are essential for mitigating the impact of segmentation variability on radiomic features.
  • Robust radiomic features identified through this approach can enhance the reliability of imaging biomarkers for personalized cancer treatment.