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Repeatability of Multiparametric Prostate MRI Radiomics Features.

Michael Schwier | Joost van Griethuysen | Mark G Vangel | Steve Pieper | Sharon Peled | Clare Tempany | Hugo J W L Aerts | Ron Kikinis | Fiona M Fennessy | Andriy Fedorov
Scientific reports | 2019

In this study we assessed the repeatability of radiomics features on small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI). The premise of radiomics is that quantitative image-based features can serve as biomarkers for detecting and characterizing disease. For such biomarkers to be useful, repeatability is a basic requirement, meaning its value must remain stable between two scans, if the conditions remain stable. We investigated repeatability of radiomics features under various preprocessing and extraction configurations including various image normalization schemes, different image pre-filtering, and different bin widths for image discretization. Although we found many radiomics features and preprocessing combinations with high repeatability (Intraclass Correlation Coefficient > 0.85), our results indicate that overall the repeatability is highly sensitive to the processing parameters. Neither image normalization, using a variety of approaches, nor the use of pre-filtering options resulted in consistent improvements in repeatability. We urge caution when interpreting radiomics features and advise paying close attention to the processing configuration details of reported results. Furthermore, we advocate reporting all processing details in radiomics studies and strongly recommend the use of open source implementations.

Pubmed ID: 31263116

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Associated grants

  • Agency: NIBIB NIH HHS, United States
    Id: P41 EB015898
  • Agency: NCI NIH HHS, United States
    Id: U24 CA180918
  • Agency: NCI NIH HHS, United States
    Id: U01 CA190234
  • Agency: NCI NIH HHS, United States
    Id: U01 CA151261

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3D Slicer (tool)

RRID:SCR_005619

A free, open source software package for visualization and image analysis including registration, segmentation, and quantification of medical image data. Slicer provides a graphical user interface to a powerful set of tools so they can be used by end-user clinicians and researchers alike. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X. Slicer is based on VTK (http://public.kitware.com/vtk) and has a modular architecture for easy addition of new functionality. It uses an XML-based file format called MRML - Medical Reality Markup Language which can be used as an interchange format among medical imaging applications. Slicer is primarily written in C++ and Tcl.

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