LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity

  • Christophe Nioche
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Fanny Orlhac
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Sarah Boughdad
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Sylvain Reuzé
    2Inserm U1030 and Department of Radiotherapy, Gustave Roussy, University Paris Sud, Université Paris Saclay, Villejuif, France.
  • Jessica Goya-Outi
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Charlotte Robert
    2Inserm U1030 and Department of Radiotherapy, Gustave Roussy, University Paris Sud, Université Paris Saclay, Villejuif, France.
  • Claire Pellot-Barakat
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Michael Soussan
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Frédérique Frouin
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.
  • Irène Buvat
    1Imagerie Moléculaire In Vivo, CEA, Inserm, Univ Paris Sud, CNRS, Université Paris Saclay, Orsay, France.

説明

<jats:title>Abstract</jats:title> <jats:p>Textural and shape analysis is gaining considerable interest in medical imaging, particularly to identify parameters characterizing tumor heterogeneity and to feed radiomic models. Here, we present a free, multiplatform, and easy-to-use freeware called LIFEx, which enables the calculation of conventional, histogram-based, textural, and shape features from PET, SPECT, MR, CT, and US images, or from any combination of imaging modalities. The application does not require any programming skills and was developed for medical imaging professionals. The goal is that independent and multicenter evidence of the usefulness and limitations of radiomic features for characterization of tumor heterogeneity and subsequent patient management can be gathered. Many options are offered for interactive textural index calculation and for increasing the reproducibility among centers. The software already benefits from a large user community (more than 800 registered users), and interactions within that community are part of the development strategy.</jats:p> <jats:p>Significance: This study presents a user-friendly, multi-platform freeware to extract radiomic features from PET, SPECT, MR, CT, and US images, or any combination of imaging modalities. Cancer Res; 78(16); 4786–9. ©2018 AACR.</jats:p>

収録刊行物

  • Cancer Research

    Cancer Research 78 (16), 4786-4789, 2018-08-14

    American Association for Cancer Research (AACR)

被引用文献 (26)*注記

もっと見る

詳細情報 詳細情報について

問題の指摘

ページトップへ