Prediction of lipomatous soft tissue malignancy on MRI: comparison between machine learning applied to radiomics and deep learning.

Journal: European radiology experimental
Published Date: Sep 8, 2022

Abstract

OBJECTIVES: Malignancy of lipomatous soft-tissue tumours diagnosis is suspected on magnetic resonance imaging (MRI) and requires a biopsy. The aim of this study is to compare the performances of MRI radiomic machine learning (ML) analysis with deep learning (DL) to predict malignancy in patients with lipomas oratypical lipomatous tumours.

Authors

  • Guillaume Fradet
    Capgemini Engineering, Paris, France.
  • Reina Ayde
    Center for Adaptable MRI Technology (AMT Center), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland. reina.ayde@unibas.ch.
  • Hugo Bottois
    Capgemini Engineering, Paris, France. bottois.hugo@outlook.fr.
  • Mohamed El Harchaoui
    Capgemini Engineering, Paris, France.
  • Wassef Khaled
    Service de Radiologie B, Groupe Hospitalier Cochin, AP-HP Centre, Université de Paris, Paris, France.
  • Jean-Luc Drapé
    Service de Radiologie B, Groupe Hospitalier Cochin, AP-HP Centre, Université de Paris, Paris, France.
  • Frank Pilleul
    Université Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220 U1206, Villeurbanne, France.
  • Amine Bouhamama
    Université de Lyon, CREATIS (CNRS UMR 5220, Inserm U1206, INSA-Lyon, UJM Saint-Étienne, UCB Lyon1), 69621 Villeurbanne, France; Centre de lutte contre le cancer Léon Bérard, département de radiologie, 28, rue Laennec, 69008 Lyon, France.
  • Olivier Beuf
    Université de Lyon, CREATIS (CNRS UMR 5220, Inserm U1206, INSA-Lyon, UJM Saint-Étienne, UCB Lyon1), 69621 Villeurbanne, France.
  • Benjamin Leporq
    Université de Lyon, CREATIS (CNRS UMR 5220, Inserm U1206, INSA-Lyon, UJM Saint-Étienne, UCB Lyon1), 69621 Villeurbanne, France.

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