Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.

Journal: Abdominal radiology (New York)
Published Date: Jan 1, 2022

Abstract

PURPOSE: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics.

Authors

  • Adam M Awe
    Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Sciences Center, 600 Highland Ave, Madison, WI, 53792, USA.
  • Michael M Vanden Heuvel
    Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, USA.
  • Tianyuan Yuan
    Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, USA.
  • Victoria R Rendell
    Department of Surgery, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA.
  • Mingren Shen
    Dept. of Materials Science and Engineering, 244 MSE, University of Wisconsin, Madison, 53562.
  • Agrima Kampani
    Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, USA.
  • Shanchao Liang
    Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, USA.
  • Dane D Morgan
    Department of Materials Science and Engineering, University of Wisconsin - Madison, Madison, WI, USA.
  • Emily R Winslow
    Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Meghan G Lubner
    Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/311 Clinical Sciences Center, 600 Highland Avenue, Madison, WI 53792, USA. Electronic address: mlubner@uwhealth.org.

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