Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples.

Journal: Clinical journal of the American Society of Nephrology : CJASN
Published Date: Dec 3, 2021

Abstract

BACKGROUND AND OBJECTIVES: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features.

Authors

  • Elise Marechal
    Department of Nephrology, CHU Dijon, France.
  • Adrien Jaugey
    Université de Bourgogne Franche comté, France.
  • Georges Tarris
    Université de Bourgogne Franche comté, France.
  • Michel Paindavoine
    Université de Bourgogne Franche comté, France.
  • Jean Seibel
    Department of Nephrology, CHU Dijon, France.
  • Laurent Martin
    Université de Bourgogne Franche comté, France.
  • Mathilde Funes de la Vega
    Department of Pathology, CHU Dijon, France.
  • Thomas Crepin
    Département de Néphrologie, Dialyse et Transplantation, CHU de Besançon, Besançon, France.
  • Didier Ducloux
    Université de Bourgogne Franche comté, France.
  • Gilbert Zanetta
    Department of Nephrology, CHU Dijon, France.
  • Sophie Felix
    Department of Pathology, CHU Besançon France.
  • Pierre Henri Bonnot
    Department of Nephrology, CHU Dijon, France.
  • Florian Bardet
    Université de Bourgogne Franche comté, France.
  • Luc Cormier
    Department of Urology, Centre Hospitalier Régional Universitaire, Hôpital François Mitterrand, Dijon, France.
  • Jean-Michel Rebibou
    Department of Nephrology, CHU Dijon, France.
  • Mathieu Legendre
    Department of Nephrology, CHU Dijon, France.

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