Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

Journal: Leukemia
Published Date: Jan 1, 2022

Abstract

The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 (NPM1)-one of the most common mutations in AML-with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1-mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.

Authors

  • Jan-Niklas Eckardt
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany. jan-niklas.eckardt@uniklinikum-dresden.de.
  • Jan Moritz Middeke
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Sebastian Riechert
    Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany.
  • Tim Schmittmann
    Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany.
  • Anas Shekh Sulaiman
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Michael Kramer
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Katja Sockel
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Frank Kroschinsky
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Ulrich Schuler
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Johannes Schetelig
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Christoph Röllig
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Christian Thiede
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
  • Karsten Wendt
    Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany.
  • Martin Bornhäuser
    Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

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