Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Journal: Journal of clinical pathology
PMID: 39304200

Abstract

Microscopic review of tissue sections is of foundational importance in pathology, yet the traditional chemistry-based histology laboratory methods are labour intensive, tissue destructive, poorly scalable to the evolving needs of precision medicine and cause delays in patient diagnosis and treatment. Recent AI-based techniques offer promise in upending histology workflow; one such method developed by PictorLabs can generate near-instantaneous diagnostic images via a machine learning algorithm. Here, we demonstrate the utility of virtual staining in a blinded, wash-out controlled study of 16 cases of lymph node excisional biopsies, including a spectrum of diagnoses from reactive to lymphoma and compare the diagnostic performance of virtual and chemical H&Es across a range of stain quality, image quality, morphometric assessment and diagnostic interpretation parameters as well as proposed follow-up immunostains. Our results show non-inferior performance of virtual H&E stains across all parameters, including an improved stain quality pass rate (92% vs 79% for virtual vs chemical stains, respectively) and an equivalent rate of binary diagnostic concordance (90% vs 92%). More detailed adjudicated reviews of differential diagnoses and proposed IHC panels showed no major discordances. Virtual H&Es appear fit for purpose and non-inferior to chemical H&Es in diagnostic assessment of clinical lymph node samples, in a limited pilot study.

Authors

  • Rima Koka
    University of Maryland School of Medicine, Baltimore, MD, USA.
  • Laura M Wake
    Department of Pathology, Johns Hopkins Hospital, Baltimore, MD, USA.
  • Nam K Ku
    Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA.
  • Kathryn Rice
    Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Autumn LaRocque
    Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Elba G Vidal
    University of Maryland Medical Center, Baltimore, Maryland, USA.
  • Serge Alexanian
    PictorLabs Inc, Los Angeles, California, USA.
  • Raymond Kozikowski
    PictorLabs Inc, Los Angeles, California, USA.
  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Michael Edward Kallen
    Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA mkallen@som.umaryland.edu.

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