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

Autor:	Koka R; Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA., Wake LM; Johns Hopkins Hospital, Baltimore, Maryland, USA., Ku NK; Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA., Rice K; Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA., LaRocque A; Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA., Vidal EG; University of Maryland Medical Center, Baltimore, Maryland, USA., Alexanian S; PictorLabs Inc, Los Angeles, California, USA., Kozikowski R; PictorLabs Inc, Los Angeles, California, USA., Rivenson Y; PictorLabs Inc, Los Angeles, California, USA., Kallen ME; Department of Pathology, University of Maryland School of Medicine, Baltimore, Maryland, USA mkallen@som.umaryland.edu.
Jazyk:	angličtina
Zdroj:	Journal of clinical pathology [J Clin Pathol] 2024 Sep 19. Date of Electronic Publication: 2024 Sep 19.
DOI:	10.1136/jcp-2024-209643
Abstrakt:	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. Competing Interests: Competing interests: SA, RK and YR are employees of PictorLabs. The authors have no other conflicts of interest or disclosures to report. (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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