Banff scoring of kidney allograft biopsies: "Manual" application vs software-assisted sign-out.

Autor:	Demetris AJ; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., Lesniak AJ; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., Popp BA; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., Frencho RJ; Scalable Solutions, Ingomar, PA, US., Minervini MI; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., Nalesnik MA; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., El Hag MI; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US., Hariharan S; Division of Transplant Nephrology, University of Pittsburgh Medical Center, Pittsburgh, PA, US., Randhawa PS; Department of Pathology, Division of Transplantation, University of Pittsburgh, Pittsburgh, PA, US.
Jazyk:	angličtina
Zdroj:	American journal of clinical pathology [Am J Clin Pathol] 2024 Jun 03; Vol. 161 (6), pp. 543-552.
DOI:	10.1093/ajcp/aqad180
Abstrakt:	Objectives: Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement. Methods: We constructed a web-based "smart template" (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees. Results: Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence-assisted scoring of biopsies. Conclusions: Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms. (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Databáze:	MEDLINE
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