Aims: The study is aimed to verify Aperio AT2 scanner for reporting on the digital pathology platform (DP) and to validate the cohort of pathologists in the interpretation of DP for routine diagnostic histopathological services in Wales, United Kingdom.
Materials Methods And Results: This was a large multicenter study involving seven hospitals across Wales and unique with 22 (largest number) pathologists participating. 7491 slides from 3001 cases were scanned on Leica Aperio AT2 scanner and reported on digital workstations with Leica software of e-slide manager. A senior pathology fellow compared DP reports with authorized reports on glass slide (GS). A panel of expert pathologists reviewed the discrepant cases under multiheader microscope to establish ground truth. 2745 out of 3001 (91%) cases showed complete concordance between DP and GS reports. Two hundred and fifty-six cases showed discrepancies in diagnosis, of which 170 (5.6%) were deemed of no clinical significance by the review panel. There were 86 (2.9%) clinically significant discrepancies in the diagnosis between DP and GS. The concordance was raised to 97.1% after discounting clinically insignificant discrepancies. Ground truth lay with DP in 28 out of 86 clinically significant discrepancies and with GS in 58 cases. Sensitivity of DP was 98.07% (confidence interval [CI] 97.57-98.56%); for GS was 99.07% (CI 98.72-99.41%).
Conclusions: We concluded that Leica Aperio AT2 scanner produces adequate quality of images for routine histopathologic diagnosis. Pathologists were able to diagnose in DP with good concordance as with GS.
Strengths And Limitations Of This Study: Strengths of this study - This was a prospective blind study. Different pathologists reported digital and glass arms at different times giving an ambience of real-time reporting. There was standardized use of software and hardware across Wales. A strong managerial support from efficiency through the technology group was a key factor for the implementation of the study.
Limitations: This study did not include Cytopathology and hybridization slides. Difficulty in achieving surgical pathology practise standardization across the whole country contributed to intra-observer variations.
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http://dx.doi.org/10.4103/jpi.jpi_55_20 | DOI Listing |
Acta Cytol
September 2024
Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan.
Introduction: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images.
Methods: This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan.
J Pathol Inform
December 2024
Division of Urology, Department of Surgery, Tung's Taichung MetroHarbor Hospital, Taichung, Taiwan.
Background: Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes.
Methods: Fourteen urine specimens were collected from patients with urothelial carcinoma.
Arch Pathol Lab Med
June 2024
From the Departments of Pathology (Shehabeldin, Rohra, Sellen, Zhao, Alqaidy, Aramin, Hameed, Perez, Lai, Tong, Edgerton, Fuller, Hansel, Prieto, Ballester, Aung) and Biostatistics (Milton), The University of Texas MD Anderson Cancer Center, Houston.
Context.—: In the United States, review of digital whole slide images (WSIs) using specific systems is approved for primary diagnosis but has not been implemented for intraoperative consultation.
Objective.
J Neuropathol Exp Neurol
February 2023
Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA.
Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-β deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-β-stained slides with minimal changes in preanalytic variables.
View Article and Find Full Text PDFAnn Clin Lab Sci
November 2022
Division of Dermatologic Surgery, Department of Dermatology, Mayo Clinic, Rochester, MN, USA
Objective: To assess accuracy of whole slide imaging (WSI) in the interpretation of permanent and frozen sections in surgical pathology and the identification of tumors in cutaneous en face frozen sections.
Methods: Twenty glass slides containing cutaneous frozen sections were selected from twenty cases of keratinocyte carcinomas treated with Mohs micrographic surgery. Ten slides contained tumor and ten did not.
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