Background: Frozen section (FS) diagnosis is one of the promising applications of digital pathology (DP). However, the implementation of an appropriate and economically viable DP solution for FS in routine practice is challenging. The objective of this study was to establish the non-inferiority of whole-slide imaging (WSI) versus optical microscopy (OM) for FS diagnosis using a low cost and portable DP system.
Materials And Methods: A validation study to investigate the technical performance and diagnostic accuracy of WSI versus OM for FS diagnosis was performed using 60 FS cases[120 slides i.e, 60 hematoxylin and eosin (H & E) and 60 toluidine blue (TOLB)]. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM were recorded.
Results: The first time successful scanning rate was 89.1% (107/120). Mean scanning time per slide for H and E and TOLB slide was 1:47 min (range; 0:22-3: 21 min) and 1:46 min (range; 0:21-3: 20 min), respectively. Mean storage space per slide for H and E and TOLB slide was 0.83 GB (range: 0.12-1.73 GB) and 0.71 GB (range: 0.11-1.66 GB), respectively. Considering major discrepancies, the overall diagnostic concordance for OM and WSI, when compared with the reference standard, was 95.42% and 95.83%, respectively. There was almost perfect intra as well as inter-observer agreement ( ≥ 0.8) among 4 pathologists between WSI and OM for FS diagnosis. Mean turnaround time (TAT) of 14:58 min was observed using WSI for FS diagnosis, which was within the College of American Pathologists recommended range for FS reporting. The image quality was average to best quality in most of the cases.
Conclusion: WSI was noninferior to OM for FS diagnosis across various specimen types. This portable WSI system can be safely adopted for routine FS diagnosis and provides an economically viable alternative to high-end scanners.
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http://dx.doi.org/10.4103/jpi.jpi_95_20 | DOI Listing |
BMJ Open Gastroenterol
January 2025
Histopathology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Objective: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an AI algorithm that can effectively classify colonic biopsies into normal versus abnormal categories, designed to automatically report normal cases. We performed a retrospective pathological and clinical review of the errors made by IGUANA.
View Article and Find Full Text PDFCancer Cytopathol
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Regional Laboratory of Cancer Prevention, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy.
Background: Whole-slide imaging (WSI) has been adopted in many fields of pathology for education, quality assurance, and remote diagnostics. In 2021, the College of American Pathologists (CAP) updated guidelines to support pathology laboratories regarding the WSI systems validation process. However, the majority of published literature refers to histopathology rather than cytology.
View Article and Find Full Text PDFEBioMedicine
December 2024
Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, Campus Box 8118, Saint Louis, MO, 63110, United States. Electronic address:
Background: Alterations in ileal Paneth cell (PC) density have been described in gut inflammatory diseases such as Crohn's disease (CD) and could be used as a biomarker for disease prognosis. However, quantifying PCs is time-intensive, a barrier for clinical workflow. Deep learning (DL) has transformed the development of robust and accurate tools for complex image evaluation.
View Article and Find Full Text PDFAdv Ther
December 2024
Sanofi, Madrid, Spain.
Brief Bioinform
September 2024
Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Health, Lebanon, NH 03766, USA.
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