Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study
56
Zitationen
13
Autoren
2017
Jahr
Abstract
Importance: Digital pathology represents a transformative technology that impacts dermatologists and dermatopathologists from residency to academic and private practice. Two concerns are accuracy of interpretation from whole-slide images (WSI) and effect on workflow. Studies of considerably large series involving single-organ systems are lacking. Objective: To evaluate whether diagnosis from WSI on a digital microscope is inferior to diagnosis of glass slides from traditional microscopy (TM) in a large cohort of dermatopathology cases with attention on image resolution, specifically eosinophils in inflammatory cases and mitotic figures in melanomas, and to measure the workflow efficiency of WSI compared with TM. Design, Setting, and Participants: Three dermatopathologists established interobserver ground truth consensus (GTC) diagnosis for 499 previously diagnosed cases proportionally representing the spectrum of diagnoses seen in the laboratory. Cases were distributed to 3 different dermatopathologists who diagnosed by WSI and TM with a minimum 30-day washout between methodologies. Intraobserver WSI/TM diagnoses were compared, followed by interobserver comparison with GTC. Concordance, major discrepancies, and minor discrepancies were calculated and analyzed by paired noninferiority testing. We also measured pathologists' read rates to evaluate workflow efficiency between WSI and TM. This retrospective study was caried out in an independent, national, university-affiliated dermatopathology laboratory. Main Outcomes and Measures: Intraobserver concordance of diagnoses between WSI and TM methods and interobserver variance from GTC, following College of American Pathology guidelines. Results: Mean intraobserver concordance between WSI and TM was 94%. Mean interobserver concordance was 94% for WSI and GTC and 94% for TM and GTC. Mean interobserver concordance between WSI, TM, and GTC was 91%. Diagnoses from WSI were noninferior to those from TM. Whole-slide image read rates were commensurate with WSI experience, achieving parity with TM by the most experienced user. Conclusions and Relevance: Diagnosis from WSI was found equivalent to diagnosis from glass slides using TM in this statistically powerful study of 499 dermatopathology cases. This study supports the viability of WSI for primary diagnosis in the clinical setting.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.877 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.746 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.436 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.026 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.375 Zit.