Use of Machine Learning-Based Software for the Screening of Thyroid Cytopathology Whole Slide Images.

Journal Article (Journal Article)

CONTEXT.—: The use of whole slide images (WSIs) in diagnostic pathology presents special challenges for the cytopathologist. Informative areas on a direct smear from a thyroid fine-needle aspiration biopsy (FNAB) smear may be spread across a large area comprising blood and dead space. Manually navigating through these areas makes screening and evaluation of FNA smears on a digital platform time-consuming and laborious. We designed a machine learning algorithm that can identify regions of interest (ROIs) on thyroid fine-needle aspiration biopsy WSIs. OBJECTIVE.—: To evaluate the ability of the machine learning algorithm and screening software to identify and screen for a subset of informative ROIs on a thyroid FNA WSI that can be used for final diagnosis. DESIGN.—: A representative slide from each of 109 consecutive thyroid fine-needle aspiration biopsies was scanned. A cytopathologist reviewed each WSI and recorded a diagnosis. The machine learning algorithm screened and selected a subset of 100 ROIs from each WSI to present as an image gallery to the same cytopathologist after a washout period of 117 days. RESULTS.—: Concordance between the diagnoses using WSIs and those using the machine learning algorithm-generated ROI image gallery was evaluated using pairwise weighted κ statistics. Almost perfect concordance was seen between the 2 methods with a κ score of 0.924. CONCLUSIONS.—: Our results show the potential of the screening software as an effective screening tool with the potential to reduce cytopathologist workloads.

Full Text

Duke Authors

Cited Authors

  • Dov, D; Kovalsky, SZ; Feng, Q; Assaad, S; Cohen, J; Bell, J; Henao, R; Carin, L; Range, DE

Published Date

  • July 1, 2022

Published In

Volume / Issue

  • 146 / 7

Start / End Page

  • 872 - 878

PubMed ID

  • 34669924

Electronic International Standard Serial Number (EISSN)

  • 1543-2165

Digital Object Identifier (DOI)

  • 10.5858/arpa.2020-0712-OA

Language

  • eng

Conference Location

  • United States