Use of Machine Learning--Based Software for the Screening of Thyroid Cytopathology Whole Slide Images.
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 naviga...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 146; no. 7; pp. 872 - 879 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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 j statistics. Almost perfect concordance was seen between the 2 methods with a j 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. |
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