Application of a machine learning algorithm to predict malignancy in thyroid cytopathology.

Journal Article

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) comprises 6 categories used for the diagnosis of thyroid fine-needle aspiration biopsy (FNAB). Each category has an associated risk of malignancy, which is important in the management of a thyroid nodule. More accurate predictions of malignancy may help to reduce unnecessary surgery. A machine learning algorithm (MLA) was developed to evaluate thyroid FNAB via whole slide images (WSIs) to predict malignancy. METHODS: Files were searched for all thyroidectomy specimens with preceding FNAB over 8 years. All cytologic and surgical pathology diagnoses were recorded and correlated for each nodule. One representative slide from each case was scanned to create a WSI. An MLA was designed to identify follicular cells and predict the malignancy of the final pathology. The test set comprised cases blindly reviewed by a cytopathologist who assigned a TBSRTC category. The area under the receiver operating characteristic curve was used to assess the MLA performance. RESULTS: Nine hundred eight FNABs met the criteria. The MLA predicted malignancy with a sensitivity and specificity of 92.0% and 90.5%, respectively. The areas under the curve for the prediction of malignancy by the cytopathologist and the MLA were 0.931 and 0.932, respectively. CONCLUSIONS: The performance of the MLA in predicting thyroid malignancy from FNAB WSIs is comparable to the performance of an expert cytopathologist. When the MLA and electronic medical record diagnoses are combined, the performance is superior to the performance of either alone. An MLA may be used as an adjunct to FNAB to assist in refining the indeterminate categories.

Full Text

Duke Authors

Cited Authors

  • Elliott Range, DD; Dov, D; Kovalsky, SZ; Henao, R; Carin, L; Cohen, J

Published Date

  • April 2020

Published In

Volume / Issue

  • 128 / 4

Start / End Page

  • 287 - 295

PubMed ID

  • 32012493

Pubmed Central ID

  • 32012493

Electronic International Standard Serial Number (EISSN)

  • 1934-6638

Digital Object Identifier (DOI)

  • 10.1002/cncy.22238

Language

  • eng

Conference Location

  • United States