Deep-Learning–Based Screening and Ancillary Testing for Thyroid Cytopathology

David Dov, Danielle Elliott Range, Jonathan Cohen, Jonathan Bell, Daniel J. Rocke, Russel R. Kahmke, Ahuva Weiss-Meilik, Walter T. Lee, Ricardo Henao, Lawrence Carin, Shahar Z. Kovalsky

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Thyroid cancer is the most common malignant endocrine tumor. The key test to assess preoperative risk of malignancy is cytologic evaluation of fine-needle aspiration biopsies (FNABs). The evaluation findings can often be indeterminate, leading to unnecessary surgery for benign post-surgical diagnoses. We have developed a deep-learning algorithm to analyze thyroid FNAB whole-slide images (WSIs). We show, on the largest reported data set of thyroid FNAB WSIs, clinical-grade performance in the screening of determinate cases and indications for its use as an ancillary test to disambiguate indeterminate cases. The algorithm screened and definitively classified 45.1% (130/288) of the WSIs as either benign or malignant with risk of malignancy rates of 2.7% and 94.7%, respectively. It reduced the number of indeterminate cases (N = 108) by reclassifying 21.3% (N = 23) as benign with a resultant risk of malignancy rate of 1.8%. Similar results were reproduced using a data set of consecutive FNABs collected during an entire calendar year, achieving clinically acceptable margins of error for thyroid FNAB classification.
Original languageEnglish (US)
Pages (from-to)1185-1194
Number of pages10
JournalThe American Journal of Pathology
Volume193
Issue number9
DOIs
StatePublished - Aug 21 2023

ASJC Scopus subject areas

  • Pathology and Forensic Medicine

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