Publications [#372683] of Lawrence Carin

Papers Published

1. Dov, D; Elliott Range, D; Cohen, J; Bell, J; Rocke, DJ; Kahmke, RR; Weiss-Meilik, A; Lee, WT; Henao, R; Carin, L; Kovalsky, SZ, Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology., Am J Pathol, vol. 193 no. 9 (September, 2023), pp. 1185-1194 [doi]
   (last updated on 2024/12/31)

   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.