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

Journal: Cancer cytopathology
Published Date: Apr 1, 2020

Abstract

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.

Authors

  • Danielle D Elliott Range
    Department of Pathology, Duke University School of Medicine, Durham, North Carolina.
  • David Dov
    Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
  • Shahar Z Kovalsky
    Department of Mathematics, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina.
  • Ricardo Henao
    Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
  • Lawrence Carin
    Department of Electronic and Computer Engineering, Duke University, Durham, NC, 27705, USA.
  • Jonathan Cohen
    Division of biostatistics, School of Public Health, university of California Berkeley, CA, USA.

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