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

Danielle D Elliott Range1, David Dov2, Shahar Z Kovalsky3

  • 1Department of Pathology, Duke University School of Medicine, Durham, North Carolina.

Cancer Cytopathology
|February 4, 2020
PubMed
Summary

A machine learning algorithm (MLA) accurately predicts thyroid cancer from fine-needle aspiration biopsy (FNAB) whole slide images (WSIs), comparable to expert pathologists. This tool can help refine diagnoses and reduce unnecessary surgeries.

Keywords:
Bethesda System for Reporting Thyroid Cytopathologymachine learningmalignancy predictionneural networkthyroid fine-needle aspiration (FNA)

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Area of Science:

  • Pathology
  • Machine Learning
  • Oncology

Background:

  • The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) categorizes thyroid fine-needle aspiration biopsies (FNABs).
  • Accurate malignancy prediction is crucial for thyroid nodule management and reducing unindicated surgeries.
  • A machine learning algorithm (MLA) was developed to analyze whole slide images (WSIs) of FNABs for malignancy prediction.

Purpose of the Study:

  • To develop and evaluate a machine learning algorithm (MLA) for predicting thyroid malignancy from fine-needle aspiration biopsy (FNAB) whole slide images (WSIs).
  • To compare the MLA's diagnostic performance against expert cytopathologists.
  • To assess the potential of MLA as an adjunct tool in thyroid nodule diagnosis.

Main Methods:

  • Retrospective analysis of 908 thyroidectomy specimens with preceding FNABs over 8 years.
  • WSIs were created from representative FNAB slides.
  • An MLA was trained to identify follicular cells and predict malignancy, with performance assessed using the area under the receiver operating characteristic curve (AUC).

Main Results:

  • The MLA achieved a sensitivity of 92.0% and specificity of 90.5% in predicting malignancy.
  • The MLA's AUC (0.932) was comparable to that of an expert cytopathologist (0.931).
  • Combining MLA with electronic medical record data improved diagnostic performance.

Conclusions:

  • The MLA demonstrates performance comparable to expert cytopathologists in predicting thyroid malignancy from FNAB WSIs.
  • The MLA shows promise as an adjunctive tool to improve the accuracy of thyroid FNAB interpretation, particularly for indeterminate categories.
  • Integration of MLA with existing diagnostic workflows may enhance diagnostic precision and patient management.