Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?

Martina Sollini, Luca Cozzi, Arturo Chiti, Margarita Kirienko

Research output: Contribution to journalReview article

Abstract

In thyroid imaging, “texture” refers to the echographic appearence of the parenchyma or a nodule. However, definition of the image characteristics is operator dependent and influenced by the operator's experience. In a more objective texture analysis, a variety of mathematical methods are used to describe image inhomogeneity, allowing assessment of an image by means of quantitative parameters. Moreover, this approach may be used to develop an efficient computer-aided diagnosis (CAD) system to yield a second opinion when differentiating malignant and benign thyroid lesions. The aim of this review is to summarize the available literature data on texture analysis, with and without CAD, in patients with suspected thyroid nodules or differentiated thyroid cancer, and to assess the current state of the approach.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalEuropean Journal of Radiology
Volume99
DOIs
Publication statusPublished - Feb 1 2018

Keywords

  • Differentiated thyroid cancer
  • Machine learning
  • Radiomics
  • Texture analysis
  • Thyroid nodule

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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