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

Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?
Author Information (click to view)

Sollini M, Cozzi L, Chiti A, Kirienko M,


Sollini M, Cozzi L, Chiti A, Kirienko M, (click to view)

Sollini M, Cozzi L, Chiti A, Kirienko M,

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European journal of radiology 2017 12 0799() 1-8 pii 10.1016/j.ejrad.2017.12.004

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.

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