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Ultrasound is the most important imaging modalities in thyroid diagnostics. High resolution and high availability make ultrasound an efficient tool in the evaluation of thyroid diseases. Diagnosis must be made on two-dimensional cross-sectional images of a three-dimensional body while the patient is present. Several TI-RADS classification schemes have been introduced to improve documentation of thyroid nodules and make their evaluation more objective. However, a strong user-dependency remains also with the use of TI-RADS criteria. This strong user-dependency results from two sources: (1) image acquisition and (2) image interpretation. 3D ultrasound systems have been available on the market for many years and are in regular use for several clinical applications. The most widely used technologies are 3D/4D transducers. The latest version of the software offers the possibility to evaluate and document thyroid glands and nodules quickly and efficiently with the support of artificial intelligence (AI). Current AI-based TI-RADS solutions will classify this nodule differently, depending on which input image will be provided. The aim was to introduce a sonographic feature of focal thyroid lesions to categorize finding and determine which thyroid nodule requires FNAB or US follow-up. At present, several standardized malignancy risk stratification systems are used, such as ACR TI-RADS (American College of Radiology), EU-TIRADS (European Thyroid Association), K-TIRADS (Korean Society of Thyroid Radiology), and ATA (American Thyroid Association).

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Thyroid Imaging Reporting and Data Systems for Cancer Risk Determination and Artificial Intelligence (TI-RADS for Cancer Risk Determination and Artificial Intelligence)

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  • 发表日期：

    2025年

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  • Turtulici Giovanni,
  • Creteanu Mihai

摘要

Ultrasound is the most important imaging modalities in thyroid diagnostics. High resolution and high availability make ultrasound an efficient tool in the evaluation of thyroid diseases. Diagnosis must be made on two-dimensional cross-sectional images of a three-dimensional body while the patient is present. Several TI-RADS classification schemes have been introduced to improve documentation of thyroid nodules and make their evaluation more objective. However, a strong user-dependency remains also with the use of TI-RADS criteria. This strong user-dependency results from two sources: (1) image acquisition and (2) image interpretation. 3D ultrasound systems have been available on the market for many years and are in regular use for several clinical applications. The most widely used technologies are 3D/4D transducers. The latest version of the software offers the possibility to evaluate and document thyroid glands and nodules quickly and efficiently with the support of artificial intelligence (AI). Current AI-based TI-RADS solutions will classify this nodule differently, depending on which input image will be provided. The aim was to introduce a sonographic feature of focal thyroid lesions to categorize finding and determine which thyroid nodule requires FNAB or US follow-up. At present, several standardized malignancy risk stratification systems are used, such as ACR TI-RADS (American College of Radiology), EU-TIRADS (European Thyroid Association), K-TIRADS (Korean Society of Thyroid Radiology), and ATA (American Thyroid Association).