0 Citations
0 Reads
A novel TIRADS of US classification
Zhuang Yan
Li Cheng
Hua Zhan
Chen Ke
Lin Jiang Li
BioMedical Engineering Online, 2018, 17(1): 82.
Background: Thyroid imaging reporting and data system (TIRADS) is the assessment of a risk stratification of thyroid nodules, usually using a score. However, there is no consensus as to the version of TIRADS for reporting the results of thyroid ultrasound in clinic. The objective of this study is to develop a practical TIRADS with which to categorize thyroid nodules and stratify their malignant risk.
Methods: A TIRADS scoring system was developed to provide more decision levels than standard scoring through the selection of the ultrasound features which include the calcification shape, margins, taller-than-wide, internal echo, blood flow quantization of features, setting of the weight, and calculation of the score. Ultimately, the accuracy of our TIRADS was evaluated by comparing with the results of current vision of TIRADS and thyroid radiologist in 153 patients who had US-guided fine-needle aspiration biopsy.
Results: Classification results showed that the total accuracy reached 97% (100% of malignant and 95% of the benign) in 153 cases (benign: 78, malignant: 75). The percentages of malignancy is defined in our TIRADS were as follows: TIRADS 2 (0% malignancy), TIRADS 3 (3.6% malignancy), TIRADS 4 (17-75% malignancy), and TIRADS 5 (98% malignancy).
Conclusions: We established a novel TIRADS to predict the malignancy risk of the thyroid nodules based on six categories US features by a scoring system, which included a standardized vocabulary and score and a quantified risk assessment. The results showed that objective quantitative classification of thyroid nodules by our TIRADS can be useful in guiding management decisions.
Thyroid nodules; TIRADS ultrasound; Feature extraction; Classification
Select Groups
Select Contacts
swap_vert Order by date
Order by date Order by name