A novel TIRADS of US classification
Abstract 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 stu...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-06-01
|
| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12938-018-0507-3 |
| _version_ | 1830449160352956416 |
|---|---|
| author | Yan Zhuang Cheng Li Zhan Hua Ke Chen Jiang Li Lin |
| author_facet | Yan Zhuang Cheng Li Zhan Hua Ke Chen Jiang Li Lin |
| author_sort | Yan Zhuang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-21T07:51:17Z |
| format | Article |
| id | doaj.art-68d142735d3b4fc9bbf8158df6136a46 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-21T07:51:17Z |
| publishDate | 2018-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-68d142735d3b4fc9bbf8158df6136a462022-12-21T19:11:05ZengBMCBioMedical Engineering OnLine1475-925X2018-06-0117111710.1186/s12938-018-0507-3A novel TIRADS of US classificationYan Zhuang0Cheng Li1Zhan Hua2Ke Chen3Jiang Li Lin4Department of Biomedical Engineering, Sichuan University College of Materials Science and EngineeringChina-Japan Friendship HospitalChina-Japan Friendship HospitalDepartment of Biomedical Engineering, Sichuan University College of Materials Science and EngineeringDepartment of Biomedical Engineering, Sichuan University College of Materials Science and EngineeringAbstract 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.http://link.springer.com/article/10.1186/s12938-018-0507-3Thyroid nodulesTIRADS ultrasoundFeature extractionClassification |
| spellingShingle | Yan Zhuang Cheng Li Zhan Hua Ke Chen Jiang Li Lin A novel TIRADS of US classification BioMedical Engineering OnLine Thyroid nodules TIRADS ultrasound Feature extraction Classification |
| title | A novel TIRADS of US classification |
| title_full | A novel TIRADS of US classification |
| title_fullStr | A novel TIRADS of US classification |
| title_full_unstemmed | A novel TIRADS of US classification |
| title_short | A novel TIRADS of US classification |
| title_sort | novel tirads of us classification |
| topic | Thyroid nodules TIRADS ultrasound Feature extraction Classification |
| url | http://link.springer.com/article/10.1186/s12938-018-0507-3 |
| work_keys_str_mv | AT yanzhuang anoveltiradsofusclassification AT chengli anoveltiradsofusclassification AT zhanhua anoveltiradsofusclassification AT kechen anoveltiradsofusclassification AT jianglilin anoveltiradsofusclassification AT yanzhuang noveltiradsofusclassification AT chengli noveltiradsofusclassification AT zhanhua noveltiradsofusclassification AT kechen noveltiradsofusclassification AT jianglilin noveltiradsofusclassification |