|Year : 2020 | Volume
| Issue : 1 | Page : 11-16
Correlation between thyroid imaging reporting and data system with histopathology in classification of thyroid nodules
Aya E Mohamed1, El Shymaa E Ahmed2, Marwa A Soliman3, Nahed A Abdullatif4
1 Department of Radiology, Faculty of Medicine for Girls, Al Azhar University, Cairo, Egypt
2 Department of General Surgery, Faculty of Medicine for Girls, Al Azhar University, Cairo, Egypt
3 Department of General Pathology, Faculty of Medicine for Girls, Al Azhar University, Cairo, Egypt
4 Department of Radiodiagnosis, Faculty of Medicine for Girls, Al Azhar University, Cairo, Egypt
|Date of Submission||19-Aug-2019|
|Date of Decision||09-Sep-2019|
|Date of Acceptance||10-Sep-2019|
|Date of Web Publication||20-Apr-2020|
Dr. Aya E Mohamed
Almahal Alkobra Algharbia Government
Source of Support: None, Conflict of Interest: None
Objective This study aims to evaluate the reliability of the ACR-thyroid imaging reporting and data system (TIRADS) classification system in predicting thyroid malignancy by using pathology diagnosis as the reference standard.
Patients and methods This was a prospective study that was carried out at Alzahraa University Hospital. Records of patients with focal thyroid nodules on ultrasound for which ultrasound-guided core needle was performed and pathology results were available, from January 2019 to June 2019, were selected for review. Correlation of the American college of Radiology - thyroid imaging reporting and data system (ACR-TIRADS) classification with pathology results was assessed. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated in a conservative and nonconservative method. The threshold for statistical performance was set at 0.05. Each sonographic feature was also compared with its pathology results.
Results A total number of 20 patients with 51 nodules were eligible in the study; seven (13.7%) of 51 nodules were malignant. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 85.7, 97.7, 85.7, 97.7, and 96.1%, respectively.
Conclusion The ACR-TIRADS classification is reliable in predicting thyroid malignancy. More evidence is nevertheless necessary for widespread adaptation and use.
Keywords: ACR-thyroid imaging reporting and data system, core needle biopsy, thyroid nodules
|How to cite this article:|
Mohamed AE, Ahmed EE, Soliman MA, Abdullatif NA. Correlation between thyroid imaging reporting and data system with histopathology in classification of thyroid nodules. Sci J Al-Azhar Med Fac Girls 2020;4:11-6
|How to cite this URL:|
Mohamed AE, Ahmed EE, Soliman MA, Abdullatif NA. Correlation between thyroid imaging reporting and data system with histopathology in classification of thyroid nodules. Sci J Al-Azhar Med Fac Girls [serial online] 2020 [cited 2020 May 30];4:11-6. Available from: http://www.sjamf.eg.net/text.asp?2020/4/1/11/282867
| Introduction|| |
Ultrasonography (US) is the imaging modality of choice for thyroid nodules. High-resolution machine can detect nodules as small as 1–3 mm with sensitivity of 95% . Thyroid nodules are very common findings in the adult population, especially in women. The prevalence on clinical examinations is ∼4–7%, but the widespread use of US has increased it to as high as 67%. The discovery of nonpalpable nodules raises concerns about their possible malignancy. Most of the detected nodules are benign, whereas 3–7% are malignant, mainly affecting patients younger than 20 or older than 60 years . Thyroid US has various advantages, such as high availability, noninvasiveness, relative low cost, and excellent temporal and spatial resolution. US is important in assessment of malignancy risk of thyroid nodules. Most thyroid nodules are incidental findings, but their risk of malignancy correlates well with the finding of subsequent US . Thyroid imaging reporting and data system (TIRADS) was initially presented in 2009 by two independent teams. TIRADS refers to the well-known and widely applied breast imaging reporting and data system (BIRADS) used to describe breast focal lesions in radiography mammography, magnetic resonant mammography, and US imaging to estimate malignancy risk and the need for further diagnostic management . Compared with BIRADS, TIRADS has only five categories from TR1 to TR5. TR1 is assigned to benign nodules (which is contrast to BIRADS, where category 1 is classified as normal breast). There is no TR6 in TIRADS scoring system. There is no subcharacterization of TR4 (as in BIRADS category 4) .
| Patients and methods|| |
This was a prospective study carried out at the Radiology Department of Alzahraa University Hospital from January 2019 to June 2019. After obtaining local medical ethics committee approval and written informed consent from all patients in the study, 20 patients with solitary or multiple thyroid nodules were eligible for the study. The patients’ data were collected from records of patients who underwent US-guided core needle biopsy for detected focal thyroid nodules on US. Some of these patients were then subjected to total and subtotal thyroidectomy according to the results of core needle biopsy, and pathology results were available. Correlation of the US classification with pathology results was done.
Imaging and imaging analysis
All US scans of the thyroid gland and neck were performed using a linear-array transducer (5–12 MHz) on a Philips US scanner (Philips Healthcare Affiniti 70 G, USA) using an optimized gain. All thyroid nodules were characterized according to the internal component (solid, mixed, or cystic), margins, echogenicity, evidence of calcifications, and shape. Margins were classified as well circumscribed, lobulated, or irregular. Echogenicity was classified as ‘hyperechogenicity,’ ‘isoechogenicity,’ ‘hypoechogenicity,’ and ‘marked hypoechogenicity.’ Isoechogenicity was defined as an echogenicity similar to that of the adjacent healthy thyroid gland. A nodule was classified as ‘marked hypoechogenicity’ if the echogenicity was less than that of the superficial surrounding neck muscles. When present, calcifications were categorized as microcalcifications (<3 mm) and macrocalcifications (>3 mm with acoustic shadowing). The shape of the nodule was categorized as ‘taller than wide’ (greater in its anteroposterior dimension than in its transverse dimension) and ‘wider than tall.’ Using the American college of Radiology - thyroid imaging reporting and data system (ACR-TIRADS) classification, each nodule was classified into a TIRADS category (1, 2, 3, 4, and 5) based on the US features. The ACR-TIRADS classification is shown in [Figure 1] and [Table 1].
|Figure 1 Thyroid imaging, reporting and data system (TIRADS): scoring and classification. .|
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|Table 1 Thyroid imaging, reporting and data system: scoring and classification |
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Ultrasound-guided core needle biopsy
After US evaluation of the thyroid gland, US-guided core needle biopsy was performed with 18×10 geotek semiotomatic needle. The harvested tissue was immediately fixed in formalin-filled container labeled with the patient’s name and diagnosis, and then the biopsy is referred to the histopathology laboratory, and the specimen was embedded in paraffin wax to make blocks and stained with hematoxylin and eosin stain. Then the slides are examined by an expert pathologist.
Data collection and analysis
A standardized form was used to collect data. Recorded data were analyzed using the statistical package for social sciences, version 20.0 (SPSS Inc., Chicago, Illinois, USA). Quantitative data were expressed as mean±SD. Qualitative data were expressed as frequency and percentage. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The risk of malignancy of each TIRADS category was determined. The relationship between the benign and the malignant groups with respect to the TIRADS categories was estimated. The threshold for statistical significance was set at 0.05.
| Results|| |
A total of 20 patients with 51 nodules were eligible for the study. There were 15 (75%) female and five (25%) male patients, with the male to female ratio of 1 : 3. The youngest patient was 33 years old and the oldest was 56 years old. There are 15 (75%) benign cases and five (25%) malignant cases, as shown in [Figure 2].
The 15 benign cases were proven by histopathology to be eight (53.4%) cases of colloid nodules, three (20%) cases of follicular adenoma, two (13.3%) cases of lymphocytic thyroiditis, and two (13.3%) cases of hyperplastic nodules, whereas the five (60%) malignant cases were found as three cases of papillary carcinoma, one (20%) case of follicular carcinoma, and one (20%) case of anaplastic thyroid carcinoma. Of the 51 nodules, 44 were benign and seven were malignant. Of the benign nodules, 23 nodules were colloid, eight were follicular adenoma, seven were hyperplastic nodules, and six were lymphocytic thyroiditis. Of the seven malignant nodules, five were papillary carcinoma, one follicular carcinoma, and one nodules anaplastic thyroid carcinoma, as shown in [Table 2].
|Table 2 Type of pathology distribution of the study group according to the number of patients|
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There were four (20%) cases with solitary thyroid nodule, and 16 (80%) case with multiple nodules. The percentage of malignancy in the solitary cases was 75% (three cases), and 25% was benign (one case), as shown in [Table 3].
|Table 3 Relation between pathology diagnosis according to number of nodules of the study group|
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All nodules found in the study population were classified according to ACR-TIRADS classification, and correlation with the histopathological results was done, as shown in [Table 4] and [Table 5].
|Table 4 Relation between pathology diagnosis according to level of TIRADS of the study group|
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|Table 5 Relation between pathology diagnosis according to thyroid imaging reporting and data system score of the study group|
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There was one nodule classified as TIRADS 5 that was benign. It was a case of thyroiditis, confirmed after core needle biopsy.
Receiver operating characteristic curve was used to define the best cutoff value of TIRADS score, which was more than 4, with sensitivity of 85.7%, specificity of 97.7%, PPV of 85.7%, NPV of 97.7%, and diagnostic accuracy of 96.1% ([Figure 3],[Figure 4],[Figure 5] and [Table 6]).
|Figure 3 Receiver operating characteristic (ROC) curve for diagnostic performance of thyroid imaging reporting and data system (TIRADS) score in discrimination of malignant.|
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|Figure 4 Thyroid ultrasound showing isoechoic solid nodule that is wider than taller, smooth margin, and no microcalcifications. This was classified as TR3. Core biopsy was done, and histopathology results revealed follicular adenoma.|
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|Figure 5 Thyroid ultrasound showing isoechoic solid nodule that is taller than wide and contains punctate microcalcifications. This was assessed as very suspicious for malignancy (TR5). Core biopsy was done at first followed by surgical intervention, and pathology results revealed papillary thyroid cancer.|
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|Table 6 Diagnostic performance of thyroid imaging reporting and data system score in discrimination of benign and malignant nodules|
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| Discussion|| |
The acronym TIRADS seems to have come to stay. It harmonizes the reporting of thyroid US findings in a very simply way that facilitates comprehension across different specialties. For any such classification system to be useful for routine clinical practice, it should be simple to use, reproducible, and very reliable .
In our study, the proportion of malignant nodules classified as TIRADS 2 was 0%, and among TIRADS 3, it was 0%. TR 4 classification was 14.3%, and 85.7% as TIRADS 5. The results of the logistic regression model was P value less than 0.001. The current study had the same results of Junior et al.  who correlated TIRADS and fine needle aspiration, and the proportion of malignant nodules classified as TIRADS 2 was 0.8%, and among TIRADS 3, it was 1.7%. TR 4 classification was 16%, and 91.3% as TIRADS 5. The results of the logistic regression model was P value less than 0.001, showing a clear association between TIRADS and biopsy results. The prevalence of thyroid cancer in our sample population accounted for ∼13.7%, whereas the benign nodules accounted for 86.3%. Similar to our results, AbdGhani et al.  discussed the reliability of US in focal thyroid nodules and compared it with the pathology results. Their study was performed on 91 patients having 104 nodules. The patients comprised 80% females 20% males, and their mean age was 54.7 years. Overall, 11.55% of nodules were malignant, and 88.5% were benign. The presence of some US features had earlier been described as highly suspicious for malignancy, and they include marked hypoechogenicity, taller than wide shape, irregular contours, and the presence of calcifications. In our study, 9.8% of all nodules contained microcalcifications, 70% contained macrocalcifications, and 20.2% contained coma tail artifact and peripheral calcifications. Shape taller than wide represented ∼11.7% of all nodules, whereas wider than taller represented 88.3%. Irregular, ill defined, and lobulated margin have the percentage of 18% and smooth margin reached ∼82%. Solid nodules in our study population were ∼47%, whereas other patterns of echogenicity accounted for ∼53%. These results are similar to Pompili et al.  who correlated the characteristic US finding of thyroid nodules to Bethesda system for reporting cytology and found that 14% of all nodules contained microcalcifications, 86% contained macrocalcifications. Shape taller than wide accounted for ∼13% of all nodules, whereas wider than taller represented 87%. Irregular, ill defined has the percentage of 18% and smooth margin reached ∼82%. Solid nodules in the population was ∼43%, whereas other patterns of compositions accounted for ∼57%. In our study, we found a statistically significant difference between benign and malignant according to the number of nodules. The percentage of thyroid carcinoma in patients with solitary thyroid nodules was 75%, whereas the percentage of thyroid carcinoma in patients with multiple thyroid nodules was 7% only. These results were found to be similar to the results of study conducted by Bailey and Wallwork  who found that the incidence of carcinoma in patients with multinodular goiters has been reported to be considerably lower than in patients with a single cold nodule. However, the study conducted by Frates et al.  stated that the likelihood of thyroid cancer is independent of the number of thyroid nodules, and the prevalence of thyroid cancer in their study did not differ between patients with a solitary thyroid nodule (175 of 1181 patients, 14.8%) and patients with multiple nodules (120 of 804 patients, 14.9%). Regarding the different histological types of thyroid carcinoma that were found in the present study population, five (72%) nodules were proven to be papillary carcinoma, one (14%) nodule was follicular carcinoma, and one (14%) other nodule was proven to be squamous cell carcinoma. These results are supported by the results of the study done by Frates et al.  which showed 86.3% were papillary, 12.0% were follicular, and 1.7% were other types of cancer. Sensitivity, specificity, PPV, NPV, and accuracy are important determining factors for diagnostic tests. In our practice, the TIRADS classification had the sensitivity, specificity, PPV, NPV, and accuracy of 85.7, 97.7, 85.7, 97.7, and 96.1%, respectively. These are compared with other similar studies. AbdGhani et al.  show these results to be 100, 91.3, 60, 100, and 92.3%, respectively
| Conclusion|| |
ACR-TIRADS can be considered an appropriate classification in the assessment of thyroid nodules, to avoid unnecessary fine needle aspirations and to assist in making decision about when it should be performed. This classification improves communication and reduces confusion among physicians and patients. Our experience demonstrated that the TIRADS classification is highly reproducible, as it is based on B-mode characteristics of the nodules, especially when performed by experienced radiologists, acquainted with its use.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Bailey S, Wallwork B. Differentiating between benign and malignant thyroid nodules.An evidence-based approach in general practice. Aust J Gen Pract
Pompili GG, Tresoldi S, Ravelli A, Primolevo A, Di Leo G, Carrafiello G. Use of the ultrasound-based totalmalignancy score in the management ofthyroid nodules. Ultrasonography
Biag FN, Liu SYW, Yip SP et al.
Update on ultrasound diagnosis for thyroid cancer. Hong Kong J Radiol
Migda B, Migda M, Migda AM et al.
Evaluation of four variants of thyroid imaging, reporting and data system (TI-RADS) classification in patients with multi nodular goiter. Endokrynol Pol
Grant EG, Tessler FN, Hoang JK et al.
Thyroid ultrasound reporting lexicon: white paper of the ACR thyroid imaging reporting and data system (TI-RADS) Committee. J Am Coll Radiol
Mandel SJ, Langer JE. Thyroid and parathyroid ultrasound and ultrasound guided FNA;Chapter 7of thyroid ultrasound. XXXX
Tessler FN, Middleton WD, Gran EG et al.
ACR thyroid imaging, reporting and data system (TI-RADS): White Paper of the ACR TI-RADS Committee. J Am Coll Radiol
Moifo B, ObenTakoeta E, Tambe J et al.
Reliability of thyroid imaging reporting and data system (TIRADS) classification in differentiating benign from malignant thyroid nodules. Open J Radiol
Junior AR et al.
Correlation of thyroid imaging reporting and data system [TI-RADS] and fine needle aspiration: experience in 1,000 nodules. Einstein (Sau Paulo)
AbdGhani F, Isa NM et al.
Reliability of the ultrasound classification system of thyroid nodules in predicting malignancy. Med J Malaysia
Frates MC, Benson CB, Doubilet PM et al.
Prevalence and distribution of carcinoma in patients with solitary and multiple thyroid nodules on sonography. J Clin Endocrinol Metabol
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]