Role of American College of Radiology Thyroid Imaging Reporting and Data System 2017 in Predicting Thyroid Nodule Malignancy: Cytological and Histological Insights in the Local Population of Pondicherry

Role of American College of Radiology Thyroid Imaging Reporting and Data System 2017 in Predicting Thyroid Nodule Malignancy: Cytological and Histological Insights in the Local Population of Pondicherry | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of American College of Radiology Thyroid Imaging Reporting and Data System 2017 in Predicting Thyroid Nodule Malignancy: Cytological and Histological Insights in the Local Population of Pondicherry Sayan Palit, Vijayalakshmi Krishnamurthy, Krishna Kumar Ramakrishnan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6962488/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Thyroid nodules represent a significant clinical challenge, with potential malignant transformation necessitating precise diagnostic strategies. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 emerged as a promising tool for risk stratification, prompting this comprehensive investigation into its diagnostic utility and correlation with histopathological findings. Methodology: A prospective cross-sectional study was conducted on 73 patients, involving comprehensive ultrasound examination and Fine Needle Aspiration Cytology (FNAC). Nodules were evaluated using ACR-TIRADS 2017 classification system, with detailed analysis of ultrasound features and histopathological correlations. Results: The study revealed a female predominance (86.3%), with most patients aged 20-40 years. TIRADS classification showed TR4 as the most significant risk category, with 77.8% malignancy correlation. Ultrasound features demonstrated strong predictive value: solid composition (77.8% sensitivity), irregular margins (100% specificity), and specific calcification patterns were key malignancy indicators. Colloid Nodular Goitre (34.2%) was the most prevalent benign condition, with Papillary Carcinoma (16.5%) being the most common malignancy. Conclusion: ACR-TIRADS 2017 demonstrates robust potential as a risk stratification tool for thyroid nodules, providing clinically valuable insights into nodule characteristics and malignancy risk. Thyroid Nodules ACR-TIRADS Malignancy FNAC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Thyroid nodules pose a significant diagnostic challenge, with detection rates varying widely based on clinical and imaging techniques. While traditional palpation methods detect nodules in approximately 4–7% of individuals, high-resolution ultrasound can reveal nodular changes in up to 67%. The primary concern remains the potential for malignancy, present in about 5–10% of nodules. Accurate differentiation between benign and malignant nodules is critical to guiding appropriate clinical management and avoiding unnecessary invasive procedures. Current diagnostic strategies include physical examination, ultrasonography, fine-needle aspiration biopsy (FNAB), and, more recently, molecular marker evaluation. Among these, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 has emerged as a standardized, sonographic-based risk stratification tool. By incorporating key ultrasound features into a structured scoring system, ACR-TIRADS offers an objective alternative to more subjective evaluation methods, aiming to streamline diagnostic decisions and improve risk assessment. Despite its structured approach, variability in the reported diagnostic performance of ACR-TIRADS across different populations underscores the need for further validation. This study aims to assess the clinical utility of ACR-TIRADS 2017 in a defined population, focusing on the correlation between sonographic risk categories and histopathological or cytological outcomes. Specific objectives include evaluating its diagnostic accuracy, determining its effectiveness in guiding clinical management, and assessing its role in reducing unnecessary FNABs or surgical interventions. In light of rising thyroid cancer incidence and ongoing advancements in imaging, this study seeks to contribute meaningful evidence to refine thyroid nodule assessment protocols. The findings are expected to support the broader implementation of ACR-TIRADS and enhance patient care through improved diagnostic precision. Materials & Methods Study Design & Setting Hospital-based prospective cross-sectional study was conducted at the Department of Radiodiagnosis, Mahatma Gandhi Medical College & Research Institute, Pondicherry. Data was collected over a period of 22 months (May 2023- March 2025). Ethical clearance was obtained from the Institutional Human Ethics Committee. Inclusion criteria Adults (> 18 yrs) from Puducherry undergoing USG neck for suspected thyroid nodules are included in the study. Exclusion criteria Operated cases, radiation history, pregnancy, bleeding diathesis, non-consent, inconclusive FNAC were excluded. Sample size and Sampling Method A total of 73 samples were taken based on prior studies. Continuous sampling method was used. Study Tools: Ultrasound: GE Logic S7 (7.5–12 MHz), Mindray DC-8B (8–14 MHz) FNAC: 23–25 gauge needles, microscopy setup, fixation materials Ultrasound Assessment Nodule size, echogenicity, margins, calcifications, vascularity was evaluated followed by evaluation of total TIRADS score and determining TIRADS level FNAC Procedure Non-aspiration technique was used followed by fixation of samples and analysed accordingly. Data Management Standardized proforma was prepared and filled based on the USG and Histopathological findings and were subsequently correlated. Statistical Analysis: Tools: Excel and SPSS v21 were used. Tests: Student’s t-test (p < 0.05), descriptive statistics for qualitative & quantitative variables were used. Outcome measures Key outcome measures included sensitivity, specificity, Positive Predictive Value and Negative Predictive Value assessment of four most important ultrasound features of TIRADS classification. Also, the strength of TIRADS to predict whether they are benign/malignant was also assessed. Data Safety and Ethical Considerations All data was anonymized before analysis. The study involved minimal patient contact and posed minimal risk. Informed consent through the help of Patient Information Sheet (PIS) was obtained before USG Neck and FNAC separately. Results This study investigated the demographic, ultrasonographic, and histopathological characteristics of thyroid nodules in the context of ACR-TIRADS 2017 scoring. The majority of patients ( 53.4% ) were in the 20–40-year age group , representing a young to middle-aged adult population. Patients at the extremes of age were relatively few. A significant female predominance (86.3%) was noted, in line with known epidemiological patterns of thyroid disease. Thyroid nodules showed nearly equal laterality , though left-sided nodules (56.7%) were slightly more common. Most nodules ( 67% ) measured between 2–4 cm , while smaller nodules (< 2 cm) made up 13.7% . Larger and very small nodules were infrequent. In terms of ultrasound features , 74% of nodules were mixed cystic-solid , whereas 26% were purely solid. Echogenicity was varied: 60.3% were isoechoic or hypoechoic , 38.4% purely hypoechoic, and only 1.4% were anechoic. Margins were smooth in 72.6% , ill-defined in 21.9% , and irregular in a few cases. All nodules were wider than tall , a generally benign feature. Echogenic foci were absent in 73% of nodules. Among those with foci, macrocalcifications were present in 13.7% , rim calcifications in 8.2% , punctate foci in 2.7% , and combined macro and rim calcifications in 1.4% . The ACR-TIRADS score distribution showed that 23.3% had a score of 2, 47.9% had a score of 3, and 27.4% scored between 4–6, with only 1.4% scoring 7. Corresponding TR categories revealed most nodules were low to moderate risk: TR3 (47.9%) , TR4 (27.4%) , TR2 (23.3%) , and TR5 (1.4%) (Refer to Tables 1 and 2 ) . Histopathological and cytological correlation showed that benign nodules were more common. Colloid nodular goitre was the most prevalent benign condition ( 34.2% ), followed by Hashimoto’s thyroiditis (13.7%) and nodular goitre (12.4%) . Among malignant cases , papillary carcinoma was dominant ( 16.5% ), followed by follicular neoplasm (4.1%) and atypical cases (Refer to Table 3 ). Comparative analysis revealed significant associations between ultrasound features and final diagnosis . Mixed composition was found in 90.9% of benign nodules, while 77.8% of malignant nodules were solid . Echogenicity patterns showed that no benign nodules were anechoic, while 83.3% of malignant nodules were isoechoic or hyperechoic . Smooth margins were seen in 94.5% of benign nodules , but only 5.6% of malignant ones , with 72.2% of malignant nodules exhibiting ill-defined margins . Peripheral/rim calcifications were notably more common in malignant lesions (Refer to Table 4 ) . Overall, the study highlights strong correlations between ACR-TIRADS-based ultrasound findings and histopathological outcomes, supporting its role as a valuable, non-invasive diagnostic tool for thyroid nodule risk stratification (Refer to Tables 5 and 6 ). Table 1 Distribution of patients according to total scores Total scores Frequency Percentage Zero - - Two 17 23.3% Three 35 47.9% Four to Six 20 27.4% More than or equal to seven One 1.4% Total 73 100% Table 2 Distribution of patients according to TIRADS level TIRADS level Frequency Percentage TR2 17 23.3% TR3 35 47.9% TR4 20 27.4% TR5 One 1.4% Total 73 100% Table 3 Distribution of patients according to FNAC findings FNAC findings Frequency Percentage Benign Nodular goiter Nine 12.4% Adenomatous nodular Four 5.5% Benign follicular lesion Four 5.5% Colloid nodular goiter 25 34.2% Granulomatous thyroiditis One 1.4% Hashimoto’s thyroiditis 10 13.7% Lymphocytic thyroiditis One 1.4% Subacute thyroiditis One 1.4% Malignant Papillary carcinoma 12 Poorly differentiated carcinoma One 16.5% Atypical Two 1.4% Follicular neoplasm Three 2.7% Table 4 Association of USG features with type of lesion USG features Type of lesion p-value Benign malignant Composition Mixed (cystic + solid) 50 (90.9%) Four (22.2%) < 0.001 Solid Five (9.1%) 14 (77.8%) Echogenicity Anechoic 0 One (5.6%) 0.007 Hyperechoic/isoechoic 29 (52.7%) 15 (83.3%) Hypoechoic 26 (47.3%) Two (11.1%) Shape- wider than tall 55 (1000%) 18 (100%) - Margins of nodule Smooth 52 (94.5%) One (5.6%) < 0.001 Ill defined Three (5.5%) 13 (72.2%) Irregular 0 Four (22.2%) Echogenic foci of nodule None One (1.8%) 0 0.008 Macro calcifications Eight (13.5%) Two (11.1%) Peripheral/rim calcifications One (1.8%) Five (27.8%) Macro + rim calcification 0 One (5.6%) Punctate echogenic foci One (1.8%) One (5.6%) Table 5 Association of TIRADS with type of lesion TIRADS Type of lesion p-value Benign malignant TR2 17 (30.9%) 0 < 0.001 TR3 32 (58.2%) Three (16.7%) TR4 Six (10.9%) 14 (77.8%) TR5 0 One (5.6%) Total 55 (100%) 18 (100%) Table 6 Sensitivity analysis of USG features USG features Sensitivity Specificity NPV PPV Solid composition 77.8% 90.9% 92.6% 73.6% Hypo echogenicity 11.1% 52.7% 64.4% 7.1% Irregular margins 22.2% 100% 79.7% 100% Punctate echogenic foci 5.5% 98.2% 76.1% 50% Discussion Thyroid nodules continue to present a significant clinical dilemma due to their potential for malignant transformation, necessitating accurate and efficient diagnostic approaches. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 has emerged as a promising risk stratification tool, and our study aimed to evaluate its clinical utility by correlating TIRADS categories with histopathological findings. Our findings reflect well-established epidemiological trends, particularly the strong female predominance (86.3%) in the study population, consistent with known gender disparities in thyroid disorders. Similarly, the age distribution—predominantly young to middle-aged adults (20–40 years)—aligns with previous studies, underscoring the demographic segment most commonly affected by thyroid nodules. Ultrasound characteristics offered key diagnostic insights. The majority of nodules exhibited a mixed cystic-solid composition (74%). Importantly, solid composition was significantly associated with malignancy (p < 0.001), affirming its diagnostic relevance, as highlighted by Hoang et al. Our analysis also revealed the predictive value of ACR-TIRADS, with TR4 nodules showing the highest malignancy rates (77.8%), further validating the system’s clinical accuracy in identifying suspicious lesions, as reported by Yucel et al. Histopathological correlations supported these observations. Colloid nodular goitre was the most common benign diagnosis (34.2%), while papillary carcinoma was the predominant malignancy (16.5%), consistent with findings by Durante et al. FNAC results reinforced the utility of combining imaging with cytological assessment for comprehensive evaluation. Diagnostic performance metrics were particularly notable for solid composition, which demonstrated 77.8% sensitivity and 90.9% specificity—figures comparable to those reported by Shin et al. Additionally, irregular margins showed 100% specificity for malignancy, albeit with lower sensitivity, echoing Russ et al.'s conclusions on the diagnostic value of margin characteristics. Peripheral or rim calcifications were more frequently seen in malignant nodules, in agreement with the findings of Moraes PHM et al. Clinically, our results underscore the effectiveness of ACR-TIRADS 2017 in stratifying thyroid nodules by malignancy risk. Its implementation could guide appropriate clinical management, reduce unnecessary biopsies, and enhance early detection. Limitations and Recommendations This study's primary limitations include its single-centre design and limited sample size , which may affect the generalizability and statistical strength of the findings. The results may not fully represent broader, more diverse populations or less common thyroid pathologies. To enhance validity, future research should involve multi-centre studies with larger, heterogeneous cohorts . Such studies would allow more robust validation of ACR-TIRADS across varied demographics. Additionally, integrating longitudinal follow-up and molecular diagnostics with ultrasound assessment may further refine risk stratification, improve diagnostic accuracy, and reduce unnecessary interventions in thyroid nodule evaluation. Conclusion The comprehensive investigation into the role of ACR-TIRADS 2017 in thyroid nodule risk stratification yielded significant insights into the diagnostic potential of ultrasound characteristics in differentiating benign and malignant thyroid nodules. The study conclusively demonstrated the robust predictive capabilities of the TIRADS classification system in assessing thyroid nodule malignancy risk. The most critical findings emerge from the correlation between ultrasound features and histopathological outcomes. Solid nodule composition, irregular margins, and specific calcification patterns emerged as the most reliable indicators of potential malignancy. The TIRADS classification, particularly at TR4 level, showed remarkable accuracy in identifying high-risk nodules, with 77.8% of TR4 nodules being confirmed as malignant through Fine Needle Aspiration Cytology (FNAC). The gender & age distribution of the study highlighted predominance of thyroid nodules in young to middle-aged females, consistent with existing epidemiological understanding. The near-equal distribution of nodules between left and right thyroid lobes suggests no significant laterality preference. Statistically significant associations were observed between various ultrasound features and lesion type, reinforcing the importance of comprehensive imaging assessment. The study validates ACR-TIRADS 2017 as a valuable tool for clinicians in making informed decisions about further diagnostic interventions and potential surgical management of thyroid nodules. Abbreviations • ACR American College of Radiology • TIRADS Thyroid Imaging Reporting and Data System • FNAB Fine–Needle Aspiration Biopsy • USG Ultrasonography / Ultrasound • FNAC Fine–Needle Aspiration Cytology • PIS Patient Information Sheet • SPSS Statistical Package for the Social Sciences • TR TIRADS Risk (as in TR2, TR3, etc., part of the TIRADS classification) Declarations Ethics approval and Consent to Participate : Yes, Institutional Human Ethics Committee, Mahatma Gandhi Medical College & Research Institute, Pondicherry (Approval No.: MGMCRI/Res/01/2022/56/IHEC/83). Consent was taken individually from each participant. Consent for publication : Taken from individual persons through institutional consent form. Funding: Self funded study. Acknowledgements : Not applicable. Author Contribution SP did both the USG Neck and FNAC of all patients & analyzed and interpreted the whole data and compiled it. KV and KKR reviewed the entire process and helped in writing the results, discussion and conclusion part. Pathological findings and all histopathological images were compiled and reviewed by KS. All authors reviewed the manuscript at last. Data Availability Datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Hoang JK, Langer JE, Middleton WD, Wu CC, et al. Managing Incidental Thyroid Nodules Detected on Imaging: White Paper of the ACR Incidental Thyroid Findings Committee. Journal of the American College of Radiology. 2015;12(2):143–150. Yucel S, Balci IG, Tomak L. Diagnostic Performance of Thyroid Nodule Risk Stratification Systems: Comparison of ACR-TIRADS, EU-TIRADS, K-TIRADS, and ATA Guidelines. Ultrasound Q. 2023;39(4):206–211. doi: 10.1097/RUQ.0000000000000653 . PMID: 37918114. Durante C, Grani G, Lamartina L, Soldini F, et al. The Diagnosis and Management of Thyroid Nodules Affecting Clinical Practice. Journal of Clinical Endocrinology & Metabolism. 2018;103(7):2743–2760. Shin JH, Baek JH, Chung J, Ha EJ, et al. Ultrasound Diagnosis of Thyroid Nodules: A Review of the Current Literature. Korean Journal of Radiology. 2016;17(4):758–766. Russ G, Bonnema SJ, Erdogan MF, Garg S, et al. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EAGLE Study. European Thyroid Journal. 2019;8(4):183–190. Moraes PHM, Sigrist R, Takahashi MS, Schelini M, Chammas MC. Ultrasound elastography in the evaluation of thyroid nodules: evolution of a promising diagnostic tool for predicting the risk of malignancy. Radiol Bras. 2019 Jul-Aug;52(4):247–253. doi: 10.1590/0100-3984.2018.0084. PMID: 31435087; PMCID: PMC6696751. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6962488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483070800,"identity":"a416dea9-479e-4b0e-ada3-7e460a7fb93e","order_by":0,"name":"Sayan Palit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBADGTYGxsYHH//ZANmMjQeI0cLDxsbcbDiDLQ2kpYE4LQxs7G3SPGyHwTy8WuTbzxh+Ltxhx8Mn3wjUwnPebm37YaAtNTbRuLQYnMkxlp55JhnoMMZmyzkSt5O3nUkEajmWltuASwtDjoE0bxszSEvjjTcGt5PNDgC1MDYcxqlFvv+N8W/etnqQlgYJnoRzyWbnH+LXwnAjxwxoy2GQliZJngMH7MxuELDF4MazMmveM8eBWhKbDWc2JCeY3QDakoDHL/L9yZtv8+6olpNvPv7wwccGO3uz8+kPH3yoscHtMAYOA2DcIbiJYHYCTuUgwP4ARYs9XsWjYBSMglEwIgEAigJf3lrNassAAAAASUVORK5CYII=","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Sayan","middleName":"","lastName":"Palit","suffix":""},{"id":483070801,"identity":"2b68bc05-a56b-4550-951d-83686cb992c5","order_by":1,"name":"Vijayalakshmi Krishnamurthy","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Vijayalakshmi","middleName":"","lastName":"Krishnamurthy","suffix":""},{"id":483070802,"identity":"a9ff411b-fa39-47a3-be5e-7629692a699c","order_by":2,"name":"Krishna Kumar Ramakrishnan","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"Kumar","lastName":"Ramakrishnan","suffix":""},{"id":483070803,"identity":"46bb2b55-66d2-4818-8aa9-818656db87be","order_by":3,"name":"Shanmugasamy Kathirvelu","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shanmugasamy","middleName":"","lastName":"Kathirvelu","suffix":""}],"badges":[],"createdAt":"2025-06-24 07:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6962488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6962488/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86660845,"identity":"41ee9595-c1a3-45a3-93f7-e1e58e79f5ab","added_by":"auto","created_at":"2025-07-14 10:37:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329886,"visible":true,"origin":"","legend":"\u003cp\u003eCase Number - One: Case No. 1 : A 45 year old female came with c/o swelling of neck. USG image showing a well defined heterogenous predominantly solid, isoechoic, wider than taller lesion with lobulated margins and no e/o echogenic foci/ vascularity on color doppler. Total score – Five, TIRADS level – TR4. FNAC showed features suggestive of Papillary cell carcinoma.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/c105209ddd2eebacf0d9a486.png"},{"id":86660848,"identity":"8e0e3d15-59d0-4e55-bc49-256695904854","added_by":"auto","created_at":"2025-07-14 10:37:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439307,"visible":true,"origin":"","legend":"\u003cp\u003eCase Number - Two: A 30 year old female with c/o swelling in the neck for the last 8 months. USG image showing a solid predominantly isoechoic lesion which is wider than taller with smooth margins and few punctate echogenic foci noted in the right lobe of thyroid. (Total score – Six, TIRADS level – TR4). FNAC showed Follicular Neoplasm (Bethesda IV).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/5ec918d42d824351c1943fb2.png"},{"id":86662678,"identity":"ca6acd40-f2b8-49b1-b98a-53e63e0a802a","added_by":"auto","created_at":"2025-07-14 10:45:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":635255,"visible":true,"origin":"","legend":"\u003cp\u003eCase Number - Three: A 33 year old female with history of hypothyroidism came for USG Neck. USG image showing a well-defined heterogenous mixed cystic solid predominantly isoechoic lesion is noted replacing nearly the entire lobe of left thyroid which is wider than taller with smooth margins and comet tail artifacts noted. (Total score – Two, TIRADS level: TR2). Histopathology showed Colloid Goitre with hyperplastic features.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/58461b8a7d3c6c178ac0dd4d.png"},{"id":86662679,"identity":"6ccf3ace-f74b-4624-825c-4cf88083027f","added_by":"auto","created_at":"2025-07-14 10:45:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261362,"visible":true,"origin":"","legend":"\u003cp\u003eCase Number - Four: A 39 year old female came with c/o mild swelling in the neck. USG image showing a well-defined mixed cystic solid predominantly hypoechoic wider than taller lesion (largest lesion shown in image) noted in the left lobe of thyroid with smooth margins and no echogenic foci noted. The lesion shows increased vascularity on colour doppler (Image B) Thyroid gland was enlarged with lobulated outline. (Total score – Three, TIRADS level : TR 3). Histopathology showed Lymphocytic Thyroiditis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/0ab528602a60b1609db13dfa.png"},{"id":86660854,"identity":"06a8e466-2ff0-4bbe-b094-503599f407e5","added_by":"auto","created_at":"2025-07-14 10:37:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":463481,"visible":true,"origin":"","legend":"\u003cp\u003eCase Number - Five: A 46 year old female came with c/o fever and swelling of neck. A. USG image showing enlarged left lobe of thyroid with heterogenous parenchyma and multiple well defined tiny mixed cystic solid, wider than taller with smooth margins and no echogenic foci predominantly hypoechoic nodules which were separated by fibrous echogenic septa. It also showed increased vascularity on colour doppler (Total score of largest nodule on left side – Three, TIRADS level : TR3. Histopathology showed features suggestive of Lymphocytic Thyroiditis.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/0b5281c15bb841076bb75b0a.png"},{"id":88713687,"identity":"6db82014-22c1-48fd-86e1-f5a5e0ca03dd","added_by":"auto","created_at":"2025-08-10 08:38:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3617343,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6962488/v1/f0aa39b7-8d34-44ca-8812-50e2836a3734.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRole of American College of Radiology Thyroid Imaging Reporting and Data System 2017 in Predicting Thyroid Nodule Malignancy: Cytological and Histological Insights in the Local Population of Pondicherry\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThyroid nodules pose a significant diagnostic challenge, with detection rates varying widely based on clinical and imaging techniques. While traditional palpation methods detect nodules in approximately 4\u0026ndash;7% of individuals, high-resolution ultrasound can reveal nodular changes in up to 67%. The primary concern remains the potential for malignancy, present in about 5\u0026ndash;10% of nodules. Accurate differentiation between benign and malignant nodules is critical to guiding appropriate clinical management and avoiding unnecessary invasive procedures.\u003c/p\u003e\u003cp\u003eCurrent diagnostic strategies include physical examination, ultrasonography, fine-needle aspiration biopsy (FNAB), and, more recently, molecular marker evaluation. Among these, the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 has emerged as a standardized, sonographic-based risk stratification tool. By incorporating key ultrasound features into a structured scoring system, ACR-TIRADS offers an objective alternative to more subjective evaluation methods, aiming to streamline diagnostic decisions and improve risk assessment.\u003c/p\u003e\u003cp\u003eDespite its structured approach, variability in the reported diagnostic performance of ACR-TIRADS across different populations underscores the need for further validation. This study aims to assess the clinical utility of ACR-TIRADS 2017 in a defined population, focusing on the correlation between sonographic risk categories and histopathological or cytological outcomes. Specific objectives include evaluating its diagnostic accuracy, determining its effectiveness in guiding clinical management, and assessing its role in reducing unnecessary FNABs or surgical interventions.\u003c/p\u003e\u003cp\u003eIn light of rising thyroid cancer incidence and ongoing advancements in imaging, this study seeks to contribute meaningful evidence to refine thyroid nodule assessment protocols. The findings are expected to support the broader implementation of ACR-TIRADS and enhance patient care through improved diagnostic precision.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design \u0026amp; Setting\u003c/strong\u003e\u003cp\u003eHospital-based prospective cross-sectional study was conducted at the Department of Radiodiagnosis, Mahatma Gandhi Medical College \u0026amp; Research Institute, Pondicherry. Data was collected over a period of 22 months (May 2023- March 2025). Ethical clearance was obtained from the Institutional Human Ethics Committee.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003cp\u003eAdults (\u0026gt;\u0026thinsp;18 yrs) from Puducherry undergoing USG neck for suspected thyroid nodules are included in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003cp\u003eOperated cases, radiation history, pregnancy, bleeding diathesis, non-consent, inconclusive FNAC were excluded.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSample size and Sampling Method\u003c/strong\u003e\u003cp\u003eA total of 73 samples were taken based on prior studies. Continuous sampling method was used.\u003c/p\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Tools:\u003c/h2\u003e\u003cp\u003eUltrasound: GE Logic S7 (7.5\u0026ndash;12 MHz), Mindray DC-8B (8\u0026ndash;14 MHz)\u003c/p\u003e\u003cp\u003eFNAC: 23\u0026ndash;25 gauge needles, microscopy setup, fixation materials\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eUltrasound Assessment\u003c/strong\u003e\u003cp\u003eNodule size, echogenicity, margins, calcifications, vascularity was evaluated followed by evaluation of total TIRADS score and determining TIRADS level\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFNAC Procedure\u003c/strong\u003e\u003cp\u003eNon-aspiration technique was used followed by fixation of samples and analysed accordingly.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Management\u003c/strong\u003e\u003cp\u003eStandardized proforma was prepared and filled based on the USG and Histopathological findings and were subsequently correlated.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eTools: Excel and SPSS v21 were used.\u003c/p\u003e\u003cp\u003eTests: Student\u0026rsquo;s t-test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), descriptive statistics for qualitative \u0026amp; quantitative variables were used.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutcome measures\u003c/strong\u003e\u003cp\u003eKey outcome measures included sensitivity, specificity, Positive Predictive Value and Negative Predictive Value assessment of four most important ultrasound features of TIRADS classification. Also, the strength of TIRADS to predict whether they are benign/malignant was also assessed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Safety and Ethical Considerations\u003c/strong\u003e\u003cp\u003eAll data was anonymized before analysis. The study involved minimal patient contact and posed minimal risk. Informed consent through the help of Patient Information Sheet (PIS) was obtained before USG Neck and FNAC separately.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study investigated the demographic, ultrasonographic, and histopathological characteristics of thyroid nodules in the context of ACR-TIRADS 2017 scoring. The majority of patients (\u003cb\u003e53.4%\u003c/b\u003e) were in the \u003cb\u003e20\u0026ndash;40-year age group\u003c/b\u003e, representing a young to middle-aged adult population. Patients at the extremes of age were relatively few. A significant \u003cb\u003efemale predominance (86.3%)\u003c/b\u003e was noted, in line with known epidemiological patterns of thyroid disease.\u003c/p\u003e\u003cp\u003eThyroid nodules showed \u003cb\u003enearly equal laterality\u003c/b\u003e, though \u003cb\u003eleft-sided nodules (56.7%)\u003c/b\u003e were slightly more common. Most nodules (\u003cb\u003e67%\u003c/b\u003e) measured between \u003cb\u003e2\u0026ndash;4 cm\u003c/b\u003e, while smaller nodules (\u0026lt;\u0026thinsp;2 cm) made up \u003cb\u003e13.7%\u003c/b\u003e. Larger and very small nodules were infrequent.\u003c/p\u003e\u003cp\u003eIn terms of \u003cb\u003eultrasound features\u003c/b\u003e, \u003cb\u003e74%\u003c/b\u003e of nodules were \u003cb\u003emixed cystic-solid\u003c/b\u003e, whereas \u003cb\u003e26%\u003c/b\u003e were purely solid. \u003cb\u003eEchogenicity\u003c/b\u003e was varied: \u003cb\u003e60.3%\u003c/b\u003e were \u003cb\u003eisoechoic or hypoechoic\u003c/b\u003e, \u003cb\u003e38.4%\u003c/b\u003e purely hypoechoic, and only \u003cb\u003e1.4%\u003c/b\u003e were anechoic. \u003cb\u003eMargins\u003c/b\u003e were \u003cb\u003esmooth in 72.6%\u003c/b\u003e, \u003cb\u003eill-defined in 21.9%\u003c/b\u003e, and irregular in a few cases. All nodules were \u003cb\u003ewider than tall\u003c/b\u003e, a generally benign feature.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEchogenic foci\u003c/b\u003e were absent in \u003cb\u003e73%\u003c/b\u003e of nodules. Among those with foci, \u003cb\u003emacrocalcifications\u003c/b\u003e were present in \u003cb\u003e13.7%\u003c/b\u003e, \u003cb\u003erim calcifications\u003c/b\u003e in \u003cb\u003e8.2%\u003c/b\u003e, \u003cb\u003epunctate foci\u003c/b\u003e in \u003cb\u003e2.7%\u003c/b\u003e, and \u003cb\u003ecombined macro and rim calcifications\u003c/b\u003e in \u003cb\u003e1.4%\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe \u003cb\u003eACR-TIRADS score distribution\u003c/b\u003e showed that \u003cb\u003e23.3%\u003c/b\u003e had a score of 2, \u003cb\u003e47.9%\u003c/b\u003e had a score of 3, and \u003cb\u003e27.4%\u003c/b\u003e scored between 4\u0026ndash;6, with only \u003cb\u003e1.4%\u003c/b\u003e scoring 7. Corresponding \u003cb\u003eTR categories\u003c/b\u003e revealed most nodules were low to moderate risk: \u003cb\u003eTR3 (47.9%)\u003c/b\u003e, \u003cb\u003eTR4 (27.4%)\u003c/b\u003e, \u003cb\u003eTR2 (23.3%)\u003c/b\u003e, and \u003cb\u003eTR5 (1.4%) (Refer to\u003c/b\u003e Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHistopathological and cytological correlation\u003c/b\u003e showed that \u003cb\u003ebenign nodules\u003c/b\u003e were more common. \u003cb\u003eColloid nodular goitre\u003c/b\u003e was the most prevalent benign condition (\u003cb\u003e34.2%\u003c/b\u003e), followed by \u003cb\u003eHashimoto\u0026rsquo;s thyroiditis (13.7%)\u003c/b\u003e and \u003cb\u003enodular goitre (12.4%)\u003c/b\u003e. Among \u003cb\u003emalignant cases\u003c/b\u003e, \u003cb\u003epapillary carcinoma\u003c/b\u003e was dominant (\u003cb\u003e16.5%\u003c/b\u003e), followed by \u003cb\u003efollicular neoplasm (4.1%)\u003c/b\u003e and atypical cases \u003cb\u003e(Refer to\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComparative analysis revealed significant associations between \u003cb\u003eultrasound features and final diagnosis\u003c/b\u003e. \u003cb\u003eMixed composition\u003c/b\u003e was found in \u003cb\u003e90.9%\u003c/b\u003e of benign nodules, while \u003cb\u003e77.8%\u003c/b\u003e of malignant nodules were \u003cb\u003esolid\u003c/b\u003e. \u003cb\u003eEchogenicity\u003c/b\u003e patterns showed that \u003cb\u003eno benign nodules\u003c/b\u003e were anechoic, while \u003cb\u003e83.3% of malignant nodules\u003c/b\u003e were \u003cb\u003eisoechoic or hyperechoic\u003c/b\u003e. \u003cb\u003eSmooth margins\u003c/b\u003e were seen in \u003cb\u003e94.5% of benign nodules\u003c/b\u003e, but only \u003cb\u003e5.6% of malignant ones\u003c/b\u003e, with \u003cb\u003e72.2% of malignant nodules\u003c/b\u003e exhibiting \u003cb\u003eill-defined margins\u003c/b\u003e. \u003cb\u003ePeripheral/rim calcifications\u003c/b\u003e were notably more common in \u003cb\u003emalignant lesions (Refer to\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eOverall, the study highlights strong correlations between ACR-TIRADS-based ultrasound findings and histopathological outcomes, supporting its role as a valuable, non-invasive diagnostic tool for thyroid nodule risk stratification \u003cb\u003e(Refer to\u003c/b\u003e Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of patients according to total scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal scores\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZero\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTwo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThree\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFour to Six\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMore than or equal to seven\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of patients according to TIRADS level\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIRADS level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTR2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTR3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTR4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTR5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e73\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of patients according to FNAC findings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFNAC findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNodular goiter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenomatous nodular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenign follicular lesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColloid nodular goiter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGranulomatous thyroiditis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHashimoto\u0026rsquo;s thyroiditis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytic thyroiditis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubacute thyroiditis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalignant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePapillary carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoorly differentiated carcinoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtypical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollicular neoplasm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of USG features with type of lesion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUSG features\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eType of lesion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emalignant\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComposition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed (cystic\u0026thinsp;+\u0026thinsp;solid)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (90.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFour (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFive (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (77.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEchogenicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnechoic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperechoic/isoechoic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (83.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypoechoic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTwo (11.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShape- wider than tall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (1000%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMargins of nodule\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmooth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIll defined\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThree (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (72.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFour (22.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEchogenic foci of nodule\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro calcifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEight (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTwo (11.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral/rim calcifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFive (27.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacro\u0026thinsp;+\u0026thinsp;rim calcification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePunctate echogenic foci\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOne (1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of TIRADS with type of lesion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTIRADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eType of lesion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBenign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emalignant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThree (16.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSix (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (77.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTR5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOne (5.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e55 (100%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e18 (100%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensitivity analysis of USG features\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUSG features\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNPV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePPV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolid composition\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypo echogenicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIrregular margins\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePunctate echogenic foci\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThyroid nodules continue to present a significant clinical dilemma due to their potential for malignant transformation, necessitating accurate and efficient diagnostic approaches. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 has emerged as a promising risk stratification tool, and our study aimed to evaluate its clinical utility by correlating TIRADS categories with histopathological findings.\u003c/p\u003e\u003cp\u003eOur findings reflect well-established epidemiological trends, particularly the strong female predominance (86.3%) in the study population, consistent with known gender disparities in thyroid disorders. Similarly, the age distribution\u0026mdash;predominantly young to middle-aged adults (20\u0026ndash;40 years)\u0026mdash;aligns with previous studies, underscoring the demographic segment most commonly affected by thyroid nodules.\u003c/p\u003e\u003cp\u003eUltrasound characteristics offered key diagnostic insights. The majority of nodules exhibited a mixed cystic-solid composition (74%). Importantly, solid composition was significantly associated with malignancy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), affirming its diagnostic relevance, as highlighted by Hoang et al. Our analysis also revealed the predictive value of ACR-TIRADS, with TR4 nodules showing the highest malignancy rates (77.8%), further validating the system\u0026rsquo;s clinical accuracy in identifying suspicious lesions, as reported by Yucel et al.\u003c/p\u003e\u003cp\u003eHistopathological correlations supported these observations. Colloid nodular goitre was the most common benign diagnosis (34.2%), while papillary carcinoma was the predominant malignancy (16.5%), consistent with findings by Durante et al. FNAC results reinforced the utility of combining imaging with cytological assessment for comprehensive evaluation.\u003c/p\u003e\u003cp\u003eDiagnostic performance metrics were particularly notable for solid composition, which demonstrated 77.8% sensitivity and 90.9% specificity\u0026mdash;figures comparable to those reported by Shin et al. Additionally, irregular margins showed 100% specificity for malignancy, albeit with lower sensitivity, echoing Russ et al.'s conclusions on the diagnostic value of margin characteristics. Peripheral or rim calcifications were more frequently seen in malignant nodules, in agreement with the findings of Moraes PHM et al.\u003c/p\u003e\u003cp\u003eClinically, our results underscore the effectiveness of ACR-TIRADS 2017 in stratifying thyroid nodules by malignancy risk. Its implementation could guide appropriate clinical management, reduce unnecessary biopsies, and enhance early detection.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations and Recommendations\u003c/strong\u003e\u003cp\u003eThis study's primary limitations include its \u003cb\u003esingle-centre design\u003c/b\u003e and \u003cb\u003elimited sample size\u003c/b\u003e, which may affect the generalizability and statistical strength of the findings. The results may not fully represent broader, more diverse populations or less common thyroid pathologies. To enhance validity, future research should involve \u003cb\u003emulti-centre studies\u003c/b\u003e with \u003cb\u003elarger, heterogeneous cohorts\u003c/b\u003e. Such studies would allow more robust validation of ACR-TIRADS across varied demographics. Additionally, integrating \u003cb\u003elongitudinal follow-up\u003c/b\u003e and \u003cb\u003emolecular diagnostics\u003c/b\u003e with ultrasound assessment may further refine risk stratification, improve diagnostic accuracy, and reduce unnecessary interventions in thyroid nodule evaluation.\u003c/p\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe comprehensive investigation into the role of ACR-TIRADS 2017 in thyroid nodule risk stratification yielded significant insights into the diagnostic potential of ultrasound characteristics in differentiating benign and malignant thyroid nodules. The study conclusively demonstrated the robust predictive capabilities of the TIRADS classification system in assessing thyroid nodule malignancy risk.\u003c/p\u003e\u003cp\u003eThe most critical findings emerge from the correlation between ultrasound features and histopathological outcomes. Solid nodule composition, irregular margins, and specific calcification patterns emerged as the most reliable indicators of potential malignancy. The TIRADS classification, particularly at TR4 level, showed remarkable accuracy in identifying high-risk nodules, with 77.8% of TR4 nodules being confirmed as malignant through Fine Needle Aspiration Cytology (FNAC). The gender \u0026amp; age distribution of the study highlighted predominance of thyroid nodules in young to middle-aged females, consistent with existing epidemiological understanding. The near-equal distribution of nodules between left and right thyroid lobes suggests no significant laterality preference. Statistically significant associations were observed between various ultrasound features and lesion type, reinforcing the importance of comprehensive imaging assessment. The study validates ACR-TIRADS 2017 as a valuable tool for clinicians in making informed decisions about further diagnostic interventions and potential surgical management of thyroid nodules.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eACR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican College of Radiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eTIRADS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThyroid Imaging Reporting and Data System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eFNAB\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFine\u0026ndash;Needle Aspiration Biopsy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eUSG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUltrasonography / Ultrasound\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eFNAC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFine\u0026ndash;Needle Aspiration Cytology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003ePIS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePatient Information Sheet\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eTR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTIRADS Risk (as in TR2, TR3, etc., part of the TIRADS classification)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cb\u003eEthics approval and Consent to Participate\u003c/b\u003e :\u003c/strong\u003e\u003cp\u003eYes, Institutional Human Ethics Committee, Mahatma Gandhi Medical College \u0026amp; Research Institute, Pondicherry (Approval No.: MGMCRI/Res/01/2022/56/IHEC/83). Consent was taken individually from each participant.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eConsent for publication\u003c/b\u003e :\u003c/strong\u003e\u003cp\u003eTaken from individual persons through institutional consent form.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eSelf funded study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements : Not applicable.\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSP did both the USG Neck and FNAC of all patients \u0026amp; analyzed and interpreted the whole data and compiled it. KV and KKR reviewed the entire process and helped in writing the results, discussion and conclusion part. Pathological findings and all histopathological images were compiled and reviewed by KS. All authors reviewed the manuscript at last.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDatasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHoang JK, Langer JE, Middleton WD, Wu CC, et al. Managing Incidental Thyroid Nodules Detected on Imaging: White Paper of the ACR Incidental Thyroid Findings Committee. Journal of the American College of Radiology. 2015;12(2):143\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYucel S, Balci IG, Tomak L. Diagnostic Performance of Thyroid Nodule Risk Stratification Systems: Comparison of ACR-TIRADS, EU-TIRADS, K-TIRADS, and ATA Guidelines. Ultrasound Q. 2023;39(4):206\u0026ndash;211. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/RUQ.0000000000000653\u003c/span\u003e\u003cspan address=\"10.1097/RUQ.0000000000000653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37918114.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDurante C, Grani G, Lamartina L, Soldini F, et al. The Diagnosis and Management of Thyroid Nodules Affecting Clinical Practice. Journal of Clinical Endocrinology \u0026amp; Metabolism. 2018;103(7):2743\u0026ndash;2760.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin JH, Baek JH, Chung J, Ha EJ, et al. Ultrasound Diagnosis of Thyroid Nodules: A Review of the Current Literature. Korean Journal of Radiology. 2016;17(4):758\u0026ndash;766.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuss G, Bonnema SJ, Erdogan MF, Garg S, et al. European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EAGLE Study. European Thyroid Journal. 2019;8(4):183\u0026ndash;190.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoraes PHM, Sigrist R, Takahashi MS, Schelini M, Chammas MC. Ultrasound elastography in the evaluation of thyroid nodules: evolution of a promising diagnostic tool for predicting the risk of malignancy. Radiol Bras. 2019 Jul-Aug;52(4):247\u0026ndash;253. doi: 10.1590/0100-3984.2018.0084. PMID: 31435087; PMCID: PMC6696751.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thyroid Nodules, ACR-TIRADS, Malignancy, FNAC","lastPublishedDoi":"10.21203/rs.3.rs-6962488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6962488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThyroid nodules represent a significant clinical challenge, with potential malignant transformation necessitating precise diagnostic strategies. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) 2017 emerged as a promising tool for risk stratification, prompting this comprehensive investigation into its diagnostic utility and correlation with histopathological findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eA prospective cross-sectional study was conducted on 73 patients, involving comprehensive ultrasound examination and Fine Needle Aspiration Cytology (FNAC). Nodules were evaluated using ACR-TIRADS 2017 classification system, with detailed analysis of ultrasound features and histopathological correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study revealed a female predominance (86.3%), with most patients aged 20-40 years. TIRADS classification showed TR4 as the most significant risk category, with 77.8% malignancy correlation. Ultrasound features demonstrated strong predictive value: solid composition (77.8% sensitivity), irregular margins (100% specificity), and specific calcification patterns were key malignancy indicators. Colloid Nodular Goitre (34.2%) was the most prevalent benign condition, with Papillary Carcinoma (16.5%) being the most common malignancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eACR-TIRADS 2017 demonstrates robust potential as a risk stratification tool for thyroid nodules, providing clinically valuable insights into nodule characteristics and malignancy risk.\u003c/p\u003e","manuscriptTitle":"Role of American College of Radiology Thyroid Imaging Reporting and Data System 2017 in Predicting Thyroid Nodule Malignancy: Cytological and Histological Insights in the Local Population of Pondicherry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:37:29","doi":"10.21203/rs.3.rs-6962488/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee976ef1-f098-44e8-8b5e-76978a73d99e","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-10T08:38:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:37:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6962488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6962488","identity":"rs-6962488","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}