A fully autonomous robotic ultrasound system for thyroid scanning.

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on human participants demonstrated that this system can perform high-quality ultrasound scans, close to manual scans obtained by clinicians. Additionally, it has the potential to detect thyroid nodules and provide data on nodule characteristics for American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) calculation.

Changes in thyroid cytology reporting in the 3rd edition of the Bethesda system.

Reporting fine-needle aspiration of thyroid nodules in the Bethesda classification is a practice widely used internationally and by us. The revised third edition of the Bethesda System of Reporting Thyroid Cytopathology brings changes in terminology, content, and new chapters. In terms of terminology, an obvious change is the removal of the two-word names of three categories while maintaining the six diagnostic categories of the previous versions - new: BI - non-diag- nostic, BIII - atypia of undetermined significance, BIV - follicular neoplasia. In the detailed description of the findings within the individual categories, the ter- minological changes adopted by the fifth edition of the WHO classification of thyroid neoplasia are respected - in particular, the recommended name follicular thyroid nodular disease for the most frequently represented category BII - benign. In the evaluation itself, the diagnostic specifications accepted by the current WHO classification of histopathological findings are reflected in the individual categories - if they are applicable at the cytological level. Targeted attention will need to be paid to high grade features. The revised version brings new chapters dedicated to molecular testing and evaluation of the paediatric population.

Differential diagnosis of thyroid nodules using heterogeneity quantification software on ultrasound images: correlation with the Bethesda system and surgical pathology.

Ultrasonography (US)-guided fine-needle aspiration cytology (FNAC) is the primary modality for evaluating thyroid nodules. However, in cases of atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), supplemental tests are necessary for a definitive diagnosis. Accordingly, we aimed to develop a non-invasive quantification software using the heterogeneity scores of thyroid nodules. This cross-sectional study retrospectively enrolled 188 patients who were categorized into four groups according to their diagnostic classification in the Bethesda system and surgical pathology [II-benign (B) (n = 24); III-B (n = 52); III-malignant (M) (n = 54); V/VI-M (n = 58)]. Heterogeneity scores were derived using an image pixel-based heterogeneity index, utilized as a coefficient of variation (CV) value, and analyzed across all US images. Differences in heterogeneity scores were compared using one-way analysis of variance with Tukey's test. Diagnostic accuracy was determined by calculating the area under the receiver operating characteristic (AUROC) curve. The results of this study indicated significant differences in mean heterogeneity scores between benign and malignant thyroid nodules, except in the comparison between III-M and V/VI-M nodules. Among malignant nodules, the Bethesda classification was not observed to be associated with mean heterogeneity scores. Moreover, there was a positive correlation between heterogeneity scores and the combined diagnostic category, which was based on the Bethesda system and surgical cytology grades (R = 0.639, p < 0.001). AUROC for heterogeneity scores showed the highest diagnostic performance (0.818; cut-off: 30.22% CV value) for differentiating the benign group (normal/II-B/III-B) from the malignant group (III-M/V&VI-M), with a diagnostic accuracy of 72.5% (161/122). Quantitative heterogeneity measurement of US images is a valuable non-invasive diagnostic tool for predicting the likelihood of malignancy in thyroid nodules, including AUS or FLUS.

Thyroid US-guided FNA techniques: A prospective,randomized controlled study.

BACKGROUND: Thyroid nodules are common in the general population. Ultrasonography is the most efficient diagnostic approach to evaluate thyroid nodules. The US FNAC procedure can be performed using either the short axis (perpendicular), or a long axis (parallel) approach to visualize the needle as it is advanced toward the desired nodule. The main aim of this study was to compare the percentage of non-diagnostic results between the long and short axis approach. METHODS: A prospective study that included a randomized controlled trial and was divided into two arms-the short axis and the long axis-was conducted. A total of 245 thyroid nodules were collected through the fine needle aspiration cytology, performed with ultrasound, from march 2021 to march 2022. The patient's demographic information were collected and also nodules characteristics. RESULTS: Of 245 nodules sampled, 122 were sampled with the long axis method, while 123 with the short axis method. There is not significantly less non diagnostic approach with either method compared to the other (11.5 % vs 16.3 % respectively). DISCUSSION: Previous studies came to the conclusion that the long axis method yields fewer non diagnostic samples. This study evaluated the two FNA approaches which were proceeded by the same physician who is expert in both techniques. CONCLUSION: The US FNAC performed in the long axis approach will not produce more conclusive results and less non diagnostic results (Bethesda category 1) than the short axis approach one.

Accuracy and diagnostic performance of the Bethesda system for reporting thyroid cytopathology in a tertiary endocrine surgical referral center in Belgium.

BACKGROUND: The Bethesda System for Reporting Thyroid Cytopathology is a commonly used classification for fine needle aspiration (FNA) cytology of suspicious thyroid nodules. The risk of malignancy (ROM) for each category has recently been analyzed in three international databases. This paper compares the diagnostic performance of the Bethesda classification in a high-volume referral center in Belgium. METHODS: All consecutive thyroid procedures were registered in a prospective database from January 2010 till August 2022. Patient and surgical characteristics, preoperative Bethesda categories, and postoperative pathology results were analyzed. RESULTS: Out of 2219 consecutive thyroid procedures, 1226 patients underwent preoperative FNA. Papillary thyroid cancer was the most prevalent malignancy (N = 119, 70.4%), followed by follicular (N = 17, 10.1%) and medullary thyroid cancer (N = 15, 8.9%). Micropapillary thyroid cancer was incidentally found in 46 (3.8%) patients. Bethesda categories I, II, III, IV, V, and VI, respectively, represented 250 (20.4%; ROM 4.4%), 546 (44.5%; ROM 3.8%), 96 (7.8%; ROM 20.8%), 231 (18.8%; ROM 15.2%), 62 (5.1%; ROM 72.6%), and 41 (3.3%; ROM 90.2%) patients. Overall ROM was 13.8%. An negative predictive value (NPV) of 96.2% was found. Overall specificity was 64.2% with a positive predictive value (PPV) of 31.9%. Diagnostic accuracy was 67.8%. Compared to international databases (CESQIP, EUROCRINE, and UKRETS), ROM in this study appeared lower for Bethesda category IV (15.2 vs. 26.7% and p = 0.612). CONCLUSION: Despite being validated in numerous studies, ROM based on preoperative FNA cytology classified according to the Bethesda classification may vary among surgical centers and countries as this study reveals a higher NPV and lower PPV.

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.

Objectives: To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. Materials and methods: This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. Results: The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Conclusions: Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

The application value of deep learning-based nomograms in benign-malignant discrimination of TI-RADS category 4 thyroid nodules.

Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes.

Predictive values of ultrasound characters associated with malignant thyroid nodules in Yaoundé: a cross-sectional study.

Introduction: most ultrasound criteria are defined in developed countries and commonly used in practice to assess the malignancy risk of thyroid nodules. This practice does not take into consideration some aspects of our context as delay of consultation and insufficient iodine intake. The objective of this study was to determine the predictive values of ultrasound characters associated with malignant thyroid nodules in our environment. Methods: we conducted a cross-sectional, prospective, and analytical study in three hospitals in Yaoundé over a six-month period in 2022. Our sample consisted of thyroid nodules with ultrasound, cytopathological, and histopathological data. The ultrasound characters and histology status of category III thyroid nodules and higher in Bethesda score were analysed in univariate and multivariate statistics to determine their predictive values. Results: eighty-nine nodules were obtained according to our inclusion criteria. The sex ratio was 0.46 and the average age of the patients was 46 years (IQR=42-59). The cancer prevalence in our sample was 22.47%. On ultrasound assessment, the characters associated to malignant histology (p<0.05) were nodules count, echogenicity, echostructure, presence or absence of microcalcifications, margins, and type of vascularization. Positive predictive values ranged from 26.15 to 57.14%, while negative predictive values ranged from 12.5 to 33.3%. Conclusion: taken alone, the ultrasound characters of suspected thyroid nodules have poor predictive values. There was a high variability in sensitivity but that was generally good (60-95%) while specificity was low. The prediction of malignant thyroid nodules is correlated with the association of at least two ultrasound criteria supported by clinical arguments.

Incidental 68Ga-DOTATATE uptake in thyroid nodules: Is guideline-directed management still appropriate?

BACKGROUND: Fluorodeoxyglucose uptake on positron emission tomography imaging has been shown to be an independent risk factor for malignancy in thyroid nodules. More recently, a new positron emission tomography radiotracer-Gallium-68 DOTATATE-has gained popularity as a sensitive method to detect neuroendocrine tumors. With greater availability of this imaging, incidental Gallium-68 DOTATATE uptake in the thyroid gland has increased. It is unclear whether current guideline-directed management of thyroid nodules remains appropriate in those that are Gallium-68 DOTATATE avid. METHODS: We retrospectively reviewed Gallium-68 DOTATATE positron emission tomography scans performed at our institution from 2012 to 2022. Patients with incidental focal Gallium-68 DOTATATE uptake in the thyroid gland were included. Fine needle aspiration biopsies were characterized via the Bethesda System for Reporting Thyroid Cytopathology. Bethesda III/IV nodules underwent molecular testing (ThyroSeq v3), and malignancy risk ≥50% was considered positive. RESULTS: In total, 1,176 Gallium-68 DOTATATE PET scans were reviewed across 837 unique patients. Fifty-three (6.3%) patients demonstrated focal Gallium-68 DOTATATE thyroid uptake. Nine patients were imaged for known medullary thyroid cancer. Forty-four patients had incidental radiotracer uptake in the thyroid and were included in our study. Patients included in the study were predominantly female sex (75%), with an average age of 62.9 ± 13.9 years and a maximum standardized uptake value in the thyroid of 7.3 ± 5.3. Frequent indications for imaging included neuroendocrine tumors of the small bowel (n = 17), lung (n = 8), and pancreas (n = 7). Thirty-three patients underwent subsequent thyroid ultrasound. Sonographic findings warranted biopsy in 24 patients, of which 3 were lost to follow-up. Cytopathology and molecular testing results are as follows: 12 Bethesda II (57.1%), 6 Bethesda III/ThyroSeq-negative (28.6%), 1 Bethesda III/ThyroSeq-positive (4.8%), 2 Bethesda V/VI (9.5%). Four nodules were resected, revealing 2 papillary thyroid cancers, 1 neoplasm with papillary-like nuclear features, and 1 follicular adenoma. There was no difference in maximum standardized uptake value between benign and malignant nodules (7.0 ± 4.6 vs 13.1 ± 5.7, P = .106). Overall, the malignancy rate among patients with sonography and appropriate follow-up was 6.7% (2/30). Among patients with cyto- or histopathology, the malignancy rate was 9.5% (2/21). There were no incidental cases of medullary thyroid cancer. CONCLUSION: The malignancy rate among thyroid nodules with incidental Gallium-68 DOTATATE uptake is comparable to rates reported among thyroid nodules in the general population. Guideline-directed management of thyroid nodules remains appropriate in those with incidental Gallium-68 DOTATATE uptake.

Radiofrequency ablation is an effective treatment for Bethesda III thyroid nodules without genetic alterations.

Background: Radiofrequency ablation (RFA) is effective in the treatment of thyroid nodules, leading to a 50-90% reduction with respect to baseline. Current guidelines indicate the need for a benign cytology prior to RFA, though, on the other side, this procedure is also successfully used for the treatment of papillary microcarcinomas. No specific indications are available for nodules with an indeterminate cytology (Bethesda III/IV). Aim: To evaluate the efficacy of RFA in Bethesda III nodules without genetic alterations as verified by means of a custom panel. Methods: We have treated 33 patients (mean delivered energy 1069 ± 1201 J/mL of basal volume) with Bethesda III cytology, EU-TIRADS 3-4, and negative genetic panel. The mean basal nodular volume was 17.3 ± 10.7 mL. Results: Considering the whole series, the mean volume reduction rate (VRR) was 36.8 ± 16.5% at 1 month, 59.9 ± 15.5% at 6 months, and 62 ± 15.7% at 1-year follow-up. The sub-analysis done in patients with 1 and 2 years follow-up data available (n = 20 and n = 5, respectively) confirmed a progressive nodular volume decrease. At all-time points, the rate of reduction was statistically significant (P < 0.0001), without significant correlation between the VRR and the basal volume. Neither cytological changes nor complications were observed after the procedure. Conclusion: RFA is effective in Bethesda III, oncogene-negative nodules, with reduction rates similar to those obtained in confirmed benign lesions. This procedure represents a good alternative to surgery or active surveillance in this particular class of nodules, regardless of their initial volume. A longer follow-up will allow to evaluate further reduction or possible regrowth.

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most frequent histological type of thyroid carcinoma. Although an increasing number of diagnostic methods have recently been developed, the diagnosis of a few nodules is still unsatisfactory. Therefore, the present study aimed to develop and validate a comprehensive prediction model to optimize the diagnosis of PTC. METHODS: A total of 152 thyroid nodules that were evaluated by postoperative pathological examination were included in the development and validation cohorts recruited from two centres between August 2019 and February 2022. Patient data, including general information, cytopathology, imprinted gene detection, and ultrasound features, were obtained to establish a prediction model for PTC. Multivariate logistic regression analysis with a bidirectional elimination approach was performed to identify the predictors and develop the model. RESULTS: A comprehensive prediction model with predictors, such as component, microcalcification, imprinted gene detection, and cytopathology, was developed. The area under the curve (AUC), sensitivity, specificity, and accuracy of the developed model were 0.98, 97.0%, 89.5%, and 94.4%, respectively. The prediction model also showed satisfactory performance in both internal and external validations. Moreover, the novel method (imprinted gene detection) was demonstrated to play a role in improving the diagnosis of PTC. CONCLUSION: The present study developed and validated a comprehensive prediction model for PTC, and a visualized nomogram based on the prediction model was provided for clinical application. The prediction model with imprinted gene detection effectively improves the diagnosis of PTCs that are undetermined by the current means.

The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms.

OBJECTIVE: The objective of this research was to create a deep learning network that utilizes multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) through preoperative US. METHODS: This retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small nodules, we introduced a multi-rescale fusion network (MRF-Net). Four different deep learning models, namely MobileNet V3, ResNet50, DenseNet121 and MRF-Net, were studied based on the feature information extracted from ultrasound images. The performance of each model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F1 value, receiver operating curve (ROC), area under the curve (AUC), decision curve analysis (DCA), and confusion matrix. RESULTS: Out of the total nodules examined, 193 were identified as FTA and 86 were confirmed as FTC. Among the deep learning models evaluated, MRF-Net exhibited the highest accuracy and area under the curve (AUC) with values of 85.3% and 84.8%, respectively. Additionally, MRF-Net demonstrated superior sensitivity and specificity compared to other models. Notably, MRF-Net achieved an impressive F1 value of 83.08%. The curve of DCA revealed that MRF-Net consistently outperformed the other models, yielding higher net benefits across various decision thresholds. CONCLUSION: The utilization of MRF-Net enables more precise discrimination between benign and malignant thyroid follicular tumors utilizing preoperative US.

Multimodal ultrasonography evaluation in thyroid nodule characterization: what is the ideal algorithm?

AIMS: The aim of this study is to investigate the diagnostic performances of Ultrasonography (US), Shear-wave Elastography (SWE), and Superb Microvascular Imaging (SMI) findings in the diagnosis of malignant thyroid nodules (MTNs) and to determine the US algorithm with the best diagnostic performance. MATERIAL AND METHODS: Eighty-one nodules in 77 patients who had underwent multimodal US with biopsy results, were evaluated. Echogenicity, nodule components, contours, presence and type of calcification, and size were analyzed with US. Nodule stiffness and vascular index (VI) measurements were performed via SWE and SMI. The power of the US algorithm in predicting malignancy was evaluated. RESULTS: Hypoechogenicity, irregular contour, aspect ratio (anteroposterior (AP)/transvers diameter) >1, and >43.9 kPa were the characteristicshad significant efficacy in the diagnosis of MTNs. Sensitivity, specificity, and AUC values were respectively 100%, 48.5%, and 0.742 for hypoechogenicity; 80%, 90.1%, and 0.855 for irregular contour; 60%, 71.2%, and 0.656 for aspect ratio >1; 60%, 72.7%, and 0.671 for >43.9 kPa; and 93.3%, 90.9%, and 0.921 for the US algorithm. VI did not show significant efficacy in diagnosis. CONCLUSION: Some B-mode and SWE findings showed sufficient efficacy in differentiating benign and malign nodules on their own. However, diagnostic accuracy increased significantly when the US algorithm was applied.

Comparative evaluation of shear wave elastography elasticity values in thyroid nodules with cytology results and TI-RADS scoring in differentiation of benign-malignant nodules.

PURPOSE: The aim of this prospective study was to investigate the diagnostic performance of shear wave elastography (SWE) in differentiating benign and malignant thyroid nodules and their correlation with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS). METHODS: This prospective study included 370 thyroid nodules in 308 patients aged 18-70 years. All the patients underwent B-mode ultrasound (US), Doppler examination, and SWE and were given an ACR TI-RADS risk score before fine needle aspiration biopsy (FNAB) and/or surgery. The correlation between SWE parameters and ACR TI-RADS categories was investigated statistically and compared with histopathologic results. Additionally, the diagnostic performance of SWE was evaluated to distinguish malignant and benign thyroid nodules. RESULTS: One hundred and thirty-five of the 370 thyroid nodules were malignant, and 235 nodules were benign. The mean shear wave velocity (SWV) value of the malignant nodules (3.70 ± 0.98 m/s) was statistically higher than that of the benign nodules (2.70 ± 0.37 m/s). The best cutoff value of the mean SWV for differentiating benign and malignant nodules was found to be 2.94 m/s (sensitivity 90.4%, specificity 89.9%, positive predictive value 81.3%, negative predictive value 94.1%, p < 0.001). The average score of the nodules according to the ACR TI-RADS was 3.57 ± 1.83 in benign nodules and 7.38 ± 2.69 in malignant nodules (p ≤ 0.001). CONCLUSION: This study showed that combining SWE and TI-RADS improves the specificity of TI-RADS alone in differentiating benign and malignant nodules.

Molecular Testing for Bethesda III Thyroid Nodules: Trends in Implementation, Cytopathology Call Rates, Surgery Rates, and Malignancy Yield at a Single Institution.

Background: Molecular testing (MT) has become standard practice to more accurately rule out malignancy in indeterminate Bethesda III (BIII) thyroid lesions. We sought to assess the adoption of this technology and its impact on cytology reporting, malignancy yield, and rates of surgery across community and academic sites affiliated with a tertiary medical center. Methods: We performed a retrospective cross-sectional study including all fine-needle aspirations (FNAs) analyzed at our institution from 2017 to 2021. We analyzed trends in MT utilization by platform and by community or academic site. We compared BIII call rates, MT utilization rates, rates of subsequent surgery, and malignancy yield on final pathology before and after MT became readily available using chi-square analysis and linear regression. Results: A total of 8960 FNAs were analyzed at our institution from 2017 to 2021. There was broad adoption of MT across both community and academic sites. There was a significant increase in both the BIII rate and the utilization of MT between the pre- and post-MT periods (p < 0.001 and p < 0.001). There was no significant change in the the malignancy yield on final pathology (57.1% vs. 50.0%, p = 0.347), while the positive predictive value of MT decreased from 85% to 50% (p = 0.008 [confidence interval 9.5-52.5% decrease]). Conclusions: The use of MT increased across the institution over the study period, with the largest increase seen after a dedicated pass for MT was routinely collected. This increased availability of MT may have led to an unintended increase in the rates of BIII lesions, MT utilization, and surgery for benign nodules. Physicians who use MT should be aware of potential consequences of its adoption to appropriately counsel patients.

[A thyroid pathology: The great simulator]. / Une pathologie thyroïdienne : la grande simulatrice.

INTRODUCTION: Tuberculosis remains a major public health problem in developing countries. Thyroid localization is very rare, and often the cause of misdiagnosis. Pathological anatomy plays an important role in the diagnosis of certainty. The authors report a case of primary thyroid tuberculosis in a 22-year-old patient. We highlight the epidemiological particularities of this case, and discuss diagnostic methods and the contribution of pathological anatomy. OBSERVATION: A 22 year-old male patient, with no reported pathological history, was seen in the clinic for the management of an isolated anterior cervical swelling that had been evolving for two months. Clinical examination revealed only a small thyroid nodule, with no inflammatory or vascular features. Biological tests were unremarkable. Ultrasound revealed a 2.4cm hypoechoic, homogeneous, poorly vascularized tissue mass in the left lobe, classified as EU-TIRADS 3. Fine needle aspiration with cytopathological study revealed a necrotizing granulomatous lesion suggestive of tuberculosis. A lobo-isthmectomy was performed, and histopathology revealed thyroid parenchyma destroyed by tubercular granulomas. The postoperative course was straightforward, with an exeat on postoperative day 6. Anti-tuberculosis treatment was instituted for 6 months. Three- and six-month follow-up examinations were unremarkable. The evolution was favorable, with recovery after treatment. CONCLUSION: Primary thyroid tuberculosis is rare. Cytology is important for orientation, and often helps to avoid misdiagnosis. The diagnosis should be considered in the presence of any thyroid mass in a patient from a tuberculosis-endemic region.