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.

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.

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.

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.

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 comparative analysis of core needle biopsy and repeat fine needle aspiration in patients with inconclusive initial cytology of thyroid nodules.

Purpose: To assess and compare the effectiveness of ultrasound-guided core needle biopsy (CNB) in comparison to repeat fine-needle aspiration(rFNA) for thyroid nodules that yield inconclusive results following the initial fine-needle aspiration (FNA). Methods: A cohort of 471 patients who received an inconclusive cytological diagnosis following the initial FNA were included in this study. These patients subsequently underwent either CNB (n=242) or rFNA (n=229). The inconclusive FNA results encompassed categories I, III, and IV of The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC), as well as the ultrasound images indicating malignancy despite FNA results falling under TBSRTC category II. This study assessed the sampling satisfaction rate, diagnostic efficacy, and complications associated with CNB compared to rFNA. Additionally, the impact of repeat puncture time and nodule size on diagnostic efficacy was analyzed. Results: Following repeat punctures, the satisfaction rate of the CNB sampling was found to be significantly higher than that of rFNA (83.9% vs 66.8%). The diagnostic rate in the CNB group was significantly greater compared to that of the rFNA group (70.7% vs 35.8%). In patients with nodule maximum diameters ranging from 5 mm to 20 mm, the diagnostic accuracy was significantly higher in the CNB group compared to that in the rFNA group. In patients with intervals less than 90 days, between 90 days and one year, the diagnostic rate in the CNB group was found to be higher compared to that in the rFNA group. In CNB, not immediately adjacent to the capsule was a risk factor for nodular puncture bleeding (37.0% vs 22.7%.). Conclusion: CNB demonstrated higher rates of satisfaction and diagnosis compared to the rFNA. The diagnostic effectiveness of CNB was not influenced by the time interval or the size of the thyroid nodule. Therefore, in cases where the initial FNA diagnosis of thyroid nodules is inconclusive, CNB should be considered as a viable option for re-puncture.

[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.

Thyroid fine-needle aspiration cytology: malignancy rate in the category of indeterminate significant atypia/indeterminate significant follicular lesion.

BACKGROUND: Fine needle aspiration cytology (FNAC) is a standard preoperative diagnostic modality for thyroid nodules. The Bethesda Thyroid Cytopathology Reporting System (TBSRTC) defines the FNAC atypia group as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS). OBJECTIVES: Determine the risk of malignancy after surgical resection in patients with AUS/FLUS. DESIGN: Retrospective. SETTING: Pathology department of a tertiary care center. PATIENTS AND METHODS: All thyroid FNACs between 2015 and 2023 that were diagnosed as AUS/FLUS in Turkey. Patient demographics, preoperative ultrasonographic features, and follow-up data were collected. MAIN OUTCOME MEASURES: Relationship between AUS/FLUS diagnosis and final histopathological diagnosis. SAMPLE SIZE: 562. RESULTS: In total, 562 thyroid nodules were diagnosed as AUS/FLUS, and 267 (47.5%) were surgically excised. A malignant histopathological diagnosis was given in 28 cases (10.4%). Malignancy risk sensitivity of AUS/FLUS diagnosis was 75.68% (95% CI=58.80-88.23%), specificity was 55.24% (95% CI=50.91-59.52%), positive predictive value was 10.49% (95% CI=8.71-12.58%), and negative predictive value was 97.04% (95% CI=94.86-98.31%). In the ultrasonographic data, having symptomatic nodules, nodule calcification, and irregular nodule borders were all statistically significant signs of cancer in a one-variable analysis (P<.01). The presence of a family history emerged as a statistically significant prognostic marker for malignancy (P=.012). Although not statistically significant, the malignancy rate for nodules with nuclear atypia was 11.9%, significantly higher than the rate of 8.3% for nodules with architectural atypia only (P=0.32). CONCLUSIONS: The diagnosis of AUS/FLUS has a high rate of predicting the risk of malignancy and should continue to be offered. In addition to cytopathological features, ultrasound data and family history should be taken into consideration when evaluating the case. LIMITATIONS: Retrospective design and no molecular studies.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images.

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

Improved cancer risk stratification of isoechoic thyroid nodules to reduce unnecessary biopsies using quantitative ultrasound.

Objective: Gray-scale ultrasound (US) is the standard-of-care for evaluating thyroid nodules (TNs). However, the performance is better for the identification of hypoechoic malignant TNs (such as classic papillary thyroid cancer) than isoechoic malignant TNs. Quantitative ultrasound (QUS) utilizes information from raw ultrasonic radiofrequency (RF) echo signal to assess properties of tissue microarchitecture. The purpose of this study is to determine if QUS can improve the cancer risk stratification of isoechoic TNs. Methods: Patients scheduled for TN fine needle biopsy (FNB) were recruited from the Thyroid Health Clinic at Boston Medical Center. B-mode US and RF data (to generate QUS parameters) were collected in 274 TNs (163 isoechoic, 111 hypoechoic). A linear combination of QUS parameters (CQP) was trained and tested for isoechoic [CQP(i)] and hypoechoic [CQP(h)] TNs separately and compared with the performance of conventional B-mode US risk stratification systems. Results: CQP(i) produced an ROC AUC value of 0.937+/- 0.043 compared to a value of 0.717 +/- 0.145 (p >0.05) for the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and 0.589 +/- 0.173 (p >0.05) for the American Thyroid Association (ATA) risk stratification system. In this study, CQP(i) avoids unnecessary FNBs in 73% of TNs compared to 55.8% and 11.8% when using ACR TI-RADS and ATA classification system. Conclusion: This data supports that a unique QUS-based classifier may be superior to conventional US stratification systems to evaluate isoechoic TNs for cancer and should be explored further in larger studies.

Ultrasonographic predictors of thyroid cancer in Bethesda III and IV thyroid nodules.

Background: Bethesda III and IV thyroid nodules continue to be difficult to manage. Although molecular testing may assist in decision-making, it is expensive, not widely available, and not without pitfalls. The objective of this study is to assess whether certain thyroid ultrasonographic features may predict the risk of thyroid cancer in patients with Bethesda III and IV thyroid nodules and be used as additional decision-making tools to complement cytopathological results in deciding on diagnostic thyroidectomy. Methods: We retrospectively evaluated the ultrasonographic features of Bethesda categories III and IV thyroid nodules in patients who underwent subsequent thyroidectomy. We used the final histopathological examination of the surgical specimens as the gold-standard test and analyzed individual preoperative ultrasonographic features as predictors of malignancy. Results: Of the 278 patients who were diagnosed with Bethesda III and IV thyroid nodules on fine needle aspiration cytology (FNAC), 111 (39.9%) had thyroid cancer, and 167 (59.9%) exhibited benign nodules. The malignancy rate was higher in patients with Bethesda IV nodules (28/50, 56%) than those with Bethesda III nodules (83/228, 36.4%; p=0.016). In univariate analysis, hypoechogenicity (55.6% in malignant vs. 35.3% in benign, p=0.006) and calcifications (54.5 in malignant vs. 35.4% in benign, p=0.008) were significantly different between the benign and malignant pathology groups, whereas the size of the dominant nodule, number of nodules, irregular borders, taller-than-wide shape, and the presence of lymph nodes were comparable between the two groups. These two ultrasonographic features (hypoechogenicity and calcifications) remained significantly associated with the risk of malignancy in multivariate logistic regression analysis (for hypoechogenicity, p=0.014, odds ratio: 2.1, 95% CI:1.0-3.7 and for calcifications, p=0.019, odds ratio: 1.98, 95% CI:1.12-3.50). The sensitivity, specificity, positive and negative predictive values, and accuracy were 31.5%, 83%, 55.6%,64.7%, and 62.6%, for hypoechogenicity, respectively and 32.4%, 82%, 54.5%, 67.8%, and 62%, for calcification, respectively. Conclusions: Hypoechogenicity and calcifications in Bethesda III and IV thyroid nodules are strong predictors of thyroid cancer and associated with a two-fold increased risk of malignancy.

The American Thyroid Association risk classification of papillary thyroid cancer according to presurgery cytology.

OBJECTIVE: To compare the American Thyroid Association (ATA) risk staging of histologically proven papillary thyroid cancer (PTC) in patients who received a presurgery cytologic result of either indeterminate thyroid nodules (ITNs, Bethesda III/IV) or suspicious for malignancy/malignant (TIR 4/5, Bethesda V/VI). METHODS: Clinical, ultrasonographic, cytological data from patients with histologically diagnosed PTC were retrospectively collected. RESULTS: Patients were stratified according to the preoperative fine-needle aspiration cytology into 2 groups: 51 ITNs (TIR3A/3B) and 118 suspicious/malignant (TIR 4/5). Male/female ratio, age, and presurgery TSH level were similar between the 2 groups. At ultrasound, TIR 4/5 nodules were significantly more frequently hypoechoic (P = .037), with irregular margins (P = .041), and with microcalcifications (P = .020) and were more frequently classified as high-risk according to the European Thyroid Imaging and Reporting Data System (EU-TIRADS; P = .021). At histology, the follicular PTC subtype was significantly more prevalent among ITNs while classical PTC subtype was more frequent in TIR 4/5 group (P = .002). In TIR 4/5 group, a higher rate of focal vascular invasion (P < .001) and neck lymph node metastasis (P = .028) was observed. Intermediate-risk category according to ATA was significantly more frequent in TIR 4/5 group while low-risk category was more frequently found among ITNs (P = .021), with a higher number of patients receiving radioiodine in TIR 4/5 group (P = .002). At multivariate logistic regression, having a TIR 4/5 cytology was associated with a significant risk of having a higher ATA risk classification as compared to ITN (OR 4.6 [95% CI 1.523-14.007], P = .007), independently from presurgery findings (nodule size at ultrasound, sex, age, and EU-TIRADS score). CONCLUSIONS: Papillary thyroid cancers recorded among ITNs are likely less aggressive and are generally assessed as at lower risk according to ATA classification.

Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules.

BACKGROUND: The low specificity of Thyroid Imaging Reporting and Data System (TI-RADS) for preoperative benign-malignant diagnosis leads to a large number of unnecessary biopsies. This study developed and validated a predictive model based on MRI morphological features to improve the specificity. METHODS: A retrospective analysis was conducted on 825 thyroid nodules pathologically confirmed postoperatively. Univariate and multivariate logistic regression were used to obtain ß coefficients, construct predictive models and nomogram incorporating MRI morphological features in the training cohort, and validated in the validation cohort. The discrimination, calibration, and decision curve analysis of the nomogram were performed. The diagnosis efficacy, area under the curve (AUC) and net reclassification index (NRI) were calculated and compared with TI-RADS. RESULTS: 572 thyroid nodules were included (training cohort: n = 397, validation cohort: n = 175). Age, low signal intensity on T2WI, restricted diffusion, reversed halo sign in delay phase, cystic degeneration and wash-out pattern were independent predictors of malignancy. The nomogram demonstrated good discrimination and calibration both in the training cohort (AUC = 0.972) and the validation cohort (AUC = 0.968). The accuracy, sensitivity, specificity, PPV, NPV and AUC of MRI-based prediction were 94.4%, 96.0%, 93.4%, 89.9%, 96.5% and 0.947, respectively. The MRI-based prediction model exhibited enhanced accuracy (NRI>0) in comparison to TI-RADSs. CONCLUSIONS: The prediction model for diagnosis of benign and malignant thyroid nodules demonstrated a more notable diagnostic efficacy than TI-RADS. Compared with the TI-RADSs, predictive model had better specificity along with a high sensitivity and can reduce overdiagnosis and unnecessary biopsies.