Risk stratification of thyroid nodules on ultrasonography with the French TI-RADS: description and reflections

A systematic review of machine learning based thyroid tumor characterisation using ultrasonographic images

Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study

OBJECTIVES

This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk.

METHODS

We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index.

RESULTS

The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively.

CONCLUSIONS

This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA).

CLINICAL RELEVANCE STATEMENT

High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent.

KEY POINTS

• Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

Markedly hypoechoic: a new definition improves the diagnostic performance of thyroid ultrasound

OBJECTIVES

To determine the contribution of a modified definition of markedly hypoechoic in the differential diagnosis of thyroid nodules.

METHODS

A total of 1031 thyroid nodules were included in this retrospective multicenter study. All of the nodules were examined with US before surgery. The US features of the nodules were evaluated, in particular, the classical markedly hypoechoic and modified markedly hypoechoic (decreased or similar echogenicity relative to the adjacent strap muscles). The sensitivity, specificity, and AUC of classical/modified markedly hypoechoic and the corresponding ACR-TIRADS, EU-TIRADS, and C-TIRADS categories were calculated and compared. The inter- and intraobserver variability in the evaluation of the main US features of the nodules was assessed.

RESULTS

There were 264 malignant nodules and 767 benign nodules. Compared with classical markedly hypoechoic as a diagnostic criterion for malignancy, using modified markedly hypoechoic as the criterion resulted in a significant increase in sensitivity (28.03% vs. 63.26%) and AUC (0.598 vs. 0.741), despite a significant decrease in specificity (91.53% vs. 84.88%) (p < 0.001 for all). Compared to the AUC of the C-TIRADS with the classical markedly hypoechoic, the AUC of the C-TIRADS with the modified markedly hypoechoic increased from 0.878 to 0.888 (p = 0.01); however, the AUCs of the ACR-TIRADS and EU-TIRADS did not change significantly (p > 0.05 for both). There was substantial interobserver agreement (κ = 0.624) and perfect intraobserver agreement (κ = 0.828) for the modified markedly hypoechoic.

CONCLUSION

The modified definition of markedly hypoechoic resulted in a significantly improved diagnostic efficacy in determining malignant thyroid nodules and may improve the diagnostic performance of the C-TIRADS.

CLINICAL RELEVANCE STATEMENT

Our study found that, compared with the original definition, modified markedly hypoechoic significantly improved the diagnostic performance in differentiating malignant from benign thyroid nodules and the predictive efficacy of the risk stratification systems.

KEY POINTS

• Compared with the classical markedly hypoechoic as a diagnostic criterion for malignancy, the modified markedly hypoechoic resulted in a significant increase in sensitivity and AUC. • The C-TIRADS with the modified markedly hypoechoic achieved higher AUC and specificity than that with the classical markedly hypoechoic (p = 0.01 and  < 0.001, respectively).

Diagnostic performance of a new two-dimensional shear wave elastography expression using siemens ultrasound system combined with ACR TI-RADS for classification of benign and malignant thyroid nodules: A prospective multi-center study

Objective

The present study aimed to evaluate the efficacy of a new two-dimensional shear wave elastography (2D-SWE) method using a Siemens ultrasound system and its combination with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for the differential diagnosis of benign and malignant thyroid nodules.

Methods

Conventional ultrasound images and 2D-SWE (E-whole-mean and E-stiffest-mean) were prospectively analyzed in 593 thyroid nodules from 543 patients. Nodules were divided into diameter (D) ≤10 mm and D > 10 mm groups and graded using ACR TI-RADS. The receiver operating characteristic curve was plotted using pathological findings as the gold standard. Diagnostic performance was compared among 2D-SWE, ACR TI-RADS, and their combination.

Results

The area under the curve (AUC) for E-whole-mean was higher than that for E-stiffest-mean (0.858 vs. 0.790, P < 0.001), which indicated that it was the better 2D-SWE parameter for differentiating malignant nodules from benign nodules with an optimal cut-off point of 11.36 kPa. In the all-sizes group, the AUC for E-whole-mean was higher than that for ACR TI-RADS (0.858 vs. 0.808, P < 0.001). The combination of E-whole-mean and ACR TI-RADS resulted in a higher AUC (0.929 vs. 0.858 vs. 0.808, P < 0.001), sensitivity (87.0% vs. 80.3% vs. 85.2%), specificity (85.1% vs. 74.0% vs. 73.6%), accuracy (86.3% vs. 78.1% vs. 81.1%), positive predictive value (91.5% vs. 85.1% vs. 85.6%), and negative predictive value (78.0% vs. 67.0% vs. 72.9%) compared to E-whole-mean or ACR TI-RADS alone. The AUC for the combination of 2D-SWE and ACR TI-RADS was superior to that for E-whole-mean or ACR TI-RADS alone in both D ≤ 10 mm and D > 10 mm groups (P < 0.001).

Conclusion

As the better 2D-SWE parameter, E-whole-mean had a higher diagnostic power than ACR TI-RADS and enhanced the diagnostic performance of ACR TI-RADS when identifying benign and malignant thyroid nodules. The combination of E-whole-mean and ACR TI-RADS improved the diagnostic performance compared to using ACR TI-RADS alone, providing a new and reliable method for the clinical diagnosis of thyroid nodules.

Diagnostic performance of ultrasound risk stratification systems on thyroid nodules cytologically classified as indeterminate: a systematic review and meta-analysis

PURPOSE

Ultrasound (US) risk stratification systems (RSSs) are increasingly being utilized for the optimal management of thyroid nodules, including those with indeterminate cytology. The goal of this study was to evaluate the category-based diagnostic performance of US RSSs in identifying malignancy in indeterminate nodules.

METHODS

This systematic review and meta-analysis was registered on PROSPERO (CRD42021266195). PubMed, EMBASE, and Web of Science were searched through December 1, 2022. Original articles reporting data on the performance of US RSSs for indeterminate nodules were included. The numbers of nodules classified as true negative, true positive, false negative, and false positive were extracted.

RESULTS

Thirty-three studies evaluating 7,225 indeterminate thyroid nodules were included. The diagnostic accuracy was quantitatively synthesized using a Bayesian bivariate model based on the integrated nested Laplace approximation in R. For the intermediate- to high-risk category, the sensitivity levels of the American College of Radiology, the American Thyroid Association, the European Thyroid Association, the Korean Thyroid Association/Korean Society of Thyroid Radiology, and Kwak et al. were found to be 0.80, 0.72, 0.76, 0.96, and 0.97, respectively. The corresponding specificity measurements were 0.36, 0.50, 0.49, 0.28, and 0.17. Furthermore, for the high-risk category, the sensitivity values were 0.40, 0.46, 0.55, 0.47, and 0.10, while the specificity levels were 0.91, 0.90, 0.71, 0.91, and 0.99, respectively.

CONCLUSION

The overall diagnostic performance of the US RSSs was moderate in the differentiation of indeterminate nodules.

Delayed Cancer Diagnosis in Thyroid Nodules Initially Treated as Benign With Radiofrequency Ablation: Ultrasound Characteristics and Predictors for Cancer

OBJECTIVE

Regrowth after radiofrequency ablation (RFA) of symptomatic large thyroid nodules, initially treated as benign, sometimes turns out to be malignancies. This study aimed to assess the ultrasound (US) characteristics of thyroid nodules initially treated as benign with RFA and later diagnosed as cancers, predictive factors for cancers masquerading as benign, and methods to avoid RFA in these cancers.

MATERIALS AND METHODS

We reviewed the medical records of 134 consecutive patients with 148 nodules who underwent RFA between February 2008 and November 2016 for the debulking of symptomatic thyroid nodules diagnosed as benign using US-guided biopsy. We investigated the pre-RFA characteristics of the thyroid nodules, changes at follow-up after RFA, and the final surgical pathology.

RESULTS

Nodule regrowth after RFA was observed in 36 (24.3%) of the 148 benign nodules. Twenty-two of the 36 nodules were surgically removed, and malignancies were confirmed in seven (19.4% of 36). Of the 22 nodules removed surgically, pre-RFA median volume (range) was significantly larger for malignant nodules than for benign nodules: 22.4 (13.9-84.5) vs. 13.4 (7.3-16.8) mL (P = 0.04). There was no significant difference in the regrowth interval between benign and malignant nodules (P = 0.49). The median volume reduction rate (range) at 12 months was significantly lower for malignant nodules than for benign nodules (51.4% [0-57.8] vs. 83.8% [47.9-89.6]) (P = 0.01). The pre-RFA benignity of all seven malignant nodules was confirmed using two US-guided fine-needle aspirations (FNAs), except for one nodule, which was confirmed using US-guided core-needle biopsy (CNB). Regrown malignant nodules were diagnosed as suspicious follicular neoplasms by CNB. Histological examination of the malignant nodules revealed follicular thyroid carcinomas, except for one follicular variant, a papillary thyroid carcinoma.

CONCLUSION

Symptomatic large benign thyroid nodules showing regrowth or suboptimal reduction after RFA may have malignant potential. The confirmation of these nodules is better with CNB than with FNA.

An adaptive multi-modal hybrid model for classifying thyroid nodules by combining ultrasound and infrared thermal images

BACKGROUND

Two types of non-invasive, radiation-free, and inexpensive imaging technologies that are widely employed in medical applications are ultrasound (US) and infrared thermography (IRT). The ultrasound image obtained by ultrasound imaging primarily expresses the size, shape, contour boundary, echo, and other morphological information of the lesion, while the infrared thermal image obtained by infrared thermography imaging primarily describes its thermodynamic function information. Although distinguishing between benign and malignant thyroid nodules requires both morphological and functional information, present deep learning models are only based on US images, making it possible that some malignant nodules with insignificant morphological changes but significant functional changes will go undetected.

RESULTS

Given the US and IRT images present thyroid nodules through distinct modalities, we proposed an Adaptive multi-modal Hybrid (AmmH) classification model that can leverage the amalgamation of these two image types to achieve superior classification performance. The AmmH approach involves the construction of a hybrid single-modal encoder module for each modal data, which facilitates the extraction of both local and global features by integrating a CNN module and a Transformer module. The extracted features from the two modalities are then weighted adaptively using an adaptive modality-weight generation network and fused using an adaptive cross-modal encoder module. The fused features are subsequently utilized for the classification of thyroid nodules through the use of MLP. On the collected dataset, our AmmH model respectively achieved 97.17% and 97.38% of F1 and F2 scores, which significantly outperformed the single-modal models. The results of four ablation experiments further show the superiority of our proposed method.

CONCLUSIONS

The proposed multi-modal model extracts features from various modal images, thereby enhancing the comprehensiveness of thyroid nodules descriptions. The adaptive modality-weight generation network enables adaptive attention to different modalities, facilitating the fusion of features using adaptive weights through the adaptive cross-modal encoder. Consequently, the model has demonstrated promising classification performance, indicating its potential as a non-invasive, radiation-free, and cost-effective screening tool for distinguishing between benign and malignant thyroid nodules. The source code is available at https://github.com/wuliZN2020/AmmH .

Differentiating thyroid nodules parathyroid lesions using 2D-shear-wave elastography: a novel approach for enhanced diagnostic accuracy

Differentiating between thyroid and parathyroid lesions by means of ultrasound can be a challenge in some cases. This study explores the diagnostic efficacy of bidimensional shear wave elastography planewave ultrasound (2D SWE PLUS) as an auxiliary technique in distinguishing these superficial structures. We evaluated 86 cases, presenting with concurrent thyroid nodules and hyperparathyroidism, through conventional ultrasound and 2D SWE PLUS, employing an Aixplorer Supersonic Mach30 with a 5-18 MHz linear probe. Statistically significant differences were observed for the elasticity index (EI) between parathyroid and normal thyroid tissue (p<0.0001, U=291), and between parathyroid lesions and thyroid nodules (p<0.0001, U=248.5). An area under the curve (AUC) of 0.961, with an optimal cut-off value of ≤8.9 kPa, was established to effectively distinguish parathyroid tissue from normal thyroid tissue (sensitivity of 91.9%; specificity of 97.5%). Furthermore, an AUC of 0.963 and an optimal cut-off of 9.24 kPa (sensitivity of 94.2%, specificity of 91.1%) were determined for parathyroid vs thyroid lesions. Elasticity values were significantly elevated in the cancer group compared to benign thyroid nodules (p<0.0001). Our findings suggest that 2D SWE PLUS is an effective tool in differentiating between thyroid nodules and parathyroid lesions, enhancing diagnostic performance in neck ultrasonography.

Integration of Artificial Intelligence Decision Aids to Reduce Workload and Enhance Efficiency in Thyroid Nodule Management

Importance

To optimize the integration of artificial intelligence (AI) decision aids and reduce workload in thyroid nodule management, it is critical to incorporate personalized AI into the decision-making processes of radiologists with varying levels of expertise.

Objective

To develop an optimized integration of AI decision aids for reducing radiologists' workload while maintaining diagnostic performance compared with traditional AI-assisted strategy.

Design, Setting, and Participants

In this diagnostic study, a retrospective set of 1754 ultrasonographic images of 1048 patients with 1754 thyroid nodules from July 1, 2018, to July 31, 2019, was used to build an optimized strategy based on how 16 junior and senior radiologists incorporated AI-assisted diagnosis results with different image features. In the prospective set of this diagnostic study, 300 ultrasonographic images of 268 patients with 300 thyroid nodules from May 1 to December 31, 2021, were used to compare the optimized strategy with the traditional all-AI strategy in terms of diagnostic performance and workload reduction. Data analyses were completed in September 2022.

Main Outcomes and Measures

The retrospective set of images was used to develop an optimized integration of AI decision aids for junior and senior radiologists based on the selection of AI-assisted significant or nonsignificant features. In the prospective set of images, the diagnostic performance, time-based cost, and assisted diagnosis were compared between the optimized strategy and the traditional all-AI strategy.

Results

The retrospective set included 1754 ultrasonographic images from 1048 patients (mean [SD] age, 42.1 [13.2] years; 749 women [71.5%]) with 1754 thyroid nodules (mean [SD] size, 16.4 [10.6] mm); 748 nodules (42.6%) were benign, and 1006 (57.4%) were malignant. The prospective set included 300 ultrasonographic images from 268 patients (mean [SD] age, 41.7 [14.1] years; 194 women [72.4%]) with 300 thyroid nodules (mean [SD] size, 17.2 [6.8] mm); 125 nodules (41.7%) were benign, and 175 (58.3%) were malignant. For junior radiologists, the ultrasonographic features that were not improved by AI assistance included cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, and nodules smaller than 5 mm, whereas for senior radiologists the features that were not improved by AI assistance were cystic or almost completely cystic nodules, anechoic nodules, spongiform nodules, very hypoechoic nodules, nodules taller than wide, lobulated or irregular nodules, and extrathyroidal extension. Compared with the traditional all-AI strategy, the optimized strategy was associated with increased mean task completion times for junior radiologists (reader 11, from 15.2 seconds [95% CI, 13.2-17.2 seconds] to 19.4 seconds [95% CI, 15.6-23.3 seconds]; reader 12, from 12.7 seconds [95% CI, 11.4-13.9 seconds] to 15.6 seconds [95% CI, 13.6-17.7 seconds]), but shorter times for senior radiologists (reader 14, from 19.4 seconds [95% CI, 18.1-20.7 seconds] to 16.8 seconds [95% CI, 15.3-18.3 seconds]; reader 16, from 12.5 seconds [95% CI, 12.1-12.9 seconds] to 10.0 seconds [95% CI, 9.5-10.5 seconds]). There was no significant difference in sensitivity (range, 91%-100%) or specificity (range, 94%-98%) between the 2 strategies for readers 11 to 16.

Conclusions and Relevance

This diagnostic study suggests that an optimized AI strategy in thyroid nodule management may reduce diagnostic time-based costs without sacrificing diagnostic accuracy for senior radiologists, while the traditional all-AI strategy may still be more beneficial for junior radiologists.

The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update

The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

Objective

This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.

Methods

A retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).

Results

The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.

Conclusions

Combining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules−81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.5 cm and 5 cm prior to biopsy and/or surgery. Nodules were analyzed based on size stratifications: <1 cm (n = 43); 1−2 cm (n = 52) and >2 cm (n = 31) and equivocal cytology status (n = 52), and diagnostic performance assessments were conducted. The combination of EU TIRADS with SWE using the SD parameter; maintained a high sensitivity and significantly improved the specificity of sole EU TIRADS for nodules 1−2 cm (SEN: 72.2% vs. 88.9%, p > 0.05; SPEC: 76.5% vs. 55.9%, p < 0.01) and >2 cm (SEN: 71.4% vs. 85.7%, p > 0.05; SPEC: 95.8% vs. 62.5%, p < 0.01). For cytologically-equivocal nodules; the combination with the SWE minimum parameter resulted in a significant reduction in sensitivity with increased specificity (SEN: 60% vs. 80%; SPEC: 83.4% vs. 37.8%; all p < 0.05). SWE in combination with EU TIRADS is diagnostically efficient in discriminating nodules > 1 cm but is not ideal for discriminating cytologically-equivocal nodules.

Ultrasound performance using the EU-TIRADS score in the diagnosis of thyroid cancer in Congolese hospitals

The thyroid imaging reporting and data systems by the European Thyroid Association (EU-TIRADS) has been widely used in malignancy risk stratification of thyroid nodules. However, there is a paucity of data in developing countries, especially in Africa, to validate the use of this scoring system. The aim of the study was to assess the diagnostic value of the EU-TIRADS score in Congolese hospitals, using pathological examination after surgery as the gold standard in Congolese hospitals. This retrospective and analytical study examined clinical, ultrasound and pathological data of 549 patients aged 45 ± 14 years, including 468 females (85.2%), operated for thyroid nodule between January 2005 and January 2019. In the present study, only the highest graded nodule according to the EU-TIRADS score in each patient was taken into account for the statistical analyses. So 549 nodules were considered. Nodules classified EU-TIRADS 2 and 3 on the one hand, and, on the other hand, 4 and 5, were considered respectively at low and high risk of malignancy. The sensitivity and specificity of the EU-TIRADS score were calculated. The significance level was set at 5%. Of all patients, 21.7% had malignant nodules. They made 48.4% of the nodules in patients younger than and at 20 years old, and 31.1% in those aged 60 or over. Malignant nodules were more frequent in men than in women (30.9% vs. 20.1%; p = 0.024). Papillary carcinoma (67.2%) and follicular carcinoma (21.8%) were the main types. The malignancy rate was 39.7% and 1.5% among nodules rated EU-TIRADS 4 and 5, and those with EU-TIRADS score 2 and 3, respectively (p < 0.001). The EU-TIRADS score had a sensitivity of 96.6% and a specificity of 59.3%. The ROC curve indicated an area under the curve of 0.862. In a low-income country, a well performed thyroid ultrasound, using the EU-TIRADS score, could be an important tool in the selection of thyroid nodules suspected of malignancy and requiring histopathological examination in the Congolese hospital setting.Trial registration: The research protocol had obtained the favorable opinion of the DRC national health ethics committee no. 197/CNES/BN/PMMF/2020. The data was collected and analyzed anonymously.

Diagnostic performance of simplified TI-RADS for malignant thyroid nodules: comparison with 2017 ACR-TI-RADS and 2020 C-TI-RADS

Background

The aim of this study is to propose a new TI-RADS and compare it with the American College of Radiology (2017 ACR)-TI-RADS and the 2020 Chinese (2020 C)-TI-RADS.

Methods

A retrospective analysis of 749 thyroid nodules was performed. Based on the calculated odds ratio of ultrasonic signs between benign and malignant nodules, a new thyroid nodule score and malignancy rate were calculated. A receiver operating characteristic curve was drawn to analyze the new system’s effectiveness in the differential diagnosis of benign and malignant thyroid nodules and was compared with the 2020 C-TI-RADS and 2017 ACR-TI-RADS. Five ultrasound physicians with different qualifications graded another 123 thyroid nodules according to the 2017ACR-TI-RADS, 2020 C-TI-RADS, and the newly proposed TI-RADS. Intergroup and intragroup consistency was evaluated using the Kappa test and intraclass correlation coefficient (ICC) test.

Results

1) The new thyroid nodule score was divided into 0, 1, 2, 3, 4, and 5 points, with malignancy rates of 1.52%, 7.69%, 38.24%, 76.00%, 90.75%, and 93.75%, respectively. Using 3 points as the cutoff value to diagnose benign and malignant thyroid nodules, the sensitivity and specificity were 94.03% and 67.39%, respectively, which were higher than those of the 2017 ACR-TI-RADS and 2020 C-TI-RADS. The simplified TI-RADS, namely, sTI-RADS, was established as follows: sTI-RADS 3 (0 points), malignancy rate < 2%; sTI-RADS 4a (1 point), malignancy rate 2–10%; sTI-RADS 4b (2 points), malignancy rate 10–50%; sTI-RADS 4 (3 points), malignancy rate 50–90%; and sTI-RADS 5 (4 and 5 points), malignancy rate > 90%. 2) Five ultrasound doctors graded thyroid nodules by the 2017 ACR-TI-RADS, 2020C-TI-RADS and sTI-RADS. Intragroup consistency was good among all tests; ICC were 0.86 (0.82–0.90), 0.84 (0.78–0.88), and 0.88 (0.84–0.91), respectively, while only sTI-RADS had good intergroup consistency.

Conclusion

In summary, we proposed a new TI-RADS, namely, sTI-RADS, which was obtained using a simple assignment method with higher specificity, accuracy, positive predictive value, and Youden index than the 2017 ACR-TI-RADS and 2020 C-TI-RADS.

Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers

For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis.

ACR-RADS Programs Current State and Future Opportunities: Defining a Governance Structure to Enable Sustained Success

In the spring of 2021, the ACR approved a proposal to improve the consistency, transparency, and administrative oversight of the ACR Reporting and Data Systems (RADS). A working group of experts and stakeholders was convened to draft this governance document. Major advances include (1) forming a RADS Steering Committee, (2) establishing minimum requirements and evidence standards for new and existing RADS, and (3) outlining a governance structure and communication strategy for RADS.

Shear Wave Elastography and Thyroid Imaging Reporting and Data System (TIRADS) for the Risk Stratification of Thyroid Nodules-Results of a Prospective Study

Purpose: To compare the diagnostic performance of thyroid imaging reporting and data system (TIRADS) in combination with shear wave elastography (SWE) for the assessment of thyroid nodules. Methods: A prospective study was conducted with the following inclusion criteria: preoperative B-mode ultrasound (US) including TIRADS classification (Kwak-TIRADS, EU-TIRADS), quantitative SWE and available histological results. Results: Out of 43 patients, 61 thyroid nodules were detected; 10 nodules were found to be thyroid cancer (7 PTC, 1 FTC, 2 HüCC) and 51 were benign. According to Kwak-TIRADS the majority of benign nodules (47 out of 51, 92.2%) were classified in the low-risk- and intermediate-risk class, four nodules were classified as high-risk (7.8%). When using EU-TIRADS, the benign nodules were distributed almost equally across all risk classes, 21 (41.2%) nodules were classified in the low-risk class, 16 (31.4%) in the intermediate-risk class and 14 (27.4%) in the high-risk class. In contrast, most of the malignant nodules (eight out of ten) were classified as high-risk on EU-TIRADS. One carcinoma was classified as low-risk and one as intermediate-risk nodule. For SWE, ROC analysis showed an optimal cutoff of 18.5 kPa to distinguish malignant and benign nodules (sensitivity 80.0%, specificity 49.0%, PPV 23.5% and NPV 92.6%). The addition of elastography resulted in an increase of accuracy from 65.6% to 82.0% when using Kwak-TIRADS and from 49.2% to 72.1% when using EU-TIRADS. Conclusion: Our data demonstrate that the combination of TIRADS and SWE seems to be superior for the risk stratification of thyroid nodules than each method by itself. However, verification of these results in a larger patient population is mandatory.

Salivary biomarkers-assisted ultrasound-based differentiation of malignant and benign thyroid nodules

BACKGROUND

The incidence of papillary thyroid cancer (PTC) is increasing annually. ultrasonography (US) is the current primary method for evaluating thyroid nodules; however, there have been persisting challenges in diagnosing borderline malignancies. This paper aimed to establish the differential diagnostic value of salivary biomarkers for thyroid nodules geared towards improving the efficacy of US.

METHODS

We recruited a total of 44 PTC patients and 42 benign thyroid tumor (BTT) patients to this study. The distribution of tumor markers and thyroid hormones in saliva and serum were compared between groups; then, uni-/multi-variate logistic analyses were used to determine the risk factors of PTC. Further, we estimated the differential diagnostic value of biomarkers in thyroid nodules, especially in borderline scenarios. Finally, a multi-index diagnostic model was constructed constituting biomarkers and US.

RESULTS

The distributions of serum thyroglobulin (TG), salivary triiodothyronine (T3), free-triiodothyronine (FT3), and free-thyroxine (FT4) were significantly different in BTT and PTC (P<0.05); salivary FT3 was identified as an independent risk factor for PTC. By analyzing the diagnostic accuracy of various Thyroid Imaging Reporting and Data System (TI-RADS) categories, category 4A was shown to have the lowest diagnostic accuracy (48.39%) with the largest proportion (31 people, 36.05%). In 4A patients, the K-nearest neighbor (KNN) algorithm attained the highest sensitivity of 87.50% and specificity of 100.00% among the machine learning-based multi-biomarkers models. Eventually, by combing the US with the KNN-based biomarkers model, the sensitivity and specificity reached 90.91% and 83.33%, respectively.

CONCLUSIONS

Salivary biomarkers exhibit good potential in the differential diagnosis of borderline thyroid nodules and they significantly improve the prediction accuracy of the US. Additionally, we found that salivary FT3 is an independent risk factor for PTC and may be used as a key marker for PTC diagnosis.

Concordance of the ACR TI-RADS

BACKGROUND

Ultrasonography (US) is the method of choice for evaluating thyroid nodules. In 2017, the American College of Radiology (ACR) created a classification system based on US characteristics. For the system to be adopted, it must be reproducible.

OBJECTIVES

To determine the intraobserver and interobserver variability of the ACR TI-RADS.

METHODS

Cross-sectional study; three radiologists with different levels of experience used the ACR TI-RADS to classify 100 nodules on two occasions one month apart, and we calculated the intraobserver and interobserver variability.

RESULTS

Regarding intraobserver variability, the first radiologist had nearly perfect concordance for composition, echogenicity, shape, and margins and substantial concordance for echogenic foci; the second radiologist had nearly perfect concordance for composition, echogenicity, shape, and margins and substantial concordance for echogenic foci, and the third radiologist had nearly perfect concordance for composition, echogenicity, and shape and substantial concordance for margins and echogenic foci. The interobserver concordance was calculated for the two readings; the concordance was substantial except for shape in the first reading and for echogenicity and margins in the second reading, which had moderate concordance.

CONCLUSIONS

The ACR TI-RADS classification system is reproducible.

Analysis of vascularization in thyroid gland nodes with superb microvascular imaging (SMI) and CD34 expression histology: a pilot study

Background

The Doppler sonography technique known as "superb microvascular imaging" (SMI) is advancing sonographic micro vascularization imaging in various disciplines. In this study, we aimed to determine whether SMI could reliably reproduce the blood flow in thyroid nodes and whether malignancy could be diagnosed, based on vascularization properties. Immunhistochemical staining by CD34 and SMI where used to determine the vascularization of nodes in terms of quantified vascularization parameters gained by computational evaluation.

Methods

We used image analysis programs to investigate whether the quantitative value for vascularization strength in the thyroid node, measured with SMI, was correlated with the actual degree of vascularization, determined microscopically. We included 16 patients that underwent thyroid resections. We prepared thyroid gland tissue slices for immunohistochemistry and labelled endothelial cells with CD34 to visualize blood vessels microscopically. We used image analysis programs, ImageJ, to quantify SMI Doppler sonographic measurements and CellProfiler to quantify CD34 expression in histological sections. We evaluated the numeric values for diagnostic value in node differentiation. Furthermore, we compared these values to check for correlations.

Results

Among the 16 nodes studied, three harboured malignant tumours (18.75%): two papillary and one follicular carcinoma. Among the 13 benign lesions (81.25%), four harboured follicular adenomas. Malignant and benign nodes were not significantly different in sonographic (0.88 ± 0.89 vs. 1.13 ± 0.19; p = 0.2790) or immunohistochemical measurements of vascularization strength (0.05 ± 0.05 vs. 0.08 ± 0.06; p = 0.2260).

Conclusion

We found a positive, significant correlation (r = 0.55588; p = 0.0254) between SMI (quantitative values for vascularization strength) and immunohistochemistry (CD34 staining) evaluations of thyroid nodes.

Comparison of Incidental Thyroid Nodules Between Early Breast Cancer Patients and Healthy Controls: Higher Incidence and Thyroid Imaging Reporting and Data System (TI-RADS) Score of Patients with Cancer

Background: Breast and thyroid cancers remain among the most common malignancies in women. In previous studies, the co-occurrence of thyroid and breast cancers has been reported. Objectives: The aim of this study was to evaluate and compare the risk and incidence of incidental thyroid nodules (ITNs) between patients with breast cancer and healthy controls, based on the Thyroid Imaging Reporting and Data System (TI-RADS). Methods: This case-control study was conducted on 140 patients with breast cancer and 140 cancer-free women in a similar age range. Thyroid ultrasonography (US) was performed before the onset of treatment. The risk stratification of thyroid nodules was based on the TI-RADS. Results: The mean age of the participants was not significantly different between the case (43.35 ± 7.85 years) and control (42.11 ± 3.69 years) groups (P = 0.094). Invasive ductal carcinoma was the most frequent type of breast cancer in the patients. Normal thyroid US findings were significantly less frequent in patients with breast cancer (35.7%) compared to the healthy controls (76.4%) (P = 0.001). On the other hand, thyroid nodules were more frequent in the patients and associated with a higher risk of malignancy (i.e., high TI-RADS scores) compared to healthy women (P = 0.001 and P = 0.001, respectively). Besides, patients with breast cancer showed more thyroid abnormalities in the US examinations. Conclusions: A higher frequency of ITN, with an elevated TI-RADS score, which raised the suspicion of malignancy, was seen in patients with breast cancer. Overall, patients with breast cancer may benefit from a regular thyroid US examination.

Validation of Four Thyroid Ultrasound Risk Stratification Systems in Patients with Hashimoto's Thyroiditis; Impact of Changes in the Threshold for Nodule's Shape Criterion

Simple Summary

Thyroid Imaging Reporting and Data Systems (TIRADS) optimize the selection of thyroid nodules for cytological examination. There is a question: is the effectiveness of these systems affected by morphological changes to thyroid parenchyma that are visible in the course of Hashimoto’s thyroiditis (HT)? This question is very important because of the increased risk of malignancy in thyroid nodules in patients with HT. We investigated widely accepted ultrasound malignancy risk features with a special consideration of the suspected nodule’s shape in patients with and without HT. We also validated EU-TIRADS, K-TIRADS, ACR-TIRADS, and ATA guidelines in both groups and evaluated the impact of changes in the threshold for nodule’s shape criterion on the diagnostic value of these TIRADS. The presence of Hashimoto’s thyroiditis did not exert any significant adverse implications for the efficiency of examined TIRADS. The impact of changes in the threshold for nodule’s shape criterion was the highest for EU-TIRADS.

Abstract

The aim of the study was to validate thyroid US malignancy features, especially the nodule’s shape, and selected Thyroid Imaging Reporting and Data Systems (EU-TIRADS; K-TIRADS; ACR-TIRADS, ATA guidelines) in patients with or without Hashimoto’s thyroiditis (HT and non-HT groups). The study included 1188 nodules (HT: 358, non-HT: 830) with known final diagnoses. We found that the strongest indications of nodule’s malignancy were microcalcifications (OR: 22.7) in HT group and irregular margins (OR:13.8) in non-HT group. Solid echostructure and macrocalcifications were ineffective in patients with HT. The highest accuracy of nodule’s shape criterion was noted on transverse section, with the cut-off value of anteroposterior to transverse dimension ratio (AP/T) close to 1.15 in both groups. When round nodules were regarded as suspicious in patients with HT (the cut-off value of AP/T set to ≥1), it led to a three-fold increase in sensitivity of this feature, with a disproportionally lower decrease in specificity and similar accuracy. Such a modification was effective also for cancers other than PTC. The diagnostic effectiveness of analyzed TIRADS in patients with HT and without HT was similar. Changes in the threshold for AP/T ratio influenced the number of nodules classified into the category of the highest risk, especially in the case of EU-TIRADS.

Hospital-Based Ultra-Sonographic Prevalence and Spectrum of Thyroid Incidentalomas in Pakistani Population

Introduction: Thyroid incidentalomas (TIs) are clinically asymptomatic nodules found accidentally during imaging studies ordered for some other reasons. Being easily accessible, non-invasive, and inexpensive, thyroid ultrasound (US) is a key investigation in the management of thyroid nodules.

Methods: This ultrasound-based cross-sectional study was performed in the radiology department of a major tertiary care hospital. Every second patient visiting the emergency department was a potential candidate for a thyroid ultrasound. Patients having ages greater than 20 years were included in the study.

Results: A total of 250 patients were included in the study. Out of these, 175 were female and 75 were male. The majority (54.80%) were in the age group 21-30 years. Nodules were found in 65 (26%) patients and in the majority of cases (67.7%) they were multiple in number. Associated lymphadenopathy was seen in only one patient. Thyroid nodules were more common in females as compared to males (75.38% versus 24.62%). According to Thyroid Imaging and Reporting Data System (TI-RADS) classification, the majority of the nodules were falling in TI-RADS 1 (74%) followed by TI-RADS 3 (9.60%) and 4A (8.80%).

Conclusion: The thyroid nodules are more commonly seen in females as compared to males. A significant association is seen between the frequency of thyroid nodules and increasing age. The majority of thyroid nodules fall in TI-RADS 1 category followed by TI-RADS 3 and 4A.

The gold standard of thyroid nodule examination? Prospective validation of the ACR TI-RADS in a secondary referral center

The aim of this prospective study was the validation of the risk stratification of thyroid nodules using ultrasonography with the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) and partly in comparison to American Thyroid Association (ATA) guidelines in a secondary referral center. Fine needle aspiration biopsy (FNA) (n=605) and histological examinations (n=63) were the reference standards for the statistical analysis. ACR TI-RADS cut-off value: TR4 with sensitivity 85.7 %, specificity 54.1 %, PPV 58.5 %, accuracy 67.7 % (AUC 0.738; p<0.001). ATA cut-off value: "high suspicion" with sensitivity 80 %, specificity 83.3 %, PPV 80 %, accuracy 81.8 % (AUC 0.800; p=0.0025). 18.4 % nodules (3 malignant) could not be assigned to a proper ATA US pattern group (p<0.0001). Both ACR TI-RADS and ATA have allowed fair selection of nodules requiring FNA with superiority of ACR TI-RADS according to classification of all thyroid nodules to the proper group. According to ACR TI-RADS almost one third of the patients were incorrectly classified with 17.9 % missed thyroid carcinomas, exclusively micropapillary carcinomas, even though, the amount of FNA would be reduced to 48 %.

Ultrasonography and Fine-Needle Aspiration in Indeterminate Thyroid Nodules: A Systematic Review of Diagnostic Test Accuracy

OBJECTIVES/HYPOTHESIS

Sonographic risk criteria may assist in further prognostication of indeterminate thyroid nodules (ITNs). Our aim was to determine whether sonographic criteria could further delineate the post-test probability of malignancy in ITNs.

STUDY DESIGN

Meta-analysis of diagnostic test accuracy.

METHODS

A systematic review of Web of Science, MEDLINE, EMBASE, and CINAHL was performed from inception to April 15, 2021. Eligible studies included those which reported ultrasonographic evaluations with the American Thyroid Association (ATA) or the Thyroid Imaging Reporting and Data System (TIRADS) in adult patients with ITNs. ATA or TIRADS were scored as low (negative) or high (positive) malignancy risk using a previously validated binary classification. Primary outcomes included pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratio for all sonographic criteria. Studies were appraised using Quality Assessment of Diagnostic Accuracy Studies and the data were pooled using bivariate random-effects models.

RESULTS

Seventeen studies were included in the analysis. For Bethesda III, ATA had a specificity (0.90, 95% confidence interval (CI): 0.74-0.94), but a sensitivity of 0.52 (95% CI: 0.25-0.77). Conversely, K-TIRADS had the highest sensitivity (0.78, 95% CI: 0.62-0.89) with a specificity of 0.53 (95% CI: 0.31-0.74). Furthermore, American College of Radiology and EU TIRADS had specificities of 0.60 (95% CI: 0.36-0.80) and 0.81 (95% CI: 0.73-0.87) with sensitivities of 0.70 (95% CI: 0.37-0.90) and 0.38 (95% CI: 0.20-0.60), respectively. There were few studies with Bethesda IV nodules.

CONCLUSIONS

Though dependent on malignancy rates, Bethesda III nodules with low-suspicion TIRADS features may benefit from clinical observation, whereas nodules with high-suspicion ATA features may require molecular testing and/or surgery.

LEVEL OF EVIDENCE

NA Laryngoscope, 2021.

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

In recent years, the incidence of thyroid nodules has shown an increasing trend year by year and has become one of the important diseases that endanger human health. Ultrasound medical images based on deep learning are widely used in clinical diagnosis due to their cheapness, no radiation, and low cost. The use of image processing technology to accurately segment the nodule area provides important auxiliary information for the doctor's diagnosis, which is of great value for guiding clinical treatment. The purpose of this article is to explore the application value of combined detection of abnormal sugar-chain glycoprotein (TAP) and carcinoembryonic antigen (CEA) in the risk estimation of thyroid cancer in patients with thyroid nodules of type IV and above based on deep learning medical images. In this paper, ultrasound thyroid images are used as the research content, and the active contour level set method is used as the segmentation basis, and a segmentation algorithm for thyroid nodules is proposed. This paper takes ultrasound thyroid images as the research content, uses the active contour level set method as the basis of segmentation, and proposes an image segmentation algorithm Fast-SegNet based on deep learning, which extends the network model that was mainly used for thyroid medical image segmentation to more scenarios of the segmentation task. From January 2019 to October 2020, 400 patients with thyroid nodules of type IV and above were selected for physical examination and screening at the Health Management Center of our hospital, and they were diagnosed as thyroid cancer by pathological examination of thyroid nodules under B-ultrasound positioning. The detection rates of thyroid cancer in patients with thyroid nodules of type IV and above are compared; serum TAP and CEA levels are detected; PT-PCR is used to detect TTF-1, PTEN, and NIS expression; the detection, missed diagnosis, misdiagnosis rate, and diagnostic efficiency of the three detection methods are compared. This article uses the thyroid nodule region segmented based on deep learning medical images and compares experiments with CV model, LBF model, and DRLSE model. The experimental results show that the segmentation overlap rate of this method is as high as 98.4%, indicating that the algorithm proposed in this paper can more accurately extract the thyroid nodule area.

The Diagnostic Value of the American College of Radiology Thyroid Imaging Reporting and Data System Classification and Shear-Wave Elastography for the Differentiation of Thyroid Nodules

This study aimed to determine the diagnostic accuracy of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) classification and shear-wave elastography (SWE) for the diagnosis of benign and malignant thyroid nodules. This retrospective study enrolled 141 patients (18-84 y of age) undergoing thyroidectomy between January 2015 and August 2020. All statistical analysis was based on pathologic results of patients. The cut-off value was found as category 4 for ACR TI-RADS classification and 5 m/s for shear-wave velocity (Vs) by the receiver operator characteristic curve analysis (area under the curve [AUC] = 0.684, p = 0.020 and AUC = 0.715, p = 0.005, respectively). SWE has higher diagnostic accuracy than the ACR TI-RADS classification system and can improve thyroid nodule discrimination in all sizes of the nodules. Also, the diagnostic performance decreases when the nodule diameter increases.

Not all that is miliary is tuberculosis: Metastatic medullary thyroid carcinoma mimicking miliary tuberculosis

Medullary carcinoma of the thyroid should be considered in the differential diagnosis of miliary pattern of micronodules on chest imaging, irrespective of clinical features.

Retrospective analysis of the ultrasound features of resected thyroid nodules

PURPOSE

Several ultrasound (US) risk stratification systems (US-RSSs) have been proposed to stratify the risk of malignancy (ROM) of thyroid nodules. This risk might be overestimated due to selection bias and comparison with the cytological report alone. Our study aimed to compare ROM and diagnostic performance of three guidelines (ATA, AACE/ACE/AME, EUTIRADS) and evaluate the changes in unnecessary biopsy according to the nodule size cutoff for biopsy, using histology as gold standard.

METHODS

This retrospective observational study included 146 consecutive patients who underwent surgery after US and cytological characterization. We analyzed the effectiveness and accuracy of three US-RSSs.

RESULTS

46.6% of nodules were diagnosed as malignant. Applying US-RSS, the percentage of nodules that should have been analyzed by biopsy was 84.25% with ATA, 69.86% with EUTIRADS and 64.38% with AACE/ACE/AME systems. The ROM was 94.9%, 86.0%, 87.0% for high-risk category, 36.4%, 32.0%, 35.4% for intermediate-risk category and 22.9%, 0.0%, 22.9% for low-risk category by ATA, AACE/ACE/AME and EUTIRADS systems, respectively. EUTIRADS and AACE/ACE/AME systems were more accurate in differentiating malignant from benign cases. ATA score was the more sensitive US-RSS to identify malignant tumors within the high-risk category. About the unnecessary biopsies, in the intermediate-risk category, the application of the size criterion helps to increase specificity in all systems.

CONCLUSIONS

The US categorization of low and high-risk thyroid nodules using current US-RSSs helps alone to determine the optimal treatment option. Nodule size remains relevant to recommend biopsy for the intermediate-risk category.

Thyroid imaging reporting and data system (TIRADS) for ultrasound features of nodules: multicentric retrospective study in China

PURPOSE

To establish a practical and simplified Chinese thyroid imaging reporting and data system (C-TIRADS) based on the Chinese patient database.

METHODS

A total of 2141 thyroid nodules that were neither cystic nor spongy were used in the current study. These specimens were derived from 2141 patients in 131 alliance hospitals of the Chinese Artificial Intelligence Alliance for Thyroid and Breast Ultrasound. The ultrasound features, including location, orientation, margin, halo, composition, echogenicity, echotexture, echogenic foci and posterior features were assessed. Univariate and multivariate analyses were performed to investigate the association between ultrasound features and malignancy. The regression equation, the weighting, and the counting methods were used to determine the malignant risk of the thyroid nodules. The areas under the receiver operating characteristic curve (Az values) were calculated.

RESULTS

Of the 2141 thyroid nodules, 1572 were benign, 565 were malignant, and 4 were borderline. Vertical orientation, ill-defined, or irregular margin (including extrathyroidal extension), microcalcifications, solid, and markedly hypoechoic were positively associated with malignancy, while comet-tail artifacts were negatively associated with malignancy. The logistic regression equation yielded the highest Az value of 0.913, which was significantly higher than that obtained using the weighting method (0.893) and the counting method (0.890); however, no significant difference was found between the latter two. The C-TIRADS, based on the counting method, was designed following the principle of balancing the diagnostic performance and sensitivity of the risk stratification with the ease of use.

CONCLUSIONS

A relatively simple C-TIRADS was established using the counting value of positive and negative ultrasound features.

Ultrasonic thyroid nodule detection method based on U-Net network

OBJECTIVE

Aiming at the time consuming processing of existing thyroid nodule detection and difficulty in feature extraction, U-Net-based thyroid nodule detection is proposed to perform computed aided diagnosis.

METHOD

This paper proposes a mark-guided ultrasound deep network segmentation model of thyroid nodules. By comparing with VGG19, Inception V3, DenseNet 161, segmentation accuracy, segmentation edge and network operation time, it is found that the algorithm in this paper has relative advantages.

RESULTS

U-Net network-based ultrasound thyroid nodules segmented the nodule area overlapped with the manually depicted nodule area close to 100%, the segmentation accuracy rate was as high as 0.9785, and the U-Net segmentation result was closer to the manually depicted nodule. The accuracy of U-Net segmentation of the thyroid is about 3% higher than the other three networks.

CONCLUSION

The segmentation of nodules based on U-Net proposed in this paper significantly improves the segmentation accuracy of thyroid nodules with a small training data set, and provides a comprehensive reference for clinical diagnosis and treatment.

Diagnostic Performance of Ultrasound Computer-Aided Diagnosis Software Compared with That of Radiologists with Different Levels of Expertise for Thyroid Malignancy: A Multicenter Prospective Study

The aim of the work described here was to evaluate the diagnostic performance of ultrasound thyroid computer-aided diagnosis (CAD) software. This multicenter prospective study included 494 patients (565 thyroid nodules) who underwent surgery or biopsy after ultrasonography at four hospitals from January 2019 to September 2019. The diagnostic performance metrics of different readers were calculated and compared with the pathologic results. The sensitivity of CAD was outstanding and was equivalent to that of a senior radiologist (90.51% vs. 88.47%, p > 0.05). The area under the curve of CAD was equivalent to that of a junior radiologist (0.748 vs. 0.739, p > 0.05). However, the specificity was only 49.63%, which was lower than those of the three radiologists (75.56%, 85.93% and 90.37% for the junior, intermediate and senior radiologists, respectively). The diagnostic performance of the junior radiologist was significantly improved with the aid of CAD (junior + CAD). The sensitivity and area under the curve of junior + CAD were improved from 72.20% to 89.93% and from 0.739 to 0.816, respectively (both p values <0.05), and the positive predictive value, negative predictive value and κ coefficient improved from 76.3% to 78.6%, 82.0% to 86.8% and 0.394 to 0.511, respectively. Though specificity slightly decreased from 75.56% to 73.33%, the difference was not statistically significant (p > 0.05). In general, the clinical application value of CAD is promising, and its instrumental value for junior radiologists is significant.

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Ultrasonography (US) is the primary diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and postFNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interobserver variability is moderate to substantial, unnecessary FNA and/or diagnostic surgery are common in practice. Artificial intelligence (AI)-based computeraided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in unnecessary FNA. This review provides a developmental overview of the AI-based CAD systems currently used for thyroid nodules and describes the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.

Comparison of Multimodal Ultrasound Imaging with Conventional Ultrasound Risk Stratification Systems in Presurgical Risk Stratification of Thyroid Nodules

BACKGROUND

Ultrasonography (US) is an indispensable tool in the management of thyroid nodules, not only for assessing tumor characteristics but also to assign risk of malignancy and guide in management. Various guidelines and US-based risk stratification systems have been proposed for this purpose. This study aims to compare the diagnostic performances of multimodal US-based risk scores (French TIRADS, TMC-RSS) with conventional US-based scoring systems (Korean TIRADS, ACR-TIRADS, ATA risk stratification).

MATERIAL AND METHODS

A total of 168 nodules from 139 patients were studied and categorized in each of the risk stratification systems. Sensitivity, specificity, positive and negative predictive values, and accuracy of each system were computed. ROC curves were plotted and area under curve (AUC) for each scoring system noted.

RESULTS

Thirty five (21%) of the 168 nodules were malignant on final histopathological examination. TMC-RSS fared the best in predicting malignant nodules with a sensitivity of 96.2% and specificity of 88.6%, while the PPV and NPV were 97% and 86.1%, respectively. The AUC for TMC-RSS was 0.924 (95% CI, 0.860-0.988; P < 0.001).

CONCLUSION

Multimodal US-based risk stratification incorporating non-grayscale characteristics in addition to conventional systems like the TMC-RSS improves the diagnostic performance of ultrasound imaging of thyroid nodules.

Thyroid Nodules with Isolated Macrocalcifications: Malignancy Risk of Isolated Macrocalcifications and Postoperative Risk Stratification of Malignant Tumors Manifesting as Isolated Macrocalcifications

Objective

To determine the malignancy risk of isolated macrocalcifications (a calcified nodule with complete posterior acoustic shadowing) detected on ultrasonography (US) and to evaluate the postoperative American Thyroid Association (ATA) risk stratification of malignant tumors manifesting as isolated macrocalcifications.

Materials and Methods

A total of 3852 thyroid nodules (≥ 1 cm) of 3061 consecutive patients who had undergone biopsy between January 2011 and June 2018 were included in this study. We assessed the prevalence, malignancy rate, and size distribution of isolated macrocalcifications and evaluated the histopathologic features and postoperative ATA risk stratification of malignant tumors manifesting as isolated macrocalcifications.

Results

Isolated macrocalcifications were found in 38 (1.2%) of the 3061 patients. Final diagnosis was established in 30 (78.9%) nodules; seven malignant tumors were diagnosed as papillary thyroid carcinomas (PTCs). The malignancy rate of the isolated macrocalcifications was 23.3% in the 30 nodules with final diagnoses and 18.4% in all nodules. Among the six surgically-treated malignant tumors, five (83.3%) had an extrathyroidal extension (ETE) (minor ETE 1, gross ETE 4), and two (33.3%) had macroscopic lymph node metastasis. Four (66.7%) malignant tumors were categorized as high-risk tumors, one as an intermediate-risk tumor, and one as a low-risk tumor using the ATA risk stratification. Histopathologically, out of the six malignant tumors, ossifications were noted in four (66.7%) and predominant calcifications in two (33.3%).

Conclusion

The US pattern of isolated macrocalcifications (≥ 1 cm) showed an intermediate malignancy risk (at least 18.4%). All malignant tumors were PTCs, and most showed an aggressive behavior and a high or intermediate postoperative ATA risk.

Comparisons of ACR TI-RADS, ATA guidelines, Kwak TI-RADS, and KTA/KSThR guidelines in malignancy risk stratification of thyroid nodules

OBJECTIVE

To compare the diagnostic performance and the unnecessary biopsy rates for recommending fine needle aspiration (FNA) of Thyroid Imaging Reporting and Data Systems proposed by American College of Radiology (ACR TI-RADS), American Thyroid Association (ATA) guidelines, TI-RADS proposed by Kwak (Kwak TI-RADS), and Korean Thyroid Association/Korean Society of Thyroid Radiology (KTA/KSThR) guidelines for malignancy risk stratification of thyroid nodules (TNs).

METHODS

The study included 1271 TNs whose cytologic results or surgical pathologic findings were available. Ultrasound images of these TNs were retrospectively reviewed and categorized according to the four guidelines. The diagnostic performances and the unnecessary biopsy rates for recommending FNA of the four guidelines were evaluated.

RESULTS

After multivariate analysis, the most significant independent predictor for malignancy was hypoechogenicity/marked hypoechogenicity (OR: 9.37, 95% CI: 5.40-16.26) (P < 0.001) among the suspicious ultrasound images features. For all nodules and two subgroups (i.e. nodules <10 mm group and nodules ≥10 mm group), ACR TI-RADS demonstrated higher specificities (all P < 0.05) and lower sensitivities (all P < 0.001) than the other guidelines. In the all nodules group and the nodules<10 mm group, ACR TI-RADS and Kwak TI-RADS had higher Azs than the other guidelines (all P < 0.01). The unnecessary biopsy rates for recommending FNA of ACR TI-RADS in the all nodules (≥10 mm) group and the subgroup (10∼19 mm) were all lower than those of the others guidelines (P < 0.001 for all). For the subgroup (≥20 mm), the unnecessary biopsy rate of ACR was lower than that of ATA guidelines and KTA/KSThR guidelines (P < 0.001).

CONCLUSIONS

The four guidelines have good diagnostic efficiency in differentiating TNs. ACR TI-RADS and Kwak TI-RADS have better diagnostic performance than the other guidelines in the all nodules group and the nodules<10 mm group. Considering the comprehensive diagnostic efficacy and unnecessary biopsy rate, ACR TI-RADS is a more desirable classification guideline in clinical practice.

A retrospective study of reducing unnecessary thyroid biopsy for American College of Radiology Thyroid Imaging Reporting and Data Systems 4 assessment through applying shear wave elastography

Objective The purpose of the study is to quantitatively assess shear-wave elastography (SWE) value in American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR TI-RADS) 4. Materials and methods One hundred and fifty-two ACR TI-RADS 4 thyroid nodules undergoing SWE were included in the study. The mean (EMean), minimum (EMin) and maximum (EMax) of SWE elasticity were measured. Results The areas under the receiver operating characteristic (ROC) curves for SWE EMean, EMin and EMax in detecting benign and malignant nodules were 0.95, 0.83 and 0.84, respectively. Cut-off value of EMean ≤ 23.30 kPa is able to downgrade the lesion category to ACR TI-RADS 3 and cut-off value of EMean ≥ 52.14 kPa is able to upgrade the lesion category to ACR TI-RADS 5. Conclusions The EMean of SWE will probably identify nodules that have a high potential for benignity in ACR TI-RADS 4. It may help identify and select benign nodules while reducing unnecessary biopsy of benign thyroid nodules.

Concordance of the ACR TI-RADS

BACKGROUND

Ultrasonography (US) is the method of choice for evaluating thyroid nodules. In 2017, the American College of Radiology (ACR) created a classification system based on US characteristics. For the system to be adopted, it must be reproducible.

OBJECTIVES

To determine the intraobserver and interobserver variability of the ACR TI-RADS.

METHODS

Cross-sectional study; three radiologists with different levels of experience used the ACR TI-RADS to classify 100 nodules on two occasions one month apart, and we calculated the intraobserver and interobserver variability.

RESULTS

Regarding intraobserver variability, the first radiologist had nearly perfect concordance for composition, echogenicity, shape, and margins and substantial concordance for echogenic foci; the second radiologist had nearly perfect concordance for composition, echogenicity, shape, and margins and substantial concordance for echogenic foci, and the third radiologist had nearly perfect concordance for composition, echogenicity, and shape and substantial concordance for margins and echogenic foci. The interobserver concordance was calculated for the two readings; the concordance was substantial except for shape in the first reading and for echogenicity and margins in the second reading, which had moderate concordance.

CONCLUSIONS

The ACR TI-RADS classification system is reproducible.

The relationship of thyroid nodule size on malignancy risk according to histological type of thyroid cancer

Background

Although several studies have examined the value of thyroid nodule size as a malignancy predictor, the results are conflicting.

Purpose

To investigate the relationship between nodule size and malignancy risk and to evaluate the impact of nodule size on the false-negative rate of fine needle aspiration or core needle biopsy according to the histological type of thyroid cancer.

Material and Methods

From January 2013 to December 2013, 3970 thyroid nodules that underwent ultrasound-guided fine needle aspiration or core needle biopsy were retrospectively reviewed. We assessed the relationship between nodule size and malignancy risk according to histological type of thyroid cancer. In addition, we compared the false-negative rate by thyroid nodule size category.

Results

Of 3970 thyroid nodules, 1170 nodules were malignant. For papillary thyroid carcinoma, nodule size was inversely related to malignancy risk, whereas in nodules of follicular carcinoma and follicular variant papillary thyroid carcinoma, nodule size was positively related to malignancy risk ( P < 0.001). The false-negative rate tended to increase as nodule size increased ( P = 0.002) for all nodules and the overall false-negative rate was 2.3%.

Conclusion

Overall, nodule size does not correlate with risk of malignancy, but the relationship between nodule size and malignancy risk depends on the histological type of thyroid cancer.

Accuracy of thyroid imaging reporting and data system category 4 or 5 for diagnosing malignancy: a systematic review and meta-analysis

OBJECTIVES

To determine the accuracies of the American College of Radiology (ACR)-thyroid imaging reporting and data systems (TIRADS), Korean (K)-TIRADS, and European (EU)-TIRADS for diagnosing malignancy in thyroid nodules.

METHODS

Original studies reporting the diagnostic accuracy of TIRADS for determining malignancy on ultrasound were identified in MEDLINE and EMBASE up to June 23, 2019. The meta-analytic summary sensitivity and specificity were obtained for TIRADS category 5 (TR-5) and category 4 or 5 (TR-4/5), using a bivariate random effects model. To explore study heterogeneity, meta-regression analyses were performed.

RESULTS

Of the 34 eligible articles (37,585 nodules), 25 used ACR-TIRADS, 12 used K-TIRADS, and seven used EU-TIRADS. For TR-5, the meta-analytic sensitivity was highest for EU-TIRADS (78% [95% confidence interval, 64-88%]), followed by ACR-TIRADS (70% [61-79%]) and K-TIRADS (64% [58-70%]), although the differences were not significant. K-TIRADS showed the highest meta-analytic specificity (93% [91-95%]), which was similar to ACR-TIRADS (89% [85-92%]) and EU-TIRADS (89% [77-95%]). For TR-4/5, all three TIRADS systems had sensitivities higher than 90%. K-TIRADS had the highest specificity (61% [50-72%]), followed by ACR-TIRADS (49% [43-56%]) and EU-TIRADS (48% [35-62%]), although the differences were not significant. Considerable threshold effects were noted with ACR- and K-TIRADS (p ≤ 0.01), with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity (p ≤ 0.05).

CONCLUSIONS

There was no significant difference among these three international TIRADS, but the trend toward higher sensitivity with EU-TIRADS and higher specificity with K-TIRADS.

KEY POINTS

• For TIRADS category 5, the meta-analytic sensitivity was highest for the EU-TIRADS, followed by the ACR-TIRADS and the K-TIRADS, although the differences were not significant. • For TIRADS category 5, K-TIRADS showed the highest meta-analytic specificity, which was similar to ACR-TIRADS and EU-TIRADS. • Considerable threshold effects were noted with ACR- and K-TIRADS, with subject enrollment, country of origin, experience level of reviewer, number of patients, and clarity of blinding in review being the main causes of heterogeneity.

TI-RADS Diagnostic Performance: Which Algorithm is Superior and How Elastography and 4D Vascularity Improve the Malignancy Risk Assessment

Given the increased prevalence of thyroid nodules in the general population (~50%), the real challenge resides in correctly recognizing the suspicious ones. This study proposes to compare four important Thyroid Imaging and Reporting Data Systems (TI-RADS) and evaluate the contribution of elastography and 4D Color Doppler assessment of vascularity in estimating the risk of malignancy. In the study, 133 nodules with histopathological examination were included. Of these, 35 (26.31%) proved to be malignant. All nodules were classified using the four selected systems and our proposed improved score. The American College of Radiology (ACR) and EU TI-RADS had good sensitivity (94.28%, 97.14%) and NPV (93.33%, 95.83%), but fairly poor specificity (31.81%, 23.46%) and PPV (35.48%, 31.19%), with an accuracy of 42.8% and 45.8%, respectively. Horvath TI-RADS had better accuracy of 66.9% and somewhat improved specificity (62.24%), but poorer sensitivity (80%). Russ’ French TI-RADS includes elastography in the risk assessment strategy. This classification proved superior in all aspects (Se: 91.42%, Sp:82.65%, NPV:96.42%, PPV:65.30%, and Acc of 84.96%). The mean strain ratio (SR) value for malignant lesions was 5.56, while the mean SR value for benign ones was significantly lower, 2.54 (p < 0.05). It also correlated well with the response variable: histopathological result (p < 0.001). Although, adding 4D vascularity to the French score generated a similar calculated accuracy and from a statistical point of view, the parameter itself proved beneficial for predicting the malignancy risk (p < 0.001) and may add important knowledge in uncertain situations. Advanced ultrasound techniques definitely improved the risk estimation and should be used more extensively.

Ultrasound Classification of Thyroid Nodules: A Systematic Review

Ultrasound (US) based classification systems exist for the stratification of thyroid nodules based on the risk for malignancy. This systematic review aimed to assess the evidence for the performance of US-based thyroid nodule classification systems through correlation with fine needle aspiration biopsy (FNAB). PubMed and Scopus were searched using keywords that included ‘ultrasound classification’, ‘thyroid nodules’, ‘fine needle aspiration’, and ‘malignancy’. Inclusion criteria were as follows: studies/reviews reporting on US imaging for the classification of thyroid nodules. Exclusion criteria were as follows: no comparison between US imaging findings and histology reports based on FNAB, no full English text available/accessible. The database searches identified 66 publications. After evaluation, 12 studies met the inclusion criteria. Two US-based classification systems for thyroid nodules were assessed: the Thyroid Imaging Reporting and Data System (TIRADS) and the American Thyroid Association (ATA) guidelines. For TIRADS, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) ranged from 70.6% to 97.4%, 29.3% to 90.4%, 23.3% to 64.3%, and 87.1% to 99.0%, respectively. The median sensitivity, specificity, PPV, and NPV for TIRADS was 90.0%, 57.4%, 49.0%, and 91.0%, respectively. One study comparing TIRADS with the ATA guidelines demonstrated that TIRADS was superior in terms of sensitivity, whereas the ATA guidelines were superior in terms of specificity and PPV. The high sensitivity and NPV of the US-based TIRADS classification system have excellent utility for correctly classifying nodules as positive for malignant disease and for predicting the absence of malignant disease. The paucity of studies assessing the ATA guidelines highlights avenues for further research comparing TIRADS with other systems of thyroid nodule classification.

US-based risk stratification "guidelines" for thyroid nodules: Quō Vādis?

PURPOSE

This rapid scoping review addresses the commentary titled the ACR TI-RADS™: An Advance in the Management of Thyroid Nodules or Pandora's Box of Surveillance? suggesting that the 2017 American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS™-2017) adds to a plethora of existing guidelines, incorporates an inconsistent lexicon, and potentially contradicts recommendations.

MATERIALS AND METHODS

The author performed a rapid scoping review using a combination of English keywords to identify and review peer reviewed articles contained in electronic databases (e-databases) comparing 2 or more guidelines for managing adult thyroid nodules (GMTNs) with (UGMTNs) and without (non-UGMTNs) ultrasound. E-databases included Medline (PubMed), EBSCO, Google, and Google Scholar published (2010-2019).

RESULTS

The search returned 28 articles, where the author identified 12 different guidelines. Most articles evaluated diagnostic performance (N = 26), not quality (N = 2) measures. The most commonly reviewed UGMTNs were in descending order ATA-2015, ACR TI-RADS™-2017, South Korean, and EU TI-RADS. No article reviewed all GMTNs or identified a generally accepted UGMTNs or non-UGMTNs. Primary origin continents were: North America (U.S.A), Asia (Japan, South Korea, Thailand), Europe (France, Italy, U.K.), and South America (Chile).

CONCLUSION

A plethora of UGMTNs may exist. No guideline enjoys general acceptance and evaluations of performance and quality vary.

A randomized trial comparing health-related quality-of-life and utility measures between routine fine-needle aspiration cytology (FNAC) and surveillance alone in patients with thyroid incidentaloma measuring 1-2 cm

PURPOSE

To present the impact of treatment on health-related quality-of-life (HRQOL) and health utility measures from the randomized controlled trial (ClinicalTrials.gov Identifier: NCT02398721) that investigated the FNAC versus watchful surveillance in patients with incidental benign thyroid nodules.

METHODS

Health utility and HRQOL were evaluated using the EQ-5D 5-level (EQ-5D-5L), 6-item Short-Form Health Survey (SF-6D), and generic 12-item Short-Form Health Survey (SF-12v2) at baseline, 3-month, 6-month, and 12-month assessments. A repeated measure analysis of variance evaluated differences in HRQOL scores between treatment groups over time. Multiple imputations were used to impute missing data at each time point.

RESULTS

HRQOL data completion rates were 99.7% at baseline, 92.7% at 3-month, 93.9% at 6-month, 92.7% at 12-month, and 88.6% at 18-month follow-up after baseline. There were significant mean differences in SF-6D, EQ-5D-5L, and SF-12v2 over time except the domain of vitality and mental health of SF-12v2. Mean change of SF-12v2 scores and utility scores from baseline between groups did not exceed minimal important difference. No significant treatment group by time interactions were found in all HRQOL and utility scores except in the vitality domain and PCS of SF-12v2 (p value = 0.033; 0.024).

CONCLUSIONS

When compared with watchful surveillance, FNAC intervention was associated with better vitality and physical-related HRQOL scores but did not provide better preservation of utility score improvement over the 18-month period. These findings support the routine FNAC approach for nodules that have a low-suspicion sonographic pattern and measure between 1.0 and 2.0 cm.

Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques

Objective

Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented.

Data sources

An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning.

Study selection

Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis.

Results

Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis.

Conclusion

Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use.

A multicentre validation study for the EU-TIRADS using histological diagnosis as a gold standard

OBJECTIVE

Many systems for risk stratification of thyroid nodule with ultrasound (US) have been proposed and the EU-TIRADS issued by the ETA in 2017 was the last to have been published. The present study was undertaken to evaluate whether the malignancy risk of each category corresponded to the given range of the guidelines and assess the diagnostic value of EU-TIRADS in a multi-institutional trial with histology as gold standard.

DESIGN

Three institutions in Switzerland, France and United Kingdom shared this retrospective study. Enrolment period was 2013-2017. Included were patients who had undergone surgery with a detailed preoperative thyroid US.

METHODS

Cancer risk was calculated for each EU-TIRADS score. Predictivity tests were estimated. Nonparametric statistical analysis was used.

RESULTS

The final series included 1058 nodules of which 257 (24.3%) carcinomas. Nodules were classified as EU-TIRADS 2, 3, 4 and 5 in 6.7, 46.4, 26.2 and 20.7%, respectively. Cancer prevalence was 1.4, 3.5, 17 and 87.7% in classes 2-5, respectively (P < 0.0001). EU-TIRADS 5 had a significantly higher cancer rate than the other summed categories (7.7%; P < 0.0001) with OR 84.7. When EU-TIRADS 4 and 5 were combined, 93% sensitivity and 97% NPV were found and findings of the three institutions were quite similar. Using the recommended criteria for FNA negative predictive value was 90.9%.

CONCLUSIONS

The cancer rate was within or close to the given range described in the EU-TIRADS guidelines. The diagnostic value was satisfactory. The results were similar in the three institutions participating in the study.