Inter- and Intra-Observer Agreement in Ultrasound BI-RADS Classification and Real-Time Elastography Tsukuba Score Assessment of Breast Lesions

Curated benchmark dataset for ultrasound based breast lesion analysis

A new detailed dataset of breast ultrasound scans (BrEaST) containing images of benign and malignant lesions as well as normal tissue examples, is presented. The dataset consists of 256 breast scans collected from 256 patients. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image using a freehand annotation and labeled according to BIRADS features and lexicon. The histopathological classification of the tumor was also provided for patients who underwent a biopsy. The BrEaST dataset is the first breast ultrasound dataset containing patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or core needle biopsy result. To enable research into breast disease detection, tumor segmentation and classification, the BrEaST dataset is made publicly available with the CC-BY 4.0 license.

Development and Validation of a Nomogram for Predicting Breast Malignancy in Male Patients Based on Clinical and Ultrasound Features

OBJECTIVE

This study aimed to construct a nomogram based on clinical and ultrasound (US) features to predict breast malignancy in males.

METHODS

The medical records between August, 2021 and February, 2023 were retrospectively collected from the database. Patients included in this study were randomly divided into training and validation sets in a 7:3 ratio. The models for predicting the risk of malignancy in male patients with breast lesions were virtualized by the nomograms.

RESULTS

Among the 71 enrolled patients, 50 were grouped into the training set, while 21 were grouped into the validation set. After the multivariate analysis was done, pain, BI-RADS category, and elastography score were identified as the predictors for malignancy risk and were selected to generate the nomogram. The C-index was 0.931 for the model. Concordance between predictions and observations was detected by calibration curves and was found to be good in this study. The model achieved a net benefit across all threshold probabilities, which was shown by the decision curve analysis (DCA) curve.

CONCLUSION

We successfully constructed a nomogram to evaluate the risk of breast malignancy in males using clinical and US features, including pain, BI-RADS category, and elastography score, which yielded good predictive performance.

The Application of Nanoparticle-Based Imaging and Phototherapy for Female Reproductive Organs Diseases

Various female reproductive disorders affect millions of women worldwide and bring many troubles to women's daily life. Let alone, gynecological cancer (such as ovarian cancer and cervical cancer) is a severe threat to most women's lives. Endometriosis, pelvic inflammatory disease, and other chronic diseases-induced pain have significantly harmed women's physical and mental health. Despite recent advances in the female reproductive field, the existing challenges are still enormous such as personalization of disease, difficulty in diagnosing early cancers, antibiotic resistance in infectious diseases, etc. To confront such challenges, nanoparticle-based imaging tools and phototherapies that offer minimally invasive detection and treatment of reproductive tract-associated pathologies are indispensable and innovative. Of late, several clinical trials have also been conducted using nanoparticles for the early detection of female reproductive tract infections and cancers, targeted drug delivery, and cellular therapeutics. However, these nanoparticle trials are still nascent due to the body's delicate and complex female reproductive system. The present review comprehensively focuses on emerging nanoparticle-based imaging and phototherapies applications, which hold enormous promise for improved early diagnosis and effective treatments of various female reproductive organ diseases.

The feasibility of a novel computer-aided classification system for the characterisation and diagnosis of breast masses on ultrasound: a single-centre preliminary test study

AIM

To introduce a novel computer-aided classification (CAC) system and investigate the feasibility of characterising and diagnosing breast masses on ultrasound (US).

MATERIALS AND METHODS

A total of 246 breast masses were included. US features and the final assessment categories of the breast masses were analysed by a radiologist and the CAC system according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CAC system evaluated the BI-RADS assessment from the fusion of multi-view and colour Doppler US images without (SmartBreast) or with combining clinical variables (m-CAC system). The diagnostic performance and agreement of US characteristics between the radiologist and the CAC system were compared.

RESULTS

The agreement between the radiologist and the CAC system was substantial for mass shape (κ = 0.673), orientation (κ = 0.682), margin (κ = 0.622), posterior features (κ = 0.629), calcifications in a mass (κ = 0.709) and vascularity (κ = 0.745), fair for echo pattern (κ = 0.379), and moderate for BI-RADS assessment (κ = 0.575). With BI-RADS 4a as the cut-off value, the specificity (52.5% versus 25%, p<0.0001) and accuracy (73.98% versus 62.6%, p=0.0002) of the m-CAC system were improved without significant loss of sensitivity (94.44% versus 98.41%, p=0.1250) compared with the SmartBreast. The m-CAC system showed similar specificity (52.5% versus 45.83%, p=0.2430) and accuracy (73.98% versus 73.58%, p=1.0000) as the radiologist, but a lower sensitivity (94.44% versus 100%, p=0.0156).

CONCLUSION

The CAC system showed an acceptable agreement with the radiologist for characterisation of breast lesions. It has the potential to mimic the decision-making behaviour of radiologists for the classification of breast lesions.

Clinical use and adjustment of ultrasound elastography for breast lesions followed WFUMB guidelines and recommendations in the real world

Objective

This study aimed to explore the value of strain elastography (SE) and shear wave elastography (SWE) following the World Federation of Ultrasound in Medicine and Biology (WFUMB) guidelines and recommendations in the real world in distinguishing benign and malignant breast lesions and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions.

Methods

This prospective study included 274 breast lesions. The elastography score (ES) by the Tsukuba score, the strain ratio (SR) for SE, and Emax for SWE of the lesion(A) and the regions(A') included the lesion and the margin (0.5-5 mm) surrounding the lesion were measured. The sensitivity, specificity, and AUC were calculated and compared by the cutoff values recommended by WFUMB guidelines.

Results

When scores of 1 to 3 were classified as probably benign by WFUMB recommendation, the ES was significantly higher in malignant lesions compared to benign lesions (p < 0.05) in all lesions. For the cohort by size >20 mm, the sensitivity was 100%, and the specificity was 45.5%. ES had the highest AUC: 0.79(95% CI 0.72-0.86) with a sensitivity of 96.2%, and a specificity of 61.8% for the cohort by size ≤20 mm. For the Emax-A'-S2.5mm, when the high stiffness would be considered with Emax above 80 kPa in SWE, the malignant lesions were diagnosed with a sensitivity of 95.8%, a specificity of 43.3% for all lesions, a sensitivity of 88.5% for lesions with size ≤20 mm, and sensitivity of 100.0% for lesions with size >20 mm. In 84 lesions of BI-RADS category 4a, if category 4a lesions with ES of 1-3 points or Emax-A'-S2.5 less than 80 kPa could be downgraded to category 3, 52 (61.9%) lesions could be no biopsy, including two malignancies. If category 4a lesions with ES of 1-3 points and Emax-A'-S2.5 less than 80kPa could be downgraded to category 3, 23 (27.4%) lesions could be no biopsy, with no malignancy.

Conclusions

The elastography score for SE and Emax-A' for SWE after our modification were beneficial in the diagnosis of breast cancer. The combination of SWE and SE could effectively reduce the biopsy rate of BI-RADS category 4a lesions.

High-Frequency Ultrasound and Shear Wave Elastography in Quantitative Differential Diagnosis of High-Risk and Low-Risk Basal Cell Carcinomas

OBJECTIVES

The purpose of this study was to investigate the value of high-frequency ultrasound and shear wave elastography (SWE) in quantitative differential diagnosis of high-risk and low-risk basal cell carcinomas (BCCs).

METHODS

A total of 52 BCCs confirmed by surgical pathology were studied. Taking pathologic subtypes as reference, all the cases were classified as high-risk BCCs or low-risk BCCs. High-frequency ultrasound parameters and SWE parameters recorded preoperatively were retrospectively analyzed. The differences of two groups were compared.

RESULTS

There were 12 high-risk BCCs and 40 low-risk BCCs. The maximum infiltration depth (MID) and average Young's modulus (E_ave ) of high-risk BCCs were 5.76 ± 2.56 mm and 31.61 ± 12.36 kPa, whereas of low-risk BCCs were 4.29 ± 1.77 mm and 20.04 ± 4.74 kPa, respectively, P < .05. The area under the receiver operator characteristic curve of MID and E_ave were 0.714 and 0.811, P > .05. Taking 5.5 mm of MID and 24.45 kPa of E_ave as the threshold for the diagnosis of high-risk BCCs, the sensitivity, specificity, and accuracy were 58.3%, 82.5%, 76.9% and 75.0%, 82.5%, 80.8%, P > .05.

CONCLUSIONS

The MID and E_ave of the lesion can be used to determine the recurrence risk of BCCs and provide a reference for the development of individualized treatment plans.

Differing benefits of artificial intelligence-based computer-aided diagnosis (AI-CAD) for breast US according to workflow and experience level

Purpose

This study evaluated how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows.

Methods

Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD.

Results

After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists.

Conclusion

Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study

Purpose

To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.

Materials and Methods

A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.

Results

A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis.

Conclusion

A 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

Purpose

Through this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China.

Materials and Methods

The survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis.

Results

Nine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37–0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%).

Conclusion

We found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features.

Comparing deep learning-based automatic segmentation of breast masses to expert interobserver variability in ultrasound imaging

Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep learning to medical data for a variety of tasks across multiple imaging modalities. The reliability and repeatability of deep learning techniques are of utmost importance if deep learning can be considered a tool for assisting experts, including physicians, radiologists, and sonographers. Owing to the high costs of labeling data, deep learning models are often evaluated against one expert, and it is unknown if any errors fall within a clinically acceptable range. Ultrasound is a commonly used imaging modality for breast cancer screening processes and for visually estimating risk using the Breast Imaging Reporting and Data System score. This process is highly dependent on the skills and experience of the sonographers and radiologists, thereby leading to interobserver variability and interpretation. For these reasons, we propose an interobserver reliability study comparing the performance of a current top-performing deep learning segmentation model against three experts who manually segmented suspicious breast lesions in clinical ultrasound (US) images. We pretrained the model using a US thyroid segmentation dataset with 455 patients and 50,993 images, and trained the model using a US breast segmentation dataset with 733 patients and 29,884 images. We found a mean Fleiss kappa value of 0.78 for the performance of three experts in breast mass segmentation compared to a mean Fleiss kappa value of 0.79 for the performance of experts and the optimized deep learning model.

Applications of Mechanical Arm in the Virtual Touch Tissue Imaging Quantification and the Differential Diagnosis of Breast Tumors

ABSTRACT

To assess the diagnostic performance of quantitative parameters for virtual touch tissue imaging quantification by the mechanical arm probe, 128 women with 152 breast lesions (90 benign and 62 malignant) were included and maximum elasticity (Vmax), median elasticity (Vmean), maximum elasticity minus minimum (Vmax-min), and minimum elasticity (Vmin) were measured. Intercorrelation coefficients and Bland-Altman plots were used to compare the repeatability and consistency of handheld probe and mechanical arm measurements. Receiver operating characteristic curve was used to evaluate the diagnostic efficiency. The results showed that intercorrelation coefficients were 0.694 to 0.951 by the mechanical arm and 95% limits of agreement were wider than handheld probe. Using 4.36 m/s as cutoff value for Vmax by the mechanical arm, we achieved sensitivity (88.7%) and specificity (90.0%). We concluded that the mechanical arm can reduce the influence of different scanning pressures on the elastic modulus of breast tissue.

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound

BACKGROUND

This study prospectively investigates the agreement between radial (r-US) and meander-like (m-US) breast ultrasound with regard to lesion location, lesion size, morphological characteristics and final BI-RADS classification of individual breast lesions.

METHODS

Each patient of a consecutive, unselected, mixed collective received a dual ultrasound examination.

RESULTS

The agreement between r-US and m-US for lesion location ranged from good (lesion to mammilla distance ICC 0.64; lesion to skin distance ICC 0.72) to substantial (clock-face localization κ 0.70). For lesion size the agreement was good (diameter ICC 0.72; volume ICC 0.69), for lesion margin and architectural distortion it was substantial (κ 0.68 and 0.70, respectively). Most importantly, there was a substantial agreement (κ 0.76) in the final BI-RADS classification between r-US and m-US.

CONCLUSIONS

Our recent comparison of radial and meander-like breast US revealed that the diagnostic accuracy of the two scanning methods was comparable. In this study, we observe a high degree of agreement between m-US and r-US for the lesion description (location, size, morphology) and final BI-RADS classification. These findings corroborate that r-US is a suitable alternative to m-US in daily clinical practice. Trial registration NCT02358837. Registered January 2015, retrospectively registered https://clinicaltrials.gov/ct2/results?cond=&term=NCT02358837&cntry=&state=&city=&dist =.

Application of ultrasonic dual-mode artificially intelligent architecture in assisting radiologists with different diagnostic levels on breast masses classification

PURPOSE

We aimed to compare the diagnostic performance and interobserver variability in breast tumor classification with or without the aid of an innovative dual-mode artificial intelligence (AI) architecture, which can automatically integrate information from ultrasonography (US) and shear-wave elastography (SWE).

METHODS

Diagnostic performance assessment was performed with a test subset, containing 599 images (from September 2018 to February 2019) from 91 patients including 64 benign and 27 malignant breast tumors. Six radiologists (three inexperienced, three experienced) were assigned to read images independently (independent diagnosis) and then make a secondary diagnosis with the knowledge of AI results. Sensitivity, specificity, accuracy, receiver-operator characteristics (ROC) curve analysis and Cohen's κ statistics were calculated.

RESULTS

In the inexperienced radiologists' group, the average area under the ROC curve (AUC) for diagnostic performance increased from 0.722 to 0.765 (p = 0.050) with secondary diagnosis using US-mode and from 0.794 to 0.834 (p = 0.019) with secondary diagnosis using dual-mode compared with independent diagnosis. In the experienced radiologists' group, the average AUC for diagnostic performance was significantly higher with AI system using the US-mode (0.812 vs. 0.833, p = 0.039), but not for dual-mode (0.858 vs. 0.866, p = 0.458). Using the US-mode, interobserver agreement among all radiologists improved from fair to moderate (p = 0.003). Using the dual-mode, substantial agreement was seen among the experienced radiologists (0.65 to 0.74, p = 0.017) and all radiologists (0.62 to 0.73, p = 0.001).

CONCLUSION

AI assistance provides a more pronounced improvement in diagnostic performance for the inexperienced radiologists; meanwhile, the experienced radiologists benefit more from AI in reducing interobserver variability.

Interobserver and intermethod variability in data interpretation of breast strain elastography in suspicious breast lesions

Background/aim

Strain elastography has the disadvantage of being operator-dependent. Interobserver variability is observed during image acquisition and interpretation. This study aimed to analyze the interobserver and intermethod variability of strain elastography in image interpretation and evaluate the diagnostic performance combining elasticity score and strain ratio with ultrasonography.

Materials and methods

A retrospective study was conducted on 70 breast lesions evaluated with B-mode ultrasonography and strain elastography. B-mode ultrasonography findings, elasticity scores, and strain ratio values were evaluated using static images by two radiologists. BI-RADS assessment of the lesions and the decision of both observers as to whether the biopsy was required using B-mode ultrasonography, and the combined ultrasonography+elasticity score, and the combined ultrasonography+elasticity score+strain ratio were compared with the histopathological results. Also, the interobserver agreement was analyzed for all the combinations.

Results

There was very good agreement (weighted κ = 0.865) between the observers for the elasticity scores. Very good agreement was observed between the observers for BI-RADS assessments using the combined ultrasonography+elasticity score and the combined ultrasonography+elasticity score+strain ratio (weighted κ = 0.848, and 0.902, respectively). Area under the curve of B-mode ultrasonography, the combined B-mode ultrasonography+elasticity score, and the combined B-mode ultrasonography+elasticity score+strain ratio, were calculated as 0.859, 0.866, and 0.916 for observer 1, and 0.851, 0.829, and 0.916 for observer 2, respectively. There were no statistically significant differences between the observers’ diagnostic performances in any of the combinations (P = 0.703, 0.067, and 0.972, respectively).

Conclusion

In the evaluation and further assessment of breast lesions, semiquantitative strain ratio calculation may help improve diagnostic accuracy by reducing interpretational variety, when used together with B-mode ultrasonography and elasticity scoring, especially for inexperienced individuals.

Development of a Deep Learning-Based Model for Diagnosing Breast Nodules With Ultrasound

OBJECTIVES

Artificial intelligence (AI) has been an important addition to medicine. We aimed to explore the use of deep learning (DL) to distinguish benign from malignant lesions with breast ultrasound (BUS).

METHODS

The DL model was trained with BUS nodule data using a standard protocol (1271 malignant nodules, 1053 benign nodules, and 2144 images of the contralateral normal breast). The model was tested with 692 images of 256 breast nodules. We used the accuracy, precision, recall, harmonic mean of recall and precision, and mean average precision as the indices to assess the DL model. We used 100 BUS images to evaluate differences in diagnostic accuracy among the AI system, experts (>25 years of experience), and physicians with varying levels of experience. A receiver operating characteristic curve was generated to evaluate the accuracy for distinguishing between benign and malignant breast nodules.

RESULTS

The DL model showed 73.3% sensitivity and 94.9% specificity for the diagnosis of benign versus malignant breast nodules (area under the curve, 0.943). No significant difference in diagnostic ability was found between the AI system and the expert group (P = .951), although the physicians with lower levels of experience showed significant differences from the AI and expert groups (P = .01 and .03, respectively).

CONCLUSIONS

Deep learning could distinguish between benign and malignant breast nodules with BUS. On BUS images, DL achieved diagnostic accuracy equivalent to that of expert physicians.

Optical coherence tomography versus intravascular ultrasound in patients with myocardial infarction: a diagnostic performance study of pre-percutaneous coronary interventions

Accurate coronary measurements are important in guiding percutaneous coronary intervention. Intravascular ultrasound is a widely accepted diagnostic modality for coronary measurement before percutaneous coronary intervention. The spatial resolution of optical coherence tomography is 10 times larger than that of intravascular ultrasound. The objective of the study was to compare quantitative and qualitative parameters of frequency domain optical coherence tomography (FDOCT) with those of intravascular ultrasound and coronary angiography in patients with acute myocardial infarction. Diagnostic parameters of coronary angiography, intravascular ultrasound, and FDOCT of 250 patients with coronary artery disease who required admission diagnosis were included in the analyses. Minimum lumen diameter detected by FDOCT was larger than that detected by quantitative coronary angiography (2.11±0.1 vs 1.89±0.09 mm, P<0.0001, q=34.67) but smaller than that detected by intravascular ultrasound (2.11±0.1 vs 2.19±0.11 mm, P<0.0001, q=12.61). Minimum lumen area detected by FDOCT was smaller than that detected by intravascular ultrasound (3.41±0.01 vs 3.69±0.01 mm², P<0.0001). FDOCT detected higher numbers of thrombus, tissue protrusion, dissection, and incomplete stent apposition than those detected by intravascular ultrasound (P<0.0001 for all). More accurate and sensitive results of the coronary lumen can be detected by FDOCT than coronary angiography and intravascular ultrasound (level of evidence: III).

A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow

Ultrasound is the most commonly used imaging modality in clinical practice because it is a nonionizing, low-cost, and portable point-of-care imaging tool that provides real-time images. Artificial intelligence (AI)-powered ultrasound is becoming more mature and getting closer to routine clinical applications in recent times because of an increased need for efficient and objective acquisition and evaluation of ultrasound images. Because ultrasound images involve operator-, patient-, and scanner-dependent variations, the adaptation of classical machine learning methods to clinical applications becomes challenging. With their self-learning ability, deep-learning (DL) methods are able to harness exponentially growing graphics processing unit computing power to identify abstract and complex imaging features. This has given rise to tremendous opportunities such as providing robust and generalizable AI models for improving image acquisition, real-time assessment of image quality, objective diagnosis and detection of diseases, and optimizing ultrasound clinical workflow. In this report, the authors review current DL approaches and research directions in rapidly advancing ultrasound technology and present their outlook on future directions and trends for DL techniques to further improve diagnosis, reduce health care cost, and optimize ultrasound clinical workflow.

Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study

Background: Single-center trials demonstrated moderate-substantial level of interobserver agreement in the evaluation of ultrasound (US) features of thyroid nodules. Multicenter studies on US agreement, however, are scanty, and data on intraobserver agreement are poor. Aim of the study was to assess inter- and intraobserver agreement between different thyroid centers and different specialists. Methods: A blinded analysis of 100 electronically recorded thyroid nodule US images was conducted in three large-volume thyroid centers by seven radiologists and endocrinologists. The evaluation was repeated after randomization 4 months later. The following US characteristics were evaluated: composition, echogenicity, margins, intranodular echogenic spots, vascularity, and shape. Thyroid nodules were also classified according to AACE/ACE/AME, EU-TIRADS, ATA, and ACR-TIRADS US classifications. Intra- and interobserver agreement was calculated using cross-tabulation expressed as mean Cohen's Kappa. Results: Interobserver agreement for US features: K-coefficient was 0.53 for composition, 0.47 for echogenicity, 0.46 for intranodular vascularity, and 0.33 for margins of the nodules. For echogenic foci, the K-coefficient was 0.47 for microcalcifications, 0.38 for macrocalcifications, 0.11 for the subcategory comet-tail artifacts, and 0.42 for shape. Operators resulted uncertain on hyperechoic foci definition in 16% of cases and described them as "hyperechoic foci of uncertain significance." Interobserver Cohen-K for US classification systems was 0.44 for AACE, 0.42 for ACR-TIRADS, 0.39 EU-TIRADS, and 0.34 for ATA. Intraobserver agreement: the K-coefficient for nodule US features was 0.62 for intranodular vascularity, 0.58 for composition, 0.60 for echogenicity, 0.54 for macrocalcifications, 0.55 for microcalcifications, 0.47 for comet tails, 0.39 for margins, and 0.35 for shape. Intraobserver Cohen-K for US classification systems was 0.54 for AACE, 0.49 for ACR-TIRADS, 0.38 for ATA, and 0.33 for EU-TIRADS. Conclusions: Intraobserver reproducibility for thyroid nodule US reporting and US classification systems appears fairly adequate, while the interobserver agreement between different centers is lower than that assessed in single-center trials. Reporting and rating ability of thyroid US examiners still appear not consistent. An unified lexicon of thyroid US features, a simplified method of classification, and a dedicated training in the description of thyroid US findings may increase the observers' agreement and the predictive value of US classification systems in real world practice.

Increased Marginal Stiffness Differentiates Infiltrative From Noninfiltrative Cutaneous Basal Cell Carcinomas in the Facial Area: A Prospective Study

OBJECTIVES

Basal cell carcinoma (BCC) is the most common dermatologic malignant skin cancer. Infiltrative histologic variants are more aggressive and require wider surgical margins or Mohs surgery, in contrast with noninfiltrative variants, which are commonly treated with standard surgical excision. Elastography has not been used to date to differentiate between the histologic variants of BCC. The purpose of this study was to differentiate infiltrative from noninfiltrative BCCs on elastography.

METHODS

A total of 31 facial BCCs were studied. Preoperatively, color Doppler ultrasound and strain elastographic examinations of the lesions were performed. The size, intralesional vascularization, and presence of hyperechoic dots were considered relevant B-mode and color Doppler variables. Strain ratios of the tumors were obtained with respect to adjacent healthy tissue. Increased marginal stiffness, which was considered hardness in greater than 50% of the tumor margin, was also blindly evaluated. Histologic confirmation and subtyping (infiltrative or noninfiltrative) were performed in all cases.

RESULTS

Infiltrative BCCs did not differ in the size, presence or absence of hyperechoic dots, or vascularization from noninfiltrative BCCs. Strain ratios were similar in both infiltrative and noninfiltrative BCCs (mean ± SD, 1.82 ± 0.879 versus 2.2 ± 1.11). However, infiltrative BCCs had statistically increased marginal stiffness in comparison with noninfiltrative BCCs (88.0% versus 18.8%). Increased marginal stiffness had sensitivity and specificity of 0.89 and 0.82 respectively, with a positive predictive value of 0.67 for infiltrative BCCs and a negative predictive value of 0.95 for noninfiltrative BCCs.

CONCLUSIONS

Histologic variants of BCC have different elastographic patterns. These differences may be of help in preoperative assessments of the BCC subtype and specific surgical planning, avoiding unnecessary skin biopsies.

Breast sonoelastography: Now and in the future

PURPOSE

The purpose of this study was to identify practice trends and opinions concerning breast sonoelastography in two different health care systems, one in Europe (France) and the other in North America (Province of Québec/Canada).

MATERIALS AND METHODS

We distributed an 11-item online survey among French and Canadian breast radiologists. The survey comprised of four sections: (i) personal practice characteristics, (ii) breast sonoelastography usage in daily practice and evaluation of its usefulness, (iii) limitations and roles of sonoelastogrpahy in their clinical practice, and (iv) types of elastographic technique and interpretation.

RESULTS

We found that sonoelastography of the breast appears unpopular among Canadian radiologists, and poorly credible among French radiologists, who perceive it as an unreliable technique. To date, its real impact in clinical practice remains uncertain.

CONCLUSION

Continued learning and awareness of the indications, advantages and limitations of breast sonoelastography may motivate breast radiologists to adopt its use.