Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions

Associated factors leading to misdiagnosis of a combined diagnostic model of different types of strain imaging and conventional ultrasound in evaluation of breast lesions: Selection strategy for using different types of strain imaging in evaluation of breast lesions

OBJECTIVE

To evaluate the effectiveness of a decision tree that integrates conventional ultrasound (CUS) with two different strain imaging (SI) techniques for diagnosing breast lesions, and to analyze the factors contributing to false negative (FN) and false positive (FP) in the decision tree's outcomes.

MATERIALS AND METHODS

Imaging and clinical data of 796 cases in the training set and 351 cases in the validation set were prospectively collected. A decision tree model that combines two types of SI and CUS was constructed, and its diagnostic performance was analyzed. Univariate analysis and multivariate analysis were applied to identify independent risk factors associated with FP and FN results of the decision tree model.

RESULTS

Size, shape, margin, vascularity, the types of internal calcifications, EI score and VTI pattern were found to be significantly independently associated with the diagnosis of benign and malignant breast lesions. Therefore, size, shape, margin, vascularity, EI score and VTI pattern were used to construct decision tree models. The Tree (EI+VTI) model had the highest AUC. Both in the training and validation groups, the AUC of Tree (EI+VTI) was significantly higher compared with that of EI, VTI, and BI-RADS (all, P < 0.05). Orientation, posterior acoustic features and the types of internal calcifications were significantly positively associated with misdiagnosis results of Tree (EI+VTI) in evaluation of breast lesions (all P < 0.05).

CONCLUSION

The diagnostic model based on a decision tree that integrates two distinct types of SI with CUS enhances the diagnostic accuracy of each method when used individually. This integration lowers the misdiagnosis rate, potentially assisting radiologists in more effective lesion assessments. When applying the decision tree model, attention should be paid to the orientation, posterior acoustic features, and the types of internal calcifications of the lesions.

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

BACKGROUND

Female breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging.

OBJECTIVE

The purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC.

METHODS

302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray-scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad-score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model.

RESULTS

When we selected 10 radiomics features to calculate the rad-score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value.

CONCLUSION

The radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

Impact of Imaging Biomarkers and AI on Breast Cancer Management: A Brief Review

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists' proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists' capabilities and ameliorating patient outcomes in the realm of breast cancer management.

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study

OBJECTIVES

To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously.

METHODS

This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists.

RESULTS

In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists.

CONCLUSIONS

The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis.

KEY POINTS

• The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.

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.

Can Pre-biopsy Second-Look Breast Ultrasound Affect Clinical Management? Experience From a Single Tertiary Hospital

Objectives

Interpretation discrepancy is a major disadvantage of breast imaging. This study aimed to determine the clinical benefit of the pre-biopsy second-look breast ultrasound (US).

Methods

Patients with suspicious breast masses referred to our tertiary hospital for US-guided breast biopsy were retrospectively reviewed between August 2017 and November 2019. Here, second-look assessments were performed by experienced specialized breast radiologists via performing a bilateral breast US scan plus reviewing former imaging studies, and results were compared with the initial assessment. Interpretation changes in terms of biopsy recommendation and surgical management (i.e., lumpectomy to mastectomy) were analyzed.

Results

A total of 537 patients were enrolled in this study. Interpretation discrepancies occurred in 109 patients (20%; 95% CI, 17%–24%). Among them, there were 84 patients (16%; 95% CI, 13%–19%) whose masses were re-classified as BI-RADS 3 by the second-look US and underwent 2-year follow-up, showing 82 benign, 1 malignant, and 1 high-risk lesions. On the other hand, 16 patients (3%; 95% CI, 2%–5%) undertook biopsy at an additional site, identifying 10 new malignant lesions, 3 high-risk lesions, and 3 benign lesions, resulting in surgical management changes in 12 patients. In addition, nine (2%; 95% CI, 1%–3%) patients received discrepant disease ranges, which also altered surgical management. Overall, 21 patients (4%; 95% CI, 3%–6%) got their surgical management altered by the second-look US.

Conclusion

Pre-biopsy second-look assessment of breast US can reduce unnecessary biopsies in 16% of patients and alter surgical management in 4% of patients, suggesting it is a practical and valuable method for patient care improvement.

Deep learning prediction of axillary lymph node status using ultrasound images

OBJECTIVE

To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images.

METHODS

In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not).

RESULTS

Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS).

CONCLUSION

It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with 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.

Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study

Background

Screening of breast cancer in asymptomatic women is important to evaluate for early diagnosis. In China ultrasound is a more frequently used method than mammography for the detection of breast cancer. The objectives of the study were to provide evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women.

Methods

Breast ultrasound examinations including the parenchymatous pattern of cytopathological confirmed breast cancer (n = 541) and age-matched cytopathological not confirmed breast cancer (n = 849) women were retrospectively reviewed by seven sonographer physicians. According to compositions of ducts, the thickness of the breast, diameter of ducts, fat lobules, and fibro glandular tissues, the breast parenchymatous pattern was categorized into heterogeneous (high percentage of fatty tissues), ductal (the inner diameters of ducts > 50% of the thick mass of the breast), mixed (the inner diameters of ducts was 50% of the thick mass of the breast), and fibrous categories (a dense classification of the breast).

Results

Heterogeneous (p < 0.0001, OR = 3.972) and fibrous categories (p < 0.0001, OR = 2.702) were higher among women who have cytopathological confirmed breast cancer than those who have not cytopathological confirmed breast cancer. The heterogeneous category was high-risk ultrasonographic examination category followed by the fibrous category. Agreements between sonographer physicians for categories of ultrasonic examinations were fair to good (Cohen’s k = 0.591).

Conclusions

Breast cancer risk in Chinese asymptomatic women differ according to the ultrasonographic breast parenchymal pattern.

Level of Evidence: III.

Technical efficacy stage: 2.

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 =.

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.

Evaluating the usefulness of breast strain elastography for intraductal lesions

PURPOSE

Strain elastography for imaging lesion stiffness is being used as a diagnostic aid in the malignant/benign discrimination of breast diseases. While acquiring elastography in addition to B-mode images has been reported to help avoid performing unnecessary biopsies, intraductal lesions are difficult to discriminate whether they are malignant or benign using elastography. An objective evaluation of strain in lesions was performed in this study by measuring the elasticity index (E-index) and elasticity ratio (E-ratio) of lesions as semi-quantitative numerical indicators of the color distribution of strain. We examined whether ductal carcinoma in situ (DCIS) and intraductal papilloma could be distinguished using these semi-quantitative numerical indicators.

METHODS

In this study, 170 ultrasonographically detected mass lesions in 162 cases (106 malignant lesions and 64 benign lesions)-in which tissue biopsy by core needle biopsy and vacuum-assisted biopsy, or surgically performed histopathological diagnosis, was performed-were selected as subjects from among 1978 consecutive cases (from January 2014 to December 2016) in which strain elastography images were acquired, in addition to standard B-mode breast ultrasonography, by measuring the E-index and E-ratio.

RESULTS

The cut-off values for E-index and E-ratio in the malignant/benign discrimination of breast lesions were determined to be optimal values at 3.5 and 4.2, respectively, based on receiver operating characteristic (ROC) curve analysis. E-index sensitivity, specificity, accuracy, and AUC value (area under the curve) were 85%, 86%, 85%, and 0.860, respectively, while those for E-ratio were 78%, 74%, 74%, and 0.780, respectively. E-index yielded superior results in all aspects of sensitivity, specificity, accuracy, and AUC values, compared to those of E-ratio. The mean E-index values for malignant tumors and benign tumors were 4.46 and 2.63, respectively, indicating a significant difference (P < 0.001). E-index values of 24 DCIS lesions and 25 intraductal papillomas were 3.88 and 3.35, respectively, which showed a considerably close value, while the false-negative rate for DCIS was 29.2%, and the false-positive rate for intraductal papilloma was as high as 32.0%.

CONCLUSION

E-index in strain elastography yielded better results than E-ratio in the malignant/benign discrimination of breast diseases. On the other hand, E-index has a high false-negative rate and false-positive rate for intraductal lesions, a factor which should be taken into account when making ultrasound diagnoses.

A Cross-Sectional Observational Study to Compare the Role of Ultrasound with Mammography in Women Identified at High Risk for Breast Cancer in a Population in China

Background

Currently, there is no national breast cancer screening program in China. In countries that have screening programs, screening mammography is used. This study aimed to compare the imaging parameters and diagnostic findings between ultrasound and mammography in women at high risk who had a histologically confirmed diagnosis of breast cancer in a population in China.

Material/Methods

A cross-sectional observational study included 1,687 women with a risk score of ≥30, according to the cancer risk assessment model, who underwent breast ultrasound and mammography. Women who had a Breast Imaging-Reporting and Data System (BI-RADS) score of 4 or 5 were identified, and 155 women had breast cancer confirmed by breast biopsy and histology. The ultrasound and mammography findings were evaluated and compared.

Results

Breast ultrasound resulted in significantly fewer inconclusive results (BI-RADS score, 0), when compared with mammography (p=0.046). In cases with a histologically confirmed diagnosis of breast cancer (BI-RADS score, 4), the diagnostic sensitivity of breast ultrasound and mammography were 0.989 and 0.859, respectively. In cases with a histologically confirmed diagnosis of breast cancer (BI-RADS score, 5), the diagnostic sensitivity of breast ultrasound and mammography were 1.000 and 0.984, respectively. In cases with a histologically confirmed diagnosis of benign breast lesions (BI-RADS score, 2), there was no significant difference between breast ultrasound and mammography.

Conclusions

In a population of women in China, breast ultrasound was a more sensitive diagnostic imaging method for women with high risk BI-RADS 4 and 5 breast lesions.

Reducing the number of unnecessary biopsies of US-BI-RADS 4a lesions through a deep learning method for residents-in-training: a cross-sectional study

OBJECTIVE

The aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.

METHODS

The study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.

PARTICIPANTS

A total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.

RESULTS

S-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%-48.67%, AUC: 0.62-0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%-76.11%, AUC: 0.71-0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).

CONCLUSIONS

With the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.

S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors

BACKGROUND

To assess inter-reader agreement for US BI-RADS descriptors using S-Detect: a computer-guided decision-making software assisting in US morphologic analysis.

METHODS

73 solid focal breast lesions (FBLs) (mean size: 15.9 mm) in 73 consecutive women (mean age: 51 years) detected at US were randomly and independently assessed according to the BI-RADS US lexicon, without and with S-Detect, by five independent reviewers. US-guided core-biopsy and 24-month follow-up were considered as standard of reference. Kappa statistics were calculated to assess inter-operator agreement, between the baseline and after S-Detect evaluation. Agreement was graded as poor (≤ 0.20), moderate (0.21-0.40), fair (0.41-0.60), good (0.61-0.80), or very good (0.81-1.00).

RESULTS

33/73 (45.2%) FBLs were malignant and 40/73 (54.8%) FBLs were benign. A statistically significant improvement of inter-reader agreement from fair to good with the use of S-Detect was observed for shape (from 0.421 to 0.612) and orientation (from 0.417 to 0.7) (p < 0.0001) and from moderate to fair for margin (from 0.204 to 0.482) and posterior features (from 0.286 to 0.522) (p < 0.0001). At baseline analysis isoechoic (0.0485) and heterogeneous (0.1978) echo pattern, microlobulated (0.1161) angular (0.1204) and spiculated (0.1692) margins and combined pattern (0.1549) for posterior features showed the worst agreement rate (poor). After S-Detect evaluation, all variables but isoechoic pattern showed an agreement class upgrade with a statistically significant improvement of inter-reader agreement (p < 0.0001).

CONCLUSIONS

S-Detect significantly improved inter-reader agreement in the assessment of FBLs according to the BI-RADS US lexicon but evaluation of margin and echo pattern needs to be further improved, particularly isoechoic pattern.

Similar Reproducibility for Strain and Shear Wave Elastography in Breast Mass Evaluation: A Prospective Study Using the Same Ultrasound System

The objective of this study was to evaluate the inter-operator reproducibility of strain elastography (SE) and shear wave elastography (SWE) in three groups: all lesions, benign lesions and malignant lesions. Ninety-one lesions from ninety-one women were examined by SE and SWE from January 2017 to December 2017 by two radiologists. The reproducibility of elastic score, SE strain ratio and SWE Young's modulus between operators was prospectively evaluated. There was good agreement on elasticity score, with κ values of 0.711, 0.640 and 0.766. The intra-class correlation coefficients of the strain ratio, mean elastic modulus (E_mean), maximum elastic modulus (E_max) and elastic modulus standard deviation (E_sd) ranged from 0.723-0.876, which indicated good and excellent agreement. We concluded that both SE and SWE had good reproducibility among different operators using the same probe in the same ultrasound instrument. Strain elasticity score was more consistent among operators in malignant breast tumors. There was better agreement on strain elastic ratio and shear wave elasticity among operators in benign breast lesions.

Conventional US and 2-D Shear Wave Elastography of Virtual Touch Tissue Imaging Quantification: Correlation with Immunohistochemical Subtypes of Breast Cancer

Our study aimed to investigate the correlation of the imaging features obtained using conventional ultrasound (US) and elastography (conventional strain elastography of elasticity imaging [EI], virtual touch tissue imaging [VTI] and 2-D shear wave elastography [2-D-SWE] of virtual touch tissue imaging quantification [VTIQ]) with the clinicopathologic features and immunohistochemical (IHC) subtypes of breast cancer. The sample consisted of images from 202 patients with 206 breast lesions that were confirmed as breast cancers. Lesions with HER2 overexpression (luminal B HER2+ or HER2+) had higher mean shear wave velocity (SWV) values than the others. Older patients, lower histologic grade, no lymphovascular invasion and no lymph node metastasis were associated with luminal A (p < 0.001). There were significant differences in SWV values, histologic grade and lymph node status among the different pathologic types. This association may allow the use of 2-D-SWE in the pre-operative prediction of tumor characteristics and biologic activity, which may determine the prognosis in a non-invasive manner.

Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China

Objective

To investigate the feasibility of a CAD system S-detect on a database from a single Chinese medical center.

Materials and methods

An experienced radiologist performed breast US examinations and made assessments of 266 consecutive breast lesions in 227 patients. S-detect classified the lesions automatically in a dichotomous form. An in-training resident who was blind to both the US diagnostic results and histological results reviewed the images afterward. The final histological results were considered as the diagnostic gold standard. The diagnostic performances and interrater agreements were analyzed.

Results

A total of 266 focal breast lesions (161 benign lesions and 105 malignant lesions) were assessed in this study. S-detect had a lower sensitivity (87.07%) and a higher specificity (72.27%) compared with the experienced radiologist (sensitivity 98.1% and specificity 65.43%). The sensitivity and specificity of S-detect were better than that of the resident (sensitivity 82.86% and specificity 68.94%). The AUC value of S-detect (0.807) showed no significant difference with the experienced radiologist (0.817) and was higher than that of the resident (0.758). S-detect had moderate agreement with the experienced radiologist.

Conclusion

In this single-center study, a high level of diagnostic performance of S-detect on 266 breast lesions of Chinese women was observed. S-detect had almost equal diagnostic capacity with an experienced radiologist and performed better than a novice reader. S-detect was also distinguished for its high specificity. These results supported the feasibility of S-detect in aiding the diagnosis of breast lesions on an independent database.

Comparison of Two Different Ultrasound Devices Using Strain Elastography Technology in the Diagnosis of Breast Lesions Related to the Histologic Results

This study was conducted to provide evidence that elastograms of two different devices and different manufacturers using the same technical approach provide the same diagnoses. A total of 110 breast lesions were prospectively analysed by two experts in ultrasound, using the strain elastography function from two different manufacturers (Hitachi HI-RTE, Hitachi Medical Systems, Wiesbaden, Germany; and Siemens eSie Touch, Siemens Medical Systems, Erlangen, Germany). Results were compared with the histopathologic results. Applying the Bowker test of symmetry, no statistically significant difference between the two elastography functions of these two devices was found (p = 0.120). The Cohen's kappa of k = 0.591 showed moderate strength of agreement between the two elastograms. The two examiners yielded moderate strength of agreement analysing the elastograms (Hitachi HI-RTE, k = 0.478; Siemens eSie Touch, k = 0.441). In conclusion, evidence is provided that elastograms of the same lesion generated by two different ultrasound devices equipped with a strain elastography function do not significantly differ.

Comparison of strain and shear wave elastography for qualitative and quantitative assessment of breast masses in the same population

We investigated addition of strain and shear wave elastography to conventional ultrasonography for the qualitative and quantitative assessment of breast masses; cut-off points were determined for strain ratio, elasticity ratio, and visual score for differentiating between benign and malignant masses. In all, 108 masses from 94 patients were evaluated with strain and shear wave elastography and scored for suspicion of malignancy, visual score, strain ratio, and elasticity ratio. The diagnostic performance between ultrasonography alone and ultrasonography combined with either type of elastography was compared; cut-off points were determined for strain ratio, elasticity ratio, and visual score. Of the 108 masses, 44 were malignant and 64 were benign. The areas under the curves were significantly higher for strain and shear wave elastography-supplemented ultrasonography (0.839 and 0.826, respectively; P = 0.656) than for ultrasonography alone (0.764; P = 0.018 and 0.035, respectively). The diagnostic performances of strain and elasticity ratios were similar when differentiating benign from malignant masses. Cut-off values for strain ratio, elasticity ratio, and visual scores for strain and shear wave elastography were 2.93, 4, 3, and 2, respectively. Both forms of elastography similarly improved the diagnostic performance of conventional ultrasonography in the qualitative and quantitative assessment of breast masses.

A Simple Ultrasound Based Classification Algorithm Allows Differentiation of Benign from Malignant Breast Lesions by Using Only Quantitative Parameters

Purpose

We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not.

Procedures

One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse–ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWV_max, SWV_interm, and SWV_min) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm.

Results

Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWV_max), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWV_max ≤ or > 3.16; if SWV_max ≤ 3.16 then RI ≤ 0.66, 0.66–0.77 or > 0.77; if RI ≤ 0.66 then SWV_max ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818–0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7–100 %), and a specificity of 61.02 % (95 % CI 47.4–73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases.

Conclusions

A simple classification algorithm incorporating two quantitative US parameters (SWV_max and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias.

Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography

A radiomics approach to sonoelastography, called "sonoelastomics," is proposed for classification of benign and malignant breast tumors. From sonoelastograms of breast tumors, a high-throughput 364-dimensional feature set was calculated consisting of shape features, intensity statistics, gray-level co-occurrence matrix texture features and contourlet texture features, which quantified the shape, hardness and hardness heterogeneity of a tumor. The high-throughput features were then selected for feature reduction using hierarchical clustering and three-feature selection metrics. For a data set containing 42 malignant and 75 benign tumors from 117 patients, seven selected sonoelastomic features achieved an area under the receiver operating characteristic curve of 0.917, an accuracy of 88.0%, a sensitivity of 85.7% and a specificity of 89.3% in a validation set via the leave-one-out cross-validation, revealing superiority over the principal component analysis, deep polynomial networks and manually selected features. The sonoelastomic features are valuable in breast tumor differentiation.

Inter-reader Variability in the Use of BI-RADS Descriptors for Suspicious Findings on Diagnostic Mammography: A Multi-institution Study of 10 Academic Radiologists

RATIONALE AND OBJECTIVES

The study aimed to determine the inter-observer agreement among academic breast radiologists when using the Breast Imaging Reporting and Data System (BI-RADS) lesion descriptors for suspicious findings on diagnostic mammography.

MATERIALS AND METHODS

Ten experienced academic breast radiologists across five medical centers independently reviewed 250 de-identified diagnostic mammographic cases that were previously assessed as BI-RADS 4 or 5 with subsequent pathologic diagnosis by percutaneous or surgical biopsy. Each radiologist assessed the presence of the following suspicious mammographic findings: mass, asymmetry (one view), focal asymmetry (two views), architectural distortion, and calcifications. For any identified calcifications, the radiologist also described the morphology and distribution. Inter-observer agreement was determined with Fleiss kappa statistic. Agreement was also calculated by years of experience.

RESULTS

Of the 250 lesions, 156 (62%) were benign and 94 (38%) were malignant. Agreement among the 10 readers was strongest for recognizing the presence of calcifications (k = 0.82). There was substantial agreement among the readers for the identification of a mass (k = 0.67), whereas agreement was fair for the presence of a focal asymmetry (k = 0.21) or architectural distortion (k = 0.28). Agreement for asymmetries (one view) was slight (k = 0.09). Among the categories of calcification morphology and distribution, reader agreement was moderate (k = 0.51 and k = 0.60, respectively). Readers with more experience (10 or more years in clinical practice) did not demonstrate higher levels of agreement compared to those with less experience.

CONCLUSIONS

Strength of agreement varies widely for different types of mammographic findings, even among dedicated academic breast radiologists. More subtle findings such as asymmetries and architectural distortion demonstrated the weakest agreement. Studies that seek to evaluate the predictive value of certain mammographic features for malignancy should take into consideration the inherent interpretive variability for these findings.

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

Our aim was to prospectively evaluate inter- and intra-observer agreement between Breast Imaging Reporting and Data System (BI-RADS) classifications and Tsukuba elasticity scores (TSs) of breast lesions. The study included 164 breast lesions (63 malignant, 101 benign). The BI-RADS classification and TS of each breast lesion was assessed by the examiner and twice by three reviewers at an interval of 2 months. Weighted κ values for inter-observer agreement ranged from moderate to substantial for BI-RADS classification (κ = 0.585-0.738) and was substantial for TS (κ = 0.608-0.779). Intra-observer agreement was almost perfect for ultrasound (US) BI-RADS (κ = 0.847-0.872) and TS (κ = 0.879-0.914). Overall, individual reviewers are highly self-consistent (almost perfect intra-observer agreement) with respect to BI-RADS classification and TS, whereas inter-observer agreement was moderate to substantial. Comprehensive training is essential for achieving high agreement and minimizing the impact of subjectivity. Our results indicate that breast US and real-time elastography can achieve high diagnostic performance.

Superb Microvascular Imaging-A new vascular detecting ultrasonographic technique for avascular breast masses: A preliminary study

OBJECTIVES

Superb Microvascular Imaging (SMI) is a new vascular imaging technique detecting a slower velocity that color Doppler flow image (CDFI) cannot. The aim of this study is to evaluate the clinical value of SMI for detecting penetrating vessels (PVs) in avascular breast lesions.

METHODS

Seventy-nine patients with 82 breast lesions were examined by conventional ultrasound and diagnosed as Breast Imaging Reporting and Data System (BI-RADS) level 3 or 4. CDFI detected no PVs; subsequently, Power Doppler (PD), Advanced Dynamic Flow (ADF), and SMI were performed to detect any PVs in the breast lesions.

RESULTS

Compared with PD or ADF, SMI revealed significantly (P<0.01) higher median numbers of PVs in breast lesions. The area under the receiver operating characteristic curve was 0.914 before the corrected classification versus 0.947 after the corrected classification (P<0.05).

CONCLUSIONS

SMI was helpful in the differential diagnosis of benign versus malignant in avascular breast lesions, especially those in BI-RADS category 4.

EUS-derived criteria for distinguishing benign from malignant metastatic solid hepatic masses

BACKGROUND

Detection of hepatic metastases during EUS is an important component of tumor staging.

OBJECTIVE

To describe our experience with EUS-guided FNA (EUS-FNA) of solid hepatic masses and derive and validate criteria to help distinguish between benign and malignant hepatic masses.

DESIGN

Retrospective study, survey.

SETTING

Single, tertiary-care referral center.

PATIENTS

Medical records were reviewed for all patients undergoing EUS-FNA of solid hepatic masses over a 12-year period.

INTERVENTIONS

EUS-FNA of solid hepatic masses.

MAIN OUTCOME MEASUREMENTS

Masses were deemed benign or malignant according to predetermined criteria. EUS images from 200 patients were used to create derivation and validation cohorts of 100 cases each, matched by cytopathologic diagnosis. Ten expert endosonographers blindly rated 15 initial endosonographic features of each of the 100 images in the derivation cohort. These data were used to derive an EUS scoring system that was then validated by using the validation cohort by the expert endosonographer with the highest diagnostic accuracy.

RESULTS

A total of 332 patients underwent EUS-FNA of a hepatic mass. Interobserver agreement regarding the initial endosonographic features among the expert endosonographers was fair to moderate, with a mean diagnostic accuracy of 73% (standard deviation 5.6). A scoring system incorporating 7 EUS features was developed to distinguish benign from malignant hepatic masses by using the derivation cohort with an area under the receiver operating curve (AUC) of 0.92; when applied to the validation cohort, performance was similar (AUC 0.86). The combined positive predictive value of both cohorts was 88%.

LIMITATIONS

Single center, retrospective, only one expert endosonographer deriving and validating the EUS criteria.

CONCLUSION

An EUS scoring system was developed that helps distinguish benign from malignant hepatic masses. Further study is required to determine the impact of these EUS criteria among endosonographers of all experience.