Characterization of solid breast masses: use of the sonographic breast imaging reporting and data system lexicon

Association of clinicopathologic and sonographic features with stromal tumor-infiltrating lymphocytes in triple-negative breast cancer

BACKGROUND

Increased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC.

METHODS

This study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher's exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level.

RESULTS

The 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002).

CONCLUSIONS

This preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.

Role of ultrasound-guided vacuum-assisted breast biopsy in the management of radiologic-pathologic discordance: a retrospective single-centre study

PURPOSE

To evaluate the efficacy of US-guided vacuum-assisted biopsy (US-VAB) in radiologic-pathologic (rad-path) discordance in women with suspicious breast lesions.

METHODS

Two thousand three hundred and sixty patients with 2385 BI-RADS category 4 and 5 lesions underwent percutaneous US-guided CNB. Thirty-six lesions were classified as discordant benign and underwent second-line US-VAB. A 14-gauge needle was utilized for CNB and 10-gauge for US-VAB. Final pathology was the reference standard for women who underwent surgery, imaging follow-up in other cases. Rates of malignancy for US-VAB and subsequent surgery were evaluated. Lesions with upgrade and no upgrade to second-line VAB were compared in terms of patient's age, lesion type and characteristics, size and BI-RADS category. Positive predictive value (PPV), negative predictive value (NPV) for BI-RADS categories and diagnostic performance for second-line US-VAB were calculated. p value < 0.05 was considered statistically significant (t-test, Mann-Whitney, χ2).

RESULTS

US-VAB identified 10 B2, 9 B3 and 17 B5 lesions with upgrade to malignancy of 47.2% (17/36). There were 8 invasive no special type, 7 ductal in situ, 1 invasive lobular carcinoma and 1 angiosarcoma, and their distribution among BI-RADS categories was: 2/2 in BI-RADS 5 (100%), 12/18 in BI-RADS 4C (67%) and 3/16 in BI-RADS 4B lesions (19%) (p = 0.006). Of the remaining 19 lesions, 6 underwent surgery and 2 were upgraded to ductal carcinoma in situ; 13 underwent radiological follow-up and one resulted malignant. False-negative rate for US-VAB was 15.8% (3/19) with final upgrade to malignancy of 55% (20/36). The univariate analysis revealed mass shape (p = 0.008) and BI-RADS categories (p = 0.006) to be associated with upgrade to malignancy. Sensitivity, specificity, PPV, NPV and accuracy for US-VAB were 85, 100, 100, 84 and 92%, respectively.

CONCLUSIONS

US-VAB identified almost 50% of cancers missed by CNB, avoiding surgical biopsies and validating as an effective mini-invasive approach in rad-path discordance.

Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network

OBJECTIVES

To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions.

METHODS

Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed.

RESULTS

1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%.

CONCLUSION

DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

Factors Associated with False Positive Breast Cancer Results in the Real-Time Sonoelastography Evaluation of Solid Breast Lesions

Background and Objectives: Breast cancer is one of the most widespread cancers among the female population around the world and is curable if diagnosed in an early stage. Consequently, breast cancer screening imaging techniques have greatly evolved and adjusted over the last decades. Alongside mammography, sonoelastography became an important tool for breast cancer detection. However, sonoelastography still has its limitations, namely, there is still a high occurrence of false positive results in the BIRADS 4 category. The aim of our study is to identify potential false positive predictors and to ascertain the factors influencing the quality of strain ultrasound elastography for the evaluation of suspicious solid breast lesions categorized as BIRADS 4B, 4C, and 5. Materials and Methods: We conducted a retrospective study in a single private medical center in Timisoara between January 2017 and January 2022 analyzing 1625 solid breast lesions by the sonoelastography strain using a standardized BIRADS-US lexicon. Results: Our study showed that most sonoelastography factors linked to incorrect and overdiagnosis were due to a nodule dimension (OR = 1.02 per unit increase), posterior acoustic shadowing (OR = 12.26), reactive adenopathy (OR = 6.35), and an increased TES score (TES3 OR = 6.60; TES4 OR = 23.02; TES5 OR = 108.24). Regarding patient characteristics, age (OR = 1.09 per unit increase), BMI, (OR = 1.09 per unit increase), and breastfeeding history (OR = 3.00) were observed to increase the likelihood of false positive results. On the other hand, the nodules less likely to be part of the false positive group exhibited the following characteristics: a regular shape (OR = 0.27), homogenous consistency (OR = 0.42), and avascularity (OR = 0.22). Conclusions: Older age, high BMI, patients with a breastfeeding history, and those who exhibit the following specific nodule characteristics were most often linked to false positive results: large tumors with posterior acoustic shadowing and high elasticity scores, accompanied by reactive adenopathy. On the other hand, homogenous, avascular nodules with regular shapes were less likely to be misdiagnosed.

Clinical and radiomics integrated nomogram for preoperative prediction of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer

Objectives

The present study aimed to develop a radiomics nomogram based on conventional ultrasound (CUS) to preoperatively distinguish high tumor-infiltrating lymphocytes (TILs) and low TILs in triple-negative breast cancer (TNBC) patients.

Methods

In the present study, 145 TNBC patients were retrospectively included. Pathological evaluation of TILs in the hematoxylin and eosin sections was set as the gold standard. The patients were randomly allocated into training dataset and validation dataset with a ratio of 7:3. Clinical features (age and CUS features) and radiomics features were collected. Then, the Rad-score model was constructed after the radiomics feature selection. The clinical features model and clinical features plus Rad-score (Clin+RS) model were built using logistic regression analysis. Furthermore, the performance of the models was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Univariate analysis and LASSO regression were employed to identify a subset of 25 radiomics features from a pool of 837 radiomics features, followed by the calculation of Rad-score. The Clin+RS integrated model, which combined posterior echo and Rad-score, demonstrated better predictive performance compared to both the Rad-score model and clinical model, achieving AUC values of 0.848 in the training dataset and 0.847 in the validation dataset.

Conclusion

The Clin+RS integrated model, incorporating posterior echo and Rad-score, demonstrated an acceptable preoperative evaluation of the TIL level. The Clin+RS integrated nomogram holds tremendous potential for preoperative individualized prediction of the TIL level in TNBC.

The utility of quantifying the orientation of breast masses in ultrasound imaging

The aim of this study was to quantify the orientation of breast masses and determine whether it can enhance the utility of a not parallel orientation in predicting breast mass malignancy. A total of 15,746 subjects who underwent breast ultrasound examinations were initially enrolled in the study. Further evaluation was performed on subjects with solid breast masses (≤ 5 cm) intended for surgical resection and/or biopsy. The orientation angle, defined as the acute angle between the align of the maximal longitudinal diameter of the breast mass and the surface of the breast skin, was measured. Receiver operating characteristic (ROC) curve analysis was conducted, and various performance measures including sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and the area under the ROC curve (AUC) were calculated. Multivariate analysis was performed to determine if the orientation angle was an independent predictor of breast malignancy. Decision curve analysis (DCA) was also conducted to assess the net benefit of adopting the orientation angle for predicting breast mass malignancy. The final analysis included 83 subjects with breast cancer and 135 subjects with benign masses. The intra-group correlation coefficient for the measurement of the orientation angle of breast masses was 0.986 (P = 0.001), indicating high reproducibility. The orientation angles of malignant and benign breast masses were 36.51 ± 14.90 (range: 10.7-88.6) degrees and 15.28 ± 8.40 (range: 0.0-58.7) degrees, respectively, and there was a significant difference between them (P < 0.001). The cutoff value for the orientation angle was determined to be 22.9°. The sensitivity, specificity, positive and negative predictive values, accuracy, odds ratio, and AUC for the prediction of breast malignancy using the orientation angle were 88.0%, 87.4%, 81.1%, 92.2%, 87.6%, 50.67%, and 0.925%, respectively. Multivariate analysis revealed that the orientation angle (> 22.9°), not circumscribed margin, and calcifications of the breast mass were independent factors predicting breast malignancy. The net benefit of adopting the orientation angle for predicting breast malignancy was 0.303. Based on these findings, it can be concluded that quantifying the orientation angle of breast masses is useful in predicting breast malignancy, as it demonstrates high sensitivity, specificity, AUC, and standardized net benefit. It optimizes the utility of the not parallel orientation in assessing breast mass malignancy.

A Nomogram Based on Conventional and Contrast-Enhanced Ultrasound for Pre-operative Prediction of Nipple-Areola Complex Involvement in Breast Cancer: A Prospective Study

OBJECTIVE

Accurately predicting nipple-areola complex (NAC) involvement in breast cancer is essential for identifying eligible patients for a nipple-sparing mastectomy. This study was aimed at developing a pre-operative nomogram for NAC involvement in breast cancer using conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS).

METHODS

All patients with primary breast cancer confirmed by pre-operative biopsy underwent US and CEUS examinations. Post-operative pathology was used as the gold standard in assessing NAC involvement. Lasso regression was used to select the predictors most associated with NAC involvement. A nomogram was constructed to calculate the diagnostic efficacy. The data were internally verified with 500 bootstrapped replications, and a calibration curve was generated to validate the predictive capability.

RESULTS

Seventy-six patients with primary breast cancer were included in this study, which included 16 patients (21.1%) with NAC involvement and 60 patients (78.9%) without NAC involvement. Among the 23 features of US and CEUS, Lasso regression selected one US feature and two CEUS features, namely, ductal echo extending from the lesion, ductal enhancement extending to the nipple and focal nipple enhancement. A nomogram was constructed, and the results revealed that the area under the curve, sensitivity, specificity and accuracy were 0.891, 81.3%, 86.7% and 85.5%, respectively. The calibration curve exhibited good consistency between the predicted probability and the actual probability.

CONCLUSION

The nomogram developed based on US and CEUS had good performance in predicting NAC involvement in breast cancer before surgery, which may facilitate the selection of suitable patients for NAC preservation with greater oncological safety.

Circumscribed Masses on Breast MRI: Can MRI Features Guide Management?

OBJECTIVE

Management of circumscribed breast masses seen on MRI is largely extrapolated from mammography and US data with limited MRI-specific data available. This study aimed to assess clinical and MRI imaging features of malignant circumscribed breast masses.

METHODS

In this IRB-approved retrospective study, breast MRIs performed between April 1, 2008, and August 30, 2020, containing circumscribed masses, excluding multiple bilateral circumscribed masses, were reviewed. Clinical and imaging features of all eligible masses were recorded, and associations with malignant outcomes were assessed using Fisher's exact test and Wilcoxon rank sum test, with P < 0.05 considered significant.

RESULTS

For the 165 masses that met study criteria in 158 women, the mean age was 48 years (SD 12.0 years). Nine of 165 masses were malignant (5.5%). Round masses were significantly more likely to be malignant (7/37, 18.9%) compared to oval masses (2/128, 1.7%) (P < 0.001). Among masses with available dynamic contrast kinetics data, the malignancy rate was 0/84 (0%) for persistent kinetics, 2/23 (8.7%) for plateau kinetics, and 4/24 (16.7%) for washout kinetics (P = 0.002). The malignancy rate for oval masses without washout kinetics was 0% (0/92). T2 hyperintense masses had a malignancy rate of 7/104 (6.7%), and homogeneously enhancing masses had a malignancy rate of 5/91 (5.5%).

CONCLUSION

These data support the use of mass shape and dynamic contrast enhancement kinetics to guide management of circumscribed breast masses seen by MRI, with oval masses without washout kinetics and any circumscribed mass with persistent kinetics showing no malignancies in this study.

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.

Fibroepithelial lesions of the breast: improving the accuracy of imaging diagnosis and reducing unnecessary biopsy

Fibroadenomas and phyllodes tumours are fibroepithelial lesions of the breast. Fibroadenomas are common benign breast masses encountered both symptomatically and incidentally. Phyllodes tumours are uncommon and usually present symptomatically. Management of fibroadenomas focuses on reducing biopsies without missing cancers while radiological management of phyllodes tumours should focus on predicting the need for surgical excision with or without margins.

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Background

Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation.

Objective

The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately.

Method

In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC).

Results

The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve.

Conclusion

Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

Does MRI have added value in ultrasound-detected BIRADS-3 breast masses in candidates for assisted reproductive therapy?

Background

Ultrasound-detected breast lesions with probably benign features are a great challenge for clinicians, especially in breasts with dense composition. We aimed to investigate the finding of two radiologic modalities on these lesions.

Methods

This retrospective cross-sectional study recruited patients including (1) candidates of assisted reproductive therapy (ART), (2) patients with prior high-risk lesions, and (3) the "suspected" BIRADS-3 masses referring to masses that US BIRADS-3 was not compatible with the clinical breast exam. The degree of agreement in diagnosing BIRADS-3 lesions between two modalities of magnetic resonance imaging (MRI) and ultrasonography (US), and comparison of the lesions in US and MRI were the study variables.

Results

A total number of 123 lesions in 67 patients with a median age of 38 (IQR: 11, range: 17-67). In the examination by MRI, 107 (87.0 %) lesions were BIRADS-3 indicating the agreement level between these two modalities. The median size of the lesions in US was 9 mm (IQR: 5, range: 3-43) and 9 mm (IQR: 10, range: 4-46) in MRI. The measured size of the lesions between the two modalities was highly correlated (Spearman correlation coefficient: 0.889, P-value < 0.001). MRI evaluation revealed two cases of deep lesions which were missed in the US imaging.

Conclusions

This study found relatively high agreement values between US and MRI in detecting BIRADS-3 breast lesions in candidates for ART or patients with prior high-risk lesions. Also, MRI could downgrade about one-tenth of the cases to a lower BIRADS level and resolved the need for closer follow-up.

A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.

The tumor-infiltrating lymphocyte ultrasonography score can provide a diagnostic prediction of lymphocyte-predominant breast cancer preoperatively

PURPOSE

Tumor-infiltrating lymphocytes (TILs) are known to predict the therapeutic effect in breast cancer. Although a preoperative tissue biopsy can be used to evaluate TILs, TILs that are heterogeneously distributed might require examination of all preoperative tissue biopsy samples. We have recently reported that the TIL ultrasonography (US) score, as determined by characteristic US findings, provides excellent predictive performance for lymphocyte predominant breast cancer (LPBC). We herein aimed to determine whether the preoperative TIL-US score can more accurately predict LPBC than preoperative tissue biopsy.

METHODS

We assessed 161 patients with invasive breast cancer that were treated with curative surgery between January 2014 and December 2017. Stromal lymphocytes were examined on preoperative tissue biopsy tissues and surgical pathological specimens. Breast cancer samples with ≥ 50% stromal TILs were defined as pre-LPBC (preoperative tissue biopsy) and LPBC (surgical pathological specimens). Useful factors for predicting LPBC were searched among clinicopathological factors.

RESULTS

The TIL-US score cutoff value for predicting LPBC was 4 points based on the receiver operating characteristic curves (area under the curve: 0.88). Several significant predictors for LPBC were revealed by the undertaken multivariate logistic regression analysis (odds ratios: TIL-US score, 26.8; pre-LPBC, 18.6; HER2, 9.2; all, p < 0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.74, 0.89, 0.85, 0.67, and 0.92 for the TIL-US score, respectively, and 0.51, 0.98, 0.87, 0.91, and 0.86 for the pre-LPBC, respectively.

CONCLUSION

TIL-US scores can predict LPBC preoperatively and are characterized by a significantly high sensitivity and negative predictive value.

Features of breast cancer initially assessed as probably benign on ultrasound: A retrospective study

We evaluated the features of breast cancers initially assessed as probably benign at ultrasound (US). Of the 7098 patients who underwent breast cancer surgery at our institution between 2014 and 2016, 179 lesions in 178 patients who had both a prior US with Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment and a recent US with a diagnosis of breast cancer were enrolled. Prior and recent US findings and category were retrospectively reassessed in line with the BI-RADS Atlas and analyzed. Of the 179 BI-RADS 3 lesions, 105 (59%) were retrospectively reassessed to category 4 and 74 (41%) retained category 3. Noncircumscribed margin, irregular shape, posterior enhancement, and nonparallel orientation were more frequently observed in the reassessment category 4 group than in the reassessment category 3 group (94% vs 43%, 81% vs 19%, 16% vs 4%, 14% vs 0%, respectively). The recent US revealed that 150 of the 179 lesions (84%) had > 20% size increase, and 121 (68%) showed morphologic changes. Margin was the most frequently observed morphologic feature to change (41%, 73/179). Care should be taken to look for subtle but suspicious US features and changes in mass, especially of margin, for early diagnosis of breast cancer.

BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer

PURPOSE

Increased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.

MATERIALS AND METHODS

Patients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial. Two fellowship-trained breast radiologists used the BI-RADS ultrasound lexicon to assess pretreatment tumor shape, margin, echo pattern, orientation, posterior features, and vascularity. sTILs were defined as low <20 or high ≥20 on the pretreatment biopsy. Fisher's exact tests were used to assess the association between lexicon descriptors and sTIL levels.

RESULTS

The 284 patients (mean age 52 years, range 24-79 years) were comprised of 68% (193/284) with low-sTIL tumors and 32% (91/284) with high-sTIL tumors. TNBC tumors with high sTILs were more likely to have the following features: (1) oval/round shape than irregular shape (p = 0.003), (2) circumscribed or microlobulated margins than spiculated, indistinct, or angular margins (p = 0.0005); (3) complex cystic and solid pattern than heterogeneous pattern (p = 0.006); and (4) posterior enhancement than shadowing (p = 0.002). There was no significant association between sTILs and descriptors for orientation and vascularity (p = 0.06 and p = 0.49, respectively).

CONCLUSION

BI-RADS ultrasound descriptors of the pretreatment appearance of a TNBC tumor can be useful in discriminating between tumors with low and high sTIL levels. Therefore, there is a potential use of ultrasound tumor characteristics to complement sTILs when used as stratification factors in treatment algorithms for TNBC.

The Utility of the Fifth Edition of the BI-RADS Ultrasound Lexicon in Category 4 Breast Lesions: A Prospective Multicenter Study in China

RATIONALE AND OBJECTIVES

The objective of this study was to evaluate the utility of the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS) in clinical breast radiology by using prospective multicenter real-time analyses of ultrasound (US) images.

MATERIALS AND METHODS

We prospectively studied 2049 female patients (age range, 19-86 years; mean age 46.88 years) with BI-RADS category 4 breast masses in 32 tertiary hospitals. All the patients underwent B-mode, color Doppler US, and US elastography examination. US features of the mass and associated features were described and categorized according to the fifth edition of the BI-RADS US lexicon. The pathological results were used as the reference standard. The positive predictive values (PPVs) of subcategories 4a-4c were calculated.

RESULTS

A total of 2094 masses were obtained, including 1124 benign masses (54.9%) and 925 malignant masses (45.1%). For BI-RADS US features of mass shape, orientation, margin, posterior features, calcifications, architectural distortion, edema, skin changes, vascularity, and elasticity assessment were significantly different for benign and malignant masses (p< 0.05). Typical signs of malignancy were irregular shape (PPV, 57.2%), spiculated margin (PPV, 83.7%), nonparallel orientation (PPV, 63.9%), and combined pattern of posterior features (PPV, 60.6%). For the changed or newly added US features, the PPVs for intraductal calcifications were 80%, 56.4% for internal vascularity, and 80% for a hard pattern on elastography. The associated features such as architectural distortion (PPV, 89.3%), edema (PPV, 69.2%), and skin changes (PPV, 76.2%) displayed high predictive value for malignancy. The rate of malignant was 7.4% (72/975) in category 4a, 61.4% (283/461) in category 4b, and 93.0% (570/613) in category 4c. The PPV for category 4b was higher than the likelihood ranges specified in BI-RADS and the PPVs for categories 4a and 4c were within the acceptable performance ranges specified in the fifth edition of BI-RADS in our study.

CONCLUSION

Not only the US features of the breast mass, but also associated features, including vascularity and elasticity assessment, have become an indispensable part of the fifth edition of BI-RADS US lexicon to distinguish benign and malignant breast lesions. The subdivision of category 4 lesions into categories 4a, 4b, and 4c for US findings is helpful for further assessment of the likelihood of malignancy of breast lesions.

Well-documented benign sonographic characteristics are rarely seen in malignant masses: is now the time to biopsy less?

AIM

To determine whether malignant breast masses demonstrate any of the eight well-documented benign criteria on ultrasound (US) to ascertain whether patients that display these characteristics can safely be discharged without intervention reducing the number of unnecessary benign biopsies.

MATERIALS AND METHODS

A retrospective evaluation was performed of the US images of 816 histologically confirmed breast cancer cases diagnosed from 2016-2018. The malignant lesions were subdivided into seven age categories and compliance with the eight benign (Stavros) characteristics assessed.

RESULTS

None of the 816 malignancies displayed all eight of the described benign Stavros characteristics. The combination of a smooth/well-circumscribed border and an ellipsoid shape with horizontal orientation in addition to being homogeneously isoechoic with three or less gentle lobulations was found in only 0.4% of cases (3/816).

CONCLUSIONS

This research adds to the body of literature supporting the robust nature of the Stavros criteria and supports the non-invasive management of lesions that display all of these associated benign features on US. The presence of established benign ultrasound characteristics are very uncommonly seen in malignancies as highlighted in this study, and it is proposed than no breast cancers will display all eight criteria combined. It is suggested that adherence to the well-documented benign characteristics when assessing solid breast lesions could safely obviate the need for biopsy in women of all ages.

A Practical, Clinical User-Friendly Format for Breast Ultrasound Report

Objective

Breast ultrasound (BUS) is often performed as an adjunct to mammography in breast cancer screening or for evaluating breast lesions. Our aim was to design a practical and user-friendly format for BUS that could include the details of the Breast Imaging Reporting and Data System.

Materials and Methods

As a team of radiologists and surgeons trained in the management of breast diseases, we gathered and carried out the project in four phases-literature search and collection of present report formats, summarizing key points and preparing the first draft, seeking expert opinion and preparing the final format, and pilot testing-followed by a survey was answered by the research team's radiologists and surgeons.

Results

It produced a list of items to be stated in the BUS report, the final BUS report format, and the pilot format guide. Then, the radiologists used the format in three active ultrasound units in university-affiliated centers, and reports were referred to the surgeons. At the end of the project, the survey showed a high degree of ease of use, clarity, conciseness, comprehensiveness, and well-classified structure of the report format; but radiologists believed that the new organization took more time.

Conclusion

We propose our design as a user-friendly and practical format for BUS reports. It should be used for a longer time and by various ultrasound centers in order to ascertain its benefits.

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P = .018) and lower parallel orientation score (P = .007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists' Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P = .014), and patient age (P < .001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists' BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P < .001 in the development cohort; 97% vs. 45% P < .001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P = .317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

B-mode ultrasound diagnostic flowchart for solid breast masses: JABTS BC-01 study

Purpose

Breast ultrasound has been widely used as an essential examination for diagnosing breast cancer. However, standardized diagnostic criteria are as yet lacking. This study aimed to develop a simple diagnostic flowchart for beginners learning breast ultrasonography. The diagnostic flowchart was developed based on the recall criteria widely used in Japan.

Methods

We conducted a multicenter study to examine recall criteria usefulness in the diagnostic phase of breast disease. Women with ultrasound-visible breast masses who underwent B-mode breast ultrasound examination were recruited from 22 hospitals in Japan between September 2009 and January 2010. B-mode images were evaluated by members of the centralized image interpretation committee. We developed the new diagnostic flowchart based on the results. The usefulness of the diagnostic flowchart was assessed by employing datasets from the current study and another study which we conducted (BC-04 study).

Results

We evaluated 1045 solid masses (malignant: 495, benign: 550). Multivariate analysis showed that shape, margin, echogenic halo, interruption of the mammary gland interface, and depth width ratio were significant findings for distinguishing between benign and malignant masses. We modified the recall criteria and developed our novel diagnostic flowchart using these findings. The sensitivity and specificity of the new flowchart (current study: 0.97, 0.45; BC-04 study dataset: 0.95, 0.45) were similar to those of experts (current study: 0.96, 0.54; BC-04 study dataset: 0.98, 0.38).

Conclusion

We developed a simple diagnostic flowchart for breast ultrasound. This flowchart is anticipated to be applicable to educating beginners learning breast ultrasound.

Audit of eliminating biopsy for presumed fibroadenomas with benign ultrasound greyscale and shear-wave elastography findings in women aged 25-39 years

AIM

To assess the safety of a policy of not biopsying presumed fibroadenomas with benign ultrasound and shear-wave elastography findings in women aged 25-39 years.

MATERIALS AND METHODS

Patients aged 25-39 years were included after attending a one-stop clinic before 01/05/2019 where clinical and ultrasound greyscale findings suggested a benign mass and shear-wave elastography showed a mean stiffness of <50 kPa. Such patients were reassured and discharged without biopsy or follow-up. The archived ultrasound images were reviewed subsequently by a second radiologist who could recall patients for biopsy if he deemed the imaging findings to be not definitively benign. Local and national electronic records were reviewed to identify whether these patients later re-presented with breast problems.

RESULTS

Seventy-six women with a mean age of 33 years were included. The minimum follow-up was 12 months and the mean follow-up was 2 years. Three (4%) of patients were recalled after review of the ultrasound images by a second radiologist. Five (7%) patients re-presented with the same lump. Three (4%) patients re-presented with a different benign problem. Two patients presented with breast cancer in the ipsilateral breast but at a different location from the assessed benign lesion.

CONCLUSION

Early audit results of a policy of not biopsying or following up clinically benign masses with benign ultrasound greyscale appearances and shear wave stiffness <50 kPa in women aged 25-39 years suggest that this approach may be safe and acceptable.

Clinical value of color Doppler ultrasound combined with serum CA153, CEA and TSGF detection in the diagnosis of breast cancer

In the present retrospective study, the clinical value of color Doppler ultrasound combined with the detection of the serum tumor markers carbohydrate antigen 153 (CA153), carcinoembryonic antigen (CEA) and tumor specific growth factor (TSGF) as diagnostic tools for breast cancer was investigated. A total of 103 patients with breast cancer and 50 patients with benign breast lesions were enrolled in the study. All patients were examined by color Doppler ultrasonography. Electrochemiluminescence was used to measure the expression levels of CA153 and CEA, and chemical colorimetric assay for the measurement of TSGF expression. The differences in mass boundary, morphology, internal echo, calcification, peak blood flow velocity (V_max), resistance index (RI), pulsatility index (PI) and blood flow signal classification between the two groups were statistically significant (P<0.05). The expression levels of CA153, CEA and TSGF in the serum of patients in the breast cancer group were significantly higher than those in the benign lesion group (P<0.01). In the breast cancer group, CA153, CEA and TSGF expression levels were significantly higher in patients with high-stage cancer and recurrence, compared with the patients with low-stage cancer and no recurrence. The sensitivity, accuracy and negative predictive value of the combined detection were significantly improved compared with those of the single tests (P<0.01). Color Doppler ultrasound combined with the detection of CA153, CEA and TSGF levels in the serum of patients can be used as an effective tool that can improve the accuracy of breast cancer diagnosis leading to early diagnosis and clinical intervention.

Ultrasound and Clinical Characteristics of False-negative Results in Mammography Screening of Dense Breasts

OBJECTIVE

We analyzed the clinical and ultrasound characteristics associated with false-negative mammography results in women with dense breasts.

MATERIALS AND METHODS

The present study included 191 women (mean age, 54.47 ± 11.61 years; range, 31-75 years) who had presented from July 2015 to June 2018 with pathologically confirmed breast cancer. The mammography, conventional ultrasound, and elastography imaging results of these patients were reviewed. Breast density and screening cancer probability from mammography and conventional ultrasound imaging were scored using the Breast Imaging Reporting and Data System. Multivariate logistic regression analysis was performed to identify the factors independently associated with the false-negative results on breast mammographic screening.

RESULTS

Of 191 confirmed breast cancer cases, 55 (28.8%) were assigned to category ≤ 3, and 136 (71.2%) were assigned to category ≥ 4a according to the mammography findings. All the breasts were graded mammographically as dense. A rougher margin (odds ratio [OR], 8.123; 95% confidence interval [CI], 1.731-38.127) was the strongest independent factor associated with negative results, followed by a lower stiffness ratio (OR, 7.773; 95% CI, 2.574-23.473), negative axillary lymph node status (OR, 5.066; 95% CI, 1.028-24.955), and softer lesions (OR, 1.037; 95% CI, 1.001-1.075).

CONCLUSION

Women with dense breasts, a lower lesion/glandular tissue stiffness ratio, and softer cancer can easily lead to a misdiagnosis using mammography. By giving sufficient attention to the margin, earlier stage cancer with negative lymph node status are more likely to benefit from supplemental ultrasound imaging.

Application of Point Shearwave Elastography to Breast Ultrasonography: Initial Experience Using “S-Shearwave” in Differential Diagnosis

Purpose

To evaluate the optimal measurement location, cut-off value, and diagnostic performance of S-Shearwave in differential diagnosis of breast masses seen on ultrasonography (US).

Materials and Methods

During the study period, 225 breast masses in 197 women were included. S-Shearwave measurements were made by applying a square region-of-interest automatically generated by the US machine. Shearwave elasticity was measured three times at four different locations of the mass, and the highest shearwave elasticity was used for calculating the optimal cut-off value. Diagnostic performance was evaluated by using the area under the receiving operator characteristic curve (AUC).

Results

Of the 225 breast masses, 156 (69.3%) were benign and 69 (30.7%) were malignant. Mean S-Shearwave values were significantly higher for malignant masses (108.0 ± 70.0 kPa vs. 43.4 ± 38.3 kPa; p < 0.001). No significant differences were seen among AUC values at different measurement locations. With a cut-off value of 41.9 kPa, S-Shearwave showed 85.7% sensitivity, 63.9% specificity, 70.7% accuracy, and positive and negative predictive values of 51.7% and 90.8%, respectively. The AUCs for US and S-Shearwave did not show significant differences (p = 0.179).

Conclusion

S-Shearwave shows comparable diagnostic performance to that of grayscale US that can be applied for differential diagnosis of breast masses seen on US.

Breast tumor classification through learning from noisy labeled ultrasound images

PURPOSE

To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, most clinical samples generally do not have biopsy results. Previous works have proposed generating benign/malignant labels according to Breast Imaging, Reporting and Data System (BI-RADS) ratings. However, this approach will cause noisy labels, which means that the benign/malignant labels produced from BI-RADS diagnoses may be inconsistent with the ground truths. Consequently, deep models will overfit the noisy labels and hence obtain poor generalization performance. In this work, we mainly focus on how to reduce the negative effect of noisy labels when they are used to train breast tumor classification models.

METHODS

We propose an effective approach called noise filter network (NF-Net) to address the problem of noisy labels when training breast tumor classification models. Specifically, to prevent deep models from overfitting the noisy labels, we propose incorporating two softmax layers for classification. Additionally, to strengthen the effect of clean labels, we design a teacher-student module for distilling the knowledge of clean labels.

RESULTS

We conduct extensive comparisons with the existing works on addressing noisy labels. Our method achieves a classification accuracy of 73%, with a precision of 69%, recall of 80%, and F1-score of 0.74. This result is significantly better than those of the existing state-of-the-art works on addressing noisy labels.

CONCLUSIONS

This work provides a means to overcome the label shortage problem in training breast tumor classification models. Specifically, we can generate benign/malignant labels according to the BI-RADS ratings. Although this approach will cause noisy labels, the design of NF-Net can effectively reduce the negative effect of such labels.

Evaluation of the positive predictive value (PPV3) of ACR BI-RADS category 4 and 5 based on the outcomes of Invasive Diagnostic Office in an outpatient clinic

PURPOSE

The American College of Radiology (ACR) BI-RADS classification is the applicable for breast lesion assessment. BI-RADS categories 4 and 5 need to be followed by biopsy. The aim of our study was to evaluate the tissue biopsy-proven positive predictive value (PPV3) for BI-RADS 4 (and its subcategories) and for BI-RADS 5, and BI-RADS distribution, in comparison to ACR assumptions and literature.

MATERIAL AND METHODS

We retrospectively analysed biopsies performed in our outpatient clinic in 2017. Our target group of patients consisted of 797 patients at the average age of 52 years.

RESULTS

BI-RADS 5 constituted 12% of cases (95 cases), and BI-RADS 4 88% (698 cases). Within BI-RADS 4 subdivisions there were 359 cases in 4a (45.3%), 215 in 4b (27%), and 124 in 4c (15.6%). Overall PPV3 was 34.8%. BI-RADS 5 category PPV3 number was 97.89%. In category BI-RADS 4 the values of PPV3 equalled 26.22% without subdivision and 3.6%, 27.9%, and 88.7% for subcategories BI-RADS 4a, 4b, and 4c, respectively.

CONCLUSIONS

BI-RADS categorisation by radiologists in the studied group matches the literature data according to achieved PPV and BI-RADS percentage distribution. The stratification of cancer risk among categories was proven with Mann-Whitney U test p value < 0.005. There was a statistically important unaccepted difference of PPV3 between core biopsy and vacuum-assisted biopsy, which needs further investigation.

Parallel vessels as a predictor of benignity in solid breast masses

To evaluate the presence of a parallel artery and vein on color Doppler ultrasound as a predictor of benignity in solid breast masses. This prospective study included all patients with solid breast masses identified by ultrasound at our center from January 2012 through December 2015. All masses were studied with B mode and color Doppler ultrasound. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value, and their respective 95% confidence intervals for the parallel vessel sign against the histologic findings or 2 years' follow-up without changes. A total of 526 solid masses were included in the 3-year period; the parallel vessel sign was observed in 377 (71.6%). We found 74.8% (95% CI: 70.9-78.7) sensitivity, 65% (95% CI: 50.2-79.7) specificity, 96.2% (95% CI: 94.3-98.1) positive predictive value, and 17.5% (95% CI: 11.4-23.6) negative predictive value. Of the 379 masses classified as breast imaging reporting and data system (BI-RADS) 3, the parallel vessel sign was observed in 275 (72.5%); all of these were definitively benign. Of the 109 masses classified as BI-RADS 4, the parallel vessel sign was observed in 89 (80.7%); 88 (98.8%) of these were definitively benign. Of the 38 masses classified as BI-RADS 5, the parallel vessel sign was observed in 13 (34.2%); all of these were definitively malignant. The parallel artery and vein sign is a good predictor of benignity, although it must be analyzed together with other morphologic descriptors.

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

BACKGROUND

Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).

METHODS

This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage.

RESULTS

We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5".

CONCLUSION

The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

Evaluating the role of strain ratio elastography in determining malignancy potential and calculating objective BIRADS US scores using ultrasonography and elastography features

Purpose

The aims of this study were, firstly, to evaluate solid breast masses based on their malignancy potential and to determine whether the strain elastography ratio (SER) can contribute to classical grey-scale ultrasonography findings, and secondly, to define objective BIRADS US scores using ultrasound (US) and SER findings.

Material and methods

A total of 280 patients and 297 solid breast masses were evaluated using sonographic and elastographic data. The SER was measured for each lesion.

Results

The positive predictive values (PPV) for each criterion was calculated to be between 35% and 83.3%. The lowest PPV was obtained from hypoechogenicity (35%) and the highest PPV was obtained for anti-parallel features (83.3%). The difference between the mean SER of benign and malignant lesions was statistically significant. After ROC analysis, the SER cut-off value was calculated to be 3.1 for determining if the mass was benign or malignant. Mass scores were calculated for each solid breast mass based on positive predictive values, and BIRADS US score was defined as the sum of mass scores.

Conclusions

SER findings can be used as malignancy criteria in evaluating solid breast masses. BIRADS US score can be objectively determined based on US and elastography features instead of doing subjective scoring. As an additional result, all solid breast masses have the possibility to be malignant, even though US and elastography findings indicate the opposite.

Probably Benign Solid Breast Lesions on Ultrasound: Need for Biopsy Reassessed

Objective:

To determine the negative predictive value of ultrasound for breast masses with probably benign morphology, and to assess whether follow-up may be an acceptable alternative to biopsy.

Methods:

After Institutional Review Board approval, all solid breast masses categorized as probably benign (American College of Radiology Breast Imaging Reporting and Data System [BI-RADS] 3) on ultrasound from January 2014 to December 2015, and having either tissue diagnosis or imaging stability for 24 months, or downgrading to BIRADS 2 during imaging surveillance were included.

Result:

A total of 157 lesions in 40 patients constituted the study population. The mean patient age was 31.3 years (range, 20-56 years). Seventeen of these 157 lesions underwent tissue diagnosis with no invasive breast cancer. Out of the remaining 140 lesions, 115 were stable on imaging for 24 months or more. The rest 25 were deemed benign because of decrease in size on follow up (n=1), non-recommendation of further imaging by the second radiologist on follow up ultrasound (n= 13) or presence of benign tissue diagnosis in the largest lesion (n=11).

Conclusion:

Ultrasound has 100% negative predictive value for breast lesions with probably benign morphology, whether palpable or not. Follow up is an appropriate option to immediate biopsy of such lesions keeping in mind that noncompliance with surveillance may be a potential problem.

Follow-up interval for probably benign breast lesions on screening ultrasound in women at average risk for breast cancer with dense breasts

Background Women at high risk for breast cancer and women at average risk have different pretest probabilities. Probably benign lesions on screening ultrasound (US) should be assessed and managed differently for these two risk groups. Purpose To evaluate the effectiveness of short-term follow-up for probably benign lesions on screening US in women at average risk for breast cancer with dense breasts. Material and Methods A total of 445 women at average risk for breast cancer with probably benign lesions on screening US, dense breasts, and negative or benign mammography results were included. Women were classified into the six-month group (n = 345) or 12-month group (n = 100) according to when the first follow-up was performed (3-9 months or 9-15 months). The cancer detection rate, frequencies of newly developed lesions and progressed lesions, and biopsy rate were compared. Results There were no malignancies from three to 15 months. Three cancers newly developed after 15 months. One was a 3-mm ductal carcinoma in situ and two were 10-mm and 18-mm invasive ductal carcinoma without lymph node metastasis. The frequency of newly developed lesions and progressed lesions and biopsy rate were not significantly different between the six-month and 12-month groups ( P = 0.320, 0.621, and >0.999). Conclusion A follow-up at 12 months can be considered for probably benign lesions on screening US in women at average risk for breast cancer with dense breasts. However, a large series prospective study is needed before clinical application.

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound (US), contrast-enhanced US (CEUS), combined US and CEUS and magnetic resonance imaging (MRI) in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve. The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and 91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them (kappa<0.20). In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.

Sonographic features of invasive ductal breast carcinomas predictive of malignancy grade

Context:

Assessment of individual sonographic features provides vital clues about the biological behavior of breast masses and can assist in determining histological grade of malignancy and thereby prognosis.

Aims:

Assessment of individual sonographic features of biopsy proven invasive ductal breast carcinomas as predictors of malignancy grade.

Settings and Design:

A retrospective analysis of sonographic findings of 103 biopsy proven invasive ductal breast carcinomas.

Materials and Methods:

Tumor characteristics on gray-scale ultrasound and color flow were assessed using American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) Atlas Fifth Edition. The sonographic findings of masses were individually correlated with their histopathologic grades.

Statistical Analysis Used:

Chi square test, ordinal regression, and Goodman and Kruskal tau test.

Results:

Breast mass showing reversal/lack of diastolic flow has a high probability of belonging to histological high grade tumor (β 1.566, P 0.0001). The masses with abrupt interface boundary are more likely grade 3 (β 1.524, P 0.001) in comparison to masses with echogenic halos. The suspicious calcifications present in and outside the mass is a finding associated with histologically high grade tumors. The invasive ductal carcinomas (IDCs) with complex solid and cystic echotexture are more likely to be of high histological grade (β 1.146, P 0.04) as compared to masses with hypoechoic echotexture.

Conclusions:

Certain ultrasound features are associated with tumor grade on histopathology. If the radiologist is cognizant of these sonographic features, ultrasound can be a potent modality for predicting histopathological grade of IDCs of the breast, especially in settings where advanced tests such as receptor and molecular analyses are limited.

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

Evaluation of an automated breast volume scanner according to the fifth edition of BI-RADS for breast ultrasound compared with hand-held ultrasound

OBJECTIVES

To investigate the automated breast volume scanner (ABVS) in comparison with hand-held ultrasound (HHUS) according to the fifth edition of BI-RADS ultrasound.

MATERIAL AND METHODS

A total of 831 lesions in 786 patients who underwent both HHUS and ABVS were included. Three radiologists independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The accuracy of malignancy prediction and diagnostic performance of the BI-RADS descriptors were assessed using multivariate logistic regression and area under the receiver operator characteristic curve (AUC), respectively.

RESULTS

ABVS and HHUS showed moderate to good interobserver agreement (κ = 0.53-0.67 and 0.55-0.70, respectively) except in associated features (κ = 0.31 and 0.36, respectively) for BI-RADS lexicons. Irregular shape, a non-circumscribed margin, and posterior features (combined or shadowing) were independently associated with malignancy in both ABVS and HHUS. Calcification presence on ABVS (odds ratio [OR], 95% confidence interval [CI]: 2.09, 1.11-3.94) and non-parallel orientation on HHUS (OR, 95% CI: 2.04, 1.10-3.78) were independently associated with malignancy. There were no significant differences between ABVS and HHUS in sensitivity (84.2% vs. 84.2%), specificity (80.5% vs. 83.9%), or AUC (0.88 vs. 0.90).

CONCLUSIONS

According to the fifth BI-RADS edition, ABVS is not statistically significantly different from HHUS with regard to interobserver variability and diagnostic performance.

Real-Time Elastography for Differentiation of Breast Lesions

Backroung

To investigate the diagnostic performance of the elastography-based strain index ratio in the differential diagnosis of malignant and benign breast lesions.

Material/Methods

Seventy-nine breast masses that were classified as BI-RADS category 3, 4, and 5 on B-mode ultrasonography (US) were further prospectively evaluated by real-time sonoelastography (RTE). To obtain an optimal cut-off value of the strain ratio for differentiating between malignant and benign breast lesions, RTE findings were compared with histopathology of core needle biopsy samples or with ultrasound follow-up data of the analyzed masses.

Results

Seventy-nine breast lesions [BI-RADS category 3 (n=15), BI-RADS category 4 (n=34), and BI-RADS category 5 (n=30)] were classified as malignant (n=36) or benign (n=43). The mean strain index value was 6.59±3.44 (range 0.6–14) for malignant lesions and 2.79±2.16 (range 0.6–8.7) for benign lesions, respectively (p<0.05). As regards the detection of malignant lesions, US was characterized by sensitivity and specificity of 100% (CI 95%; 88–100) and 90% (CI 95%; 76–97), respectively. When an optimal value of the strain ratio (4.25) was obtained by ROC curve analysis, the sensitivity and specificity for diagnosing malignant lesions were 86% (CI 95%; 70–95) and 76% (CI 95%; 60–87), respectively.

Conclusions

RTE can play an important role in the differentiation between malignant and benign breast masses, but it should be used in conjunction with ultrasonography.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.

The role of ultrasonographic findings to predict molecular subtype, histologic grade, and hormone receptor status of breast cancer

PURPOSE

The correlation between imaging findings and pathologic characteristics of tumors may provide information for diagnosis and treatment of cancer. The aim of this study is to determine whether ultrasound features of breast cancer are associated with molecular subtype, histologic grade, and hormone receptor status, as well as assess the predictive value of these features.

METHODS

A total of 201 consecutive invasive breast cancer patients were reviewed from the database according to the Breast Imaging and Reporting Data System (BI-RADS). Tumor margins were classified as circumscribed and noncircumscribed. Noncircumscribed group was divided into indistinct, spiculated, angular, and microlobulated. The posterior acoustic features were divided into four categories: shadowing, enhancement, no change, and mixed pattern.

RESULTS

Tumors with posterior shadowing were more likely to be of nontriple negative subtype (odds ratio [OR], 7.42; 95% CI, 2.10-24.99; P = 0.002), low histologic grade (grade 1 or 2 vs. grade 3: OR, 2.42; 95% CI, 1.34-4.35; P = 0.003) and having at least one positive receptor (OR, 3.36; 95% CI, 1.55-7.26; P = 0.002). Tumors with circumscribed margins were more often triple-negative subtype (OR, 6.72; 95% CI, 2.56-17.65; P < 0.001), high grade (grade 3 vs. grade 1 or 2: OR, 5.42; 95% CI, 2.66-11.00; P < 0.001) and hormone receptor negative (OR, 4.87; 95% CI, 2.37-9.99; P < 0.001).

CONCLUSION

Sonographic features are strongly associated with molecular subtype, histologic grade, and hormone receptor status of the tumor. These findings may separate triple-negative breast cancer from other molecular subtypes.

Cancer Measurement at Ultrasound: State of the Art

Reliable and reproducible tumor measurement is fundamental in the oncologic decision making. In this article, we first highlight the importance of a precise tumor measurement, reviewing the correct modality of measuring tumor lesions at ultrasound. Then we analyze the measurement discrepancies between ultrasound and pathology as well as the discrepancies reported between ultrasound and other imaging modalities. Thereafter, basing on the existent literature and on our experience, we discuss the factors influencing the tumor size measurements at ultrasound. Finally, we illustrate the current strategies to improve the effectiveness of cancer lesions measurement.

Which supplementary imaging modality should be used for breast ultrasonography? Comparison of the diagnostic performance of elastography and computer-aided diagnosis

Purpose

The aim of this study was to evaluate and compare the diagnostic performance of grayscale ultrasonography (US), US elastography, and US computer-aided diagnosis (US-CAD) in the differential diagnosis of breast masses.

Methods

A total of 193 breast masses in 175 consecutive women (mean age, 46.4 years) from June to August 2015 were included. US and elastography images were obtained and recorded. A US-CAD system was applied to the grayscale sonograms, which were automatically analyzed and visualized in order to generate a final assessment. The final assessments of breast masses were based on the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, while elasticity scores were assigned using a 5-point scoring system. The diagnostic performance of grayscale US, elastography, and US-CAD was calculated and compared.

Results

Of the 193 breast masses, 120 (62.2%) were benign and 73 (37.8%) were malignant. Breast masses had significantly higher rates of malignancy in BI-RADS categories 4c and 5, elastography patterns 4 and 5, and when the US-CAD assessment was possibly malignant (all P<0.001). Elastography had higher specificity (40.8%, P=0.042) than grayscale US. US-CAD showed the highest specificity (67.5%), positive predictive value (PPV) (61.4%), accuracy (74.1%), and area under the curve (AUC) (0.762, all P<0.05) among the three diagnostic tools.

Conclusion

US-CAD had higher values for specificity, PPV, accuracy, and AUC than grayscale US or elastography. Computer-based analysis based on the morphologic features of US may be very useful in improving the diagnostic performance of breast US.

Hyper-Echoic Rim in Thyroid Nodules: A New Ultrasonographic Feature for Malignancy Prediction

The goal of this study was to verify the ultrasound features of hyper-echoic rims in thyroid nodules and to evaluate their diagnostic value in predicting thyroid malignancies. We retrospectively analyzed 228 pathologically proven thyroid nodules (137 malignant and 91 benign nodules). Forty-eight thyroid nodules had a hyper echogenic rim. All malignant nodules (137) were papillary carcinomas, which were studied to identify the correlation between the hyper-echoic rim (detected by ultrasound) and other histologic features. Presence of a hyper-echoic rim had high specificity (94.51%), but low sensitivity (31.39%) in predicting malignancy (p < 0.05). Thirty-seven of 43 malignant nodules had boundary zones of mixed structure (apparent fibrous stroma bands or dense collagenous border with a mixed population of cancerous cells) under microscopic examination. In conclusion, the hyper-echogenic rim could be one additional ultrasound parameter in the diagnosis of thyroid lesions.