Correlation of shear-wave elastography stiffness and apparent diffusion coefficient values with tumor characteristics in breast cancer.

We aimed to investigate the correlation between shear-wave elastography (SWE) and apparent diffusion coefficient (ADC) values in breast cancer and to identify the associated characteristics. We included 91 breast cancer patients who underwent SWE and breast MRI prior to surgery between January 2016 and November 2017. We measured the lesion's mean (Emean) and maximum (Emax) elasticities of SWE and ADC values. We evaluated the correlation between SWE, ADC values and tumor size. The mean SWE and ADC values were compared for categorical variable of the pathological/imaging characteristics. ADC values showed negative correlation with Emean (r = - 0.315, p = 0.002) and Emax (r = - 0.326, p = 0.002). SWE was positively correlated with tumor size (r = 0.343-0.366, p < 0.001). A higher SWE value indicated a tendency towards a higher T stage (p < 0.001). Triple-negative breast cancer showed the highest SWE values (p = 0.02). SWE were significantly higher in breast cancers with posterior enhancement, vascularity, and washout kinetics (p < 0.02). SWE stiffness and ADC values were negatively correlated in breast cancer. SWE values correlated significantly with tumor size, and were higher in triple-negative subtype and aggressive imaging characteristics.

[An analysis of breast cancer patients with ultrasound BI-RADS 3 lesions after minimally invasive excision in clinicopathological features and influencing factors of residual tumor].

Objectives: To examine the clinicopathological characteristics and the influencing factors of the residual tumor of patients with Breast Image Report and Data System (BI-RADS) grade 3 lesions diagnosed with malignancy after minimally invasive excision. Methods: In this retrospective case-control study, clinicopathological data of 69 cases, which had been evaluated as BI-RADS 3 lesions by ultrasound (4 151 cases) diagnosed with breast cancer by minimally invasive excision pathology, were analyzed between May 2012 and June 2016 at the Department of Breast Surgery of the Second Hospital of Shandong University and Linyi People's Hospital. All patients were female, aged (43.4±8.2) years (range: 22 to 70 years). Based on residual tumor after minimally invasive excision, patients were classified into two subgroups: tumor residual group (n=39) and non-tumor residual group (n=30). The clinicopathological features between the two groups were compared. The differences in clinicopathological characteristics were compared in different groups using the χ2 test and the t test. Potential variables identified in the univariate analysis and other relevant variables will be analyzed multivarially using Logistic regression models. The Kaplan-Meier method was applied for survival analysis and survival curves. Results: The breast cancer detection rate of ultrasound BI-RADS 3 lesions was 1.66% (69/4 151), and their maximum diameter of the masses was (1.27±0.45) cm (range: 0.5 to 2.3 cm). Among them, the maximum diameter were ≤1 cm in 28 cases and >1 cm in 41 cases. Histopathological results showed carcinoma in situ in 24 cases and invasive carcinoma in 41 cases, positive expression of the estrogen receptor in 47 cases, positive expression of the progesterone receptor in 43 cases, Ki-67 proliferation index elevated in 26 cases. Axillary metastasis positive rate was 10.1% (7/69). Residual tumor after minimally invasive surgery was found in 39 cases (56.5%). Univariate analysis showed that the tumour residual group showed a significantly increased rate of positive expression of the estrogen receptor (91.9%(34/37) vs. 61.9%(13/21), χ2=7.838, P=0.012). In multivariate analysis, the only variable found to significantly affect the residual tumor was the positive expression of the estrogen receptor (OR=16.852, 95%CI: 1.819 to 156.130, P=0.013). The 5-year disease-free survival rate of breast cancer patients with breast ultrasound BI-RADS 3 lesions was 97.1% and the overall survival rate was 98.6%. Conclusions: BI-RADS 3 lesions diagnosed by ultrasound undergoing ultrasound-guided minimally invasive excision have a certain risk of detected malignancy, approximately 1.66%. Patients with positive expression of the estrogen receptor are more likely to develop residual tumor. A secondary operation should be considered to ensure that no tumor residues remain in the cavity.

Predictive value of contrast-enhanced ultrasonography and ultrasound elastography for management of BI-RADS category 4 nonpalpable breast masses.

PURPOSE: The objective of this study was to investigate the independent risk factors and associated predictive values of contrast-enhanced ultrasound (CEUS), shear wave elastography (SWE), and strain elastography (SE) for high-risk lesions (HRL) and malignant tumors (MT) among nonpalpable breast masses classified as BI-RADS category 4 on conventional ultrasound. METHODS: This prospective study involved consecutively admitted patients with breast tumors from January 2018, aiming to explore the management of BI-RADS category 4 breast tumors using CEUS and elastography. We conducted a retrospective review of patient data, focusing on those with a history of a nonpalpable mass as the primary complaint. Pathologic findings after surgical resection served as the gold standard. The CEUS arterial-phase indices were analyzed using contrast agent arrival-time parametric imaging processing mode, while quantitative and qualitative indices were examined on ES images. Independent risk factors were identified through binary logistic regression multifactorial analysis. The predictive efficacy of different modalities was compared using a receiver operating characteristics curve. Subsequently, a nomogram for predicting the risk of HRL/MT was established based on a multifactorial logistic regression model. RESULTS: A total of 146 breast masses from 146 patients were included, comprising 80 benign tumors, 12 HRLs, and 54 MTs based on the final pathology. There was no significant difference in pathologic size between the benign and HRL/MT groups [8.00(6.25,10.00) vs. 9.00(6.00,10.00), P = 0.506]. The diagnostic efficacy of US plus CEUS exceeded that of US plus SWE/SE for BI-RADS 4 nonpalpable masses, with an AUC of 0.954 compared to 0.798/0.741 (P ï¼œ 0.001). Further stratified analysis revealed a more pronounced improvement for reclassification of BI-RADS 4a masses (AUC: 0.943 vs. 0.762/0.675, P ï¼œ 0.001) than BI-RADS 4b (AUC:0.950 vs. 0.885/0.796, P＞0.05) with the assistance of CEUS than SWE/SE. Employing downgrade CEUS strategies resulted in negative predictive values ranging from 95.2 % to 100.0 % for BI-RADS 4a and 4b masses. Conversely, using upgrade nomogram strategies, which included the independent predictive risk factors of irregular enhanced shape, poor defined enhanced margin, earlier enhanced time, increased surrounding vessels, and presence of contrast agent retention, the diagnostic performance achieved an AUC of 0.947 with good calibration. CONCLUSION: After investigating the potential of CEUS and ES in improving risk assessment and diagnostic accuracy for nonpalpable BI-RADS category 4 breast masses, it is evident that CEUS has a more significant impact on enhancing classification compared to ES, particularly for BI-RADS 4a subgroup masses. This finding suggests that CEUS may offer greater benefits in improving risk assessment and diagnostic accuracy for this specific subgroup of breast masses.

Differential diagnosis of breast mucinous carcinoma with an oval shape from fibroadenoma based on ultrasonographic features.

BACKGROUND: Approximately 50% of breast mucinous carcinomas (MCs) are oval and have the possibility of being misdiagnosed as fibroadenomas (FAs). We aimed to identify the key features that can help differentiate breast MC with an oval shape from FA on ultrasonography (US). METHODS: Seventy-six MCs from 71 consecutive patients and 50 FAs with an oval shape from 50 consecutive patients were included in our study. All lesions pathologically diagnosed. According to the Breast Imaging Reporting and Data System (BI-RADS), first, the ultrasonographic features of the MCs and FAs were recorded and a final category was assessed. Then, the differences in ultrasonographic characteristics between category 4 A (low-risk group) and category 4B-5 (medium-high- risk group) MCs were identified. Finally, other ultrasonographic features of MC and FA both with an oval shape were compared to determine the key factors for differential diagnosis. The Mann-Whitney test, χ2 test or Fisher's exact test was used to compare data between groups. RESULTS: MCs with an oval shape (81.2%) and a circumscribed margin (25%) on US were more commonly assessed in the low-risk group (BI-RADS 4 A) than in the medium-high-risk group (BI-RADS 4B-5) (20%, p < 0.001 and 0%, p = 0.001, respectively). Compared with those with FA, patients with MC were older, and tended to have masses with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement on US (p < 0.001, p < 0.001, and p = 0.003, respectively). CONCLUSION: The oval shape was the main reason for the underestimation of MCs. On US, an oval mass found in the breast of women of older age with non-hypoechoic patterns, not circumscribed margins, and a posterior echo enhancement was associated with an increased risk of being an MC, and should be subjected to active biopsy.

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.

Strain Elastography Fat-to-Lesion Index Is Associated with Mammography BI-RADS Grading, Biopsy, and Molecular Phenotype in Breast Cancer.

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (-PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% -PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman's r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.

Biopathologic Characterization and Grade Assessment of Breast Cancer With 3-D Multiparametric Ultrasound Combining Shear Wave Elastography and Backscatter Tensor Imaging.

OBJECTIVE: Despite recent improvements in medical imaging, the final diagnosis and biopathologic characterization of breast cancers currently still requires biopsies. Ultrasound is commonly used for clinical examination of breast masses. B-Mode and shear wave elastography (SWE) are already widely used to detect suspicious masses and differentiate benign lesions from cancers. But additional ultrasound modalities such as backscatter tensor imaging (BTI) could provide relevant biomarkers related to tissue organization. Here we describe a 3-D multiparametric ultrasound approach applied to breast carcinomas in the aims of (i) validating the ability of BTI to reveal the underlying organization of collagen fibers and (ii) assessing the complementarity of SWE and BTI to reveal biopathologic features of diagnostic interest. METHODS: Three-dimensional SWE and BTI were performed ex vivo on 64 human breast carcinoma samples using a linear ultrasound probe moved by a set of motors. Here we describe a 3-D multiparametric representation of the breast masses and quantitative measurements combining B-mode, SWE and BTI. RESULTS: Our results reveal for the first time that BTI can capture the orientation of the collagen fibers around tumors. BTI was found to be a relevant marker for assessing cancer stages, revealing a more tangent tissue orientation for in situ carcinomas than for invasive cancers. In invasive cases, the combination of BTI and SWE parameters allowed for classification of invasive tumors with respect to their grade with an accuracy of 95.7%. CONCLUSION: Our results highlight the potential of 3-D multiparametric ultrasound imaging for biopathologic characterization of breast tumors.

Automated Versus Handheld Breast Ultrasound for Evaluating Axillary Lymph Nodes in Patients With Breast Cancer.

OBJECTIVE: Automated breast ultrasound (ABUS) is a relevant imaging technique for early breast cancer diagnosis and is increasingly being used as a supplementary tool for mammography. This study compared the performance of ABUS and handheld ultrasound (HHUS) in detecting and characterizing the axillary lymph nodes (LNs) in patients with breast cancer. MATERIALS AND METHODS: We retrospectively reviewed the medical records of women with recently diagnosed early breast cancer (≤ T2) who underwent both ABUS and HHUS examinations for axilla (September 2017-May 2018). ABUS and HHUS findings were compared using pathological outcomes as reference standards. Diagnostic performance in predicting any axillary LN metastasis and heavy nodal-burden metastases (i.e., ≥ 3 LNs) was evaluated. The ABUS-HHUS agreement for visibility and US findings was calculated. RESULTS: The study included 377 women (53.1 ± 11.1 years). Among 385 breast cancers in 377 patients, 101 had axillary LN metastases and 30 had heavy nodal burden metastases. ABUS identified benign-looking or suspicious axillary LNs (average, 1.4 ± 0.8) in 246 axillae (63.9%, 246/385). According to the per-breast analysis, the sensitivity, specificity, positive and negative predictive values, and accuracy of ABUS in predicting axillary LN metastases were 43.6% (44/101), 95.1% (270/284), 75.9% (44/58), 82.6% (270/327), and 81.6% (314/385), respectively. The corresponding results for HHUS were 41.6% (42/101), 95.1% (270/284), 75.0% (42/56), 82.1% (270/329), and 81.0% (312/385), respectively, which were not significantly different from those of ABUS (P ≥ 0.53). The performance results for heavy nodal-burden metastases were 70.0% (21/30), 89.6% (318/355), 36.2% (21/58), 97.3% (318/327), and 88.1% (339/385), respectively, for ABUS and 66.7% (20/30), 89.9% (319/355), 35.7% (20/56), 97.0% (319/329), and 88.1% (339/385), respectively, for HHUS, also not showing significant difference (P ≥ 0.57). The ABUS-HHUS agreement was 95.9% (236/246; Cohen's kappa = 0.883). CONCLUSION: Although ABUS showed limited sensitivity in diagnosing axillary LN metastasis in early breast cancer, it was still useful as the performance was comparable to that of HHUS.

Beyond BI-RADS: Nonmass Abnormalities on Breast Ultrasound.

Abnormalities on breast ultrasound (US) images which do not meet the criteria for masses are referred to as nonmass lesions. These features and outcomes have been investigated in several studies conducted by Asian researchers. However, the term "nonmass" is not included in the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) 5th edition for US. According to the Japan Association of Breast and Thyroid Sonology guidelines, breast lesions are divided into mass and nonmass. US findings of nonmass abnormalities are classified into five subtypes: abnormalities of the ducts, hypoechoic areas in the mammary glands, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. These findings can be benign or malignant; however, focal or segmental distributions and presence of calcifications suggest malignancy. Intraductal, invasive ductal, and lobular carcinomas can present as nonmass abnormalities. For the nonmass concept to be included in the next BI-RADS and be widely accepted in clinical practice, standardized terminologies, an interpretation algorithm, and outcome-based evidence are required for both screening and diagnostic US.

Characterization of breast changes in the early gestational period on automated breast ultrasound.

PURPOSE: This study was conducted to determine the characteristics of milk duct development in early pregnancy on ultrasound images. METHODS: Automated breast ultrasound (ABUS) images used for breast cancer screening in 332 pregnant women were evaluated retrospectively to determine when and how ductal development becomes evident on ultrasonography. The diagnostic criteria used for mammary gland changes during the gestational period were extension of the ducts to the margins of the breast where little or no echogenic fibroglandular tissue is seen on sonograms and/or the appearance of ductal structures running along the ascending Cooper's ligament tapering off or ending in a blind end at the superficial layer of the superficial fascia. The correlations between gestational stage and the prevalence of these criteria were verified by Spearman's rank correlation coefficient (ρ). Assessments were performed by a single radiologist with experience reading ABUS images. RESULTS: With a few exceptions, the prevalence of the above findings increased sharply beginning at 10 weeks, and then increased with progression of gestation, reaching a plateau after 20 weeks (ρ = 0.766, P < 0.00001). CONCLUSION: The findings in this study suggested that development of the milk ducts in early pregnancy can be observed using ABUS. These findings will be useful to gain a better understanding of breast ultrasound imaging characteristics during pregnancy.

Value of micro-flow imaging and high-definition micro-flow imaging in differentiating malignant and benign breast lesions.

AIM: To evaluate the value of non-contrast micro-flow imaging (MFI) and high-definition micro-flow imaging (HD-MFI) in differentiating malignant and benign breast lesions. MATERIALS AND METHODS: One hundred and thirty-three patients with 138 breast lesions (80 benign and 58 malignant lesions) were examined using colour Doppler flow imaging (CDFI), MFI, and HD-MFI before biopsy, with blood flow signals graded into four types (grade 0, 1, 2, and 3) and penetrating vessels evaluated. The micro-vascular patterns of MFI and HD-MFI were evaluated and classified into five patterns: avascular, line-like, tree-like, root hair-like, and crab claw-like pattern. The diagnostic efficiency of micro-vascular patterns was analysed. Moreover, ultrasound Breast Imaging Reporting and Data System (BI-RADS) 4A lesions were also re-assessed according to the micro-vascular patterns of MFI or HD-MFI. RESULTS: The capability of detecting blood flow and penetrating vessels from high to low was HD-MFI, MFI, and CDFI, respectively (p<0.05). Rich blood flow signals, penetrating vessels, and root hair-like or crab claw-like pattern were more likely in malignant breast lesions, while few blood flow signals, tree-like pattern were mostly in benign lesions (p<0.05). The diagnostic efficiency of HD-MFI and MFI were higher than CDFI (p>0.05). MFI could reduce unnecessary biopsy of 52 US BI-RADS 4A lesions but with two malignancies missed, while 56 ultrasound BI-RADS 4A lesions could be downgraded by HD-MFI with none malignancies missed. CONCLUSIONS: MFI and HD-MFI can detect more blood flow in breast lesions than CDFI, and could help distinguish benign and malignant breast lesions. HD-MFI could reduce the unnecessary biopsy of US BI-RADS 4A lesions without missed malignancy.

Evaluation of standard breast ultrasonography by adding two-dimensional and three-dimensional shear wave elastography: a prospective, multicenter trial.

OBJECTIVE: To reduce the number of biopsies performed on benign breast lesions categorized as BI-RADS 4-5, we investigated the diagnostic performance of combined two-dimensional and three-dimensional shear wave elastography (2D + 3D SWE) with standard breast ultrasonography (US) for the BI-RADS assessment of breast lesions. METHODS: A total of 897 breast lesions, categorized as BI-RADS 3-5, were subjected to standard breast US and supplemented by 2D SWE only and 2D + 3D SWE analysis. Based on the malignancy rate of less than 2% for BI-RADS 3, lesions assessed by standard breast US were reclassified with SWE assessment. RESULTS: After standard breast US evaluation, 268 (46.1%) participants underwent benign biopsies in BI-RADS 4-5 lesions. By using separated cutoffs for upstaging BI-RADS 3 at 120 kPa and downstaging BI-RADS 4a at 90 kPa in 2D + 3D SWE reclassification, 123 (21.2%) participants underwent benign biopsy, resulting in a 54.1% reduction (123 versus 268). CONCLUSION: Combining 2D + 3D SWE with standard breast US for reclassification of BI-RADS lesions may achieve a reduction in benign biopsies in BI-RADS 4-5 lesions without sacrificing sensitivity unacceptably. CLINICAL RELEVANCE STATEMENT: Combining 2D + 3D SWE with US effectively reduces benign biopsies in breast lesions with categories 4-5, potentially improving diagnostic accuracy of BI-RADS assessment for patients with breast lesions. TRIAL REGISTRATION: ChiCTR1900026556 KEY POINTS: • Reduce benign biopsy is necessary in breast lesions with BI-RADS 4-5 category. • A reduction of 54.1% on benign biopsies in BI-RADS 4-5 lesions was achieved using 2D + 3D SWE reclassification. • Adding 2D + 3D SWE to standard breast US improved the diagnostic performance of BI-RADS assessment on breast lesions: specificity increased from 54 to 79%, and PPV increased from 54 to 71%, with slight loss in sensitivity (97.2% versus 98.7%) and NPV (98.1% versus 98.7%).

A Force-Matched Approach to Large-Strain Nonlinearity in Elasticity Imaging for Breast Lesion Characterization.

OBJECTIVE: Ultrasound elasticity imaging is a class of ultrasound techniques with applications that include the detection of malignancy in breast lesions. Although elasticity imaging traditionally assumes linear elasticity, the large strain elastic response of soft tissue is known to be nonlinear. This study evaluates the nonlinear response of breast lesions for the characterization of malignancy using force measurement and force-controlled compression during ultrasound imaging. METHODS: 54 patients were recruited for this study. A custom force-instrumented compression device was used to apply a controlled force during ultrasound imaging. Motion tracking derived strain was averaged over lesion or background ROIs and matched with compression force. The resulting force-matched strain was used for subsequent analysis and curve fitting. RESULTS: Greater median differences between malignant and benign lesions were observed at higher compressional forces (p-value < 0.05 for compressional forces of 2-6N). Of three candidate functions, a power law function produced the best fit to the force-matched strain. A statistically significant difference in the scaling parameter of the power function between malignant and benign lesions was observed (p-value = 0.025). CONCLUSIONS: We observed a greater separation in average lesion strain between malignant and benign lesions at large compression forces and demonstrated the characterization of this nonlinear effect using a power law model. Using this model, we were able to differentiate between malignant and benign breast lesions. SIGNIFICANCE: With further development, the proposed method to utilize the nonlinear elastic response of breast tissue has the potential for improving non-invasive lesion characterization for potential malignancy.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection Software for Automated Breast Ultrasound.

RATIONALE AND OBJECTIVES: This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS). MATERIALS AND METHODS: ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared. RESULTS: For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing. CONCLUSION: AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.

Automatic classification and prioritisation of actionable BI-RADS categories using natural language processing models.

AIM: To facilitate the routine tasks performed by radiologists in their evaluation of breast radiology reports, by enhancing the communication of relevant results to referring physicians via a natural language processing (NLP)-based system to classify and prioritise Breast Imaging Reporting Data System (BI-RADS). MATERIALS AND METHODS: A NLP-based system was developed to classify and prioritise BI-RADS categories from breast ultrasound and mammogram reports, with the potential to streamline and speed up the standard procedures that radiologists must follow to evaluate and categorise breast imaging results. BI-RADS category extraction was divided into two specific tasks: (1) multi-label classification of BI-RADS categories (0-6) and (2) classification of high-priority (BI-RADS 0, 3, 4 and 5) and low priority (BI-RADS 1, 2, and 6) reports according to the previous BI-RADS assessment. RESULTS: To develop the NLP tool, three different Bidirectional Encoder Representations from Transformers (BERT)-based models (XLM-RoBERTa, BETO, and Bio-BERT-Spanish) were trained and tested on three distinct corpora (containing only breast ultrasound reports, only mammogram reports, or both), and achieved an accuracy of 74.29-77.5% in detecting BI-RADS categories and 88.52-91.02% in prioritising reports. CONCLUSION: The system designed can effectively classify all BI-RADS categories present in a single radiology report. In the clinical setting, such an automated tool can assist radiologists in evaluating breast radiology reports and decision-making tasks and enhance the speed of communicating priority BI-RADS reports to referring physicians.

Combination of Breast Ultrasound With Magnetic Resonance Imaging in the Diagnosis of Non-mass-like Breast Lesions Detected on Ultrasound: A New Integrated Strategy to Improve Diagnostic Performance.

OBJECTIVE: The aim of the work described here was to evaluate the diagnostic performance of a new integrated strategy using breast ultrasound (US) combined with magnetic resonance imaging (MRI) to differentiate benign and malignant breast non-mass-like lesions (NMLs) detected on US. METHODS: From October 2017 to January 2021, 183 NMLs detected on US that had undergone MRI examinations were included in this respective study. Pathological results were used as the reference standard. The integrated diagnostic strategy of breast US combined with MRI based on a combination of MRI Breast Imaging Reporting and Data System (BI-RADS) with discriminant sonographic indicators highly associated with malignancy was established and validated in a cohort of 61 women. The diagnostic performances of US, MRI and the combined method were calculated and compared. RESULTS: In the training set, the area under the receiver operating characteristic curve (AUC), sensitivity and specificity of US, MRI and the integrated diagnostic strategy using US combined with MRI for NMLs were 0.730, 93.7% and 52.3%; 0.849, 94.7% and 75.0%; and 0.901, 92.6% and 87.5%, respectively. Compared with US or MRI alone, the integrated diagnostic strategy significantly increased the AUC (p < 0.001, p = 0.007) and specificity (p < 0.001, p = 0.034) while maintaining high sensitivity (p = 0.774, p = 0.551). In the validation set, the integrated strategy of US combined with MRI (AUC = 0.899) also had good performance compared with US (AUC = 0.728) or MRI (AUC = 0.838). CONCLUSION: The integrated diagnostic strategy of US combined with MRI exhibited good performance for breast NMLs compared with either modality used alone, which can improve the diagnostic specificity while maintaining high sensitivity.

Automated 3-D Ultrasound Elastography of the Breast: An In Vivo Validation Study.

OBJECTIVE: Studies have indicated that adding 2-D quasi-static elastography to B-mode ultrasound imaging improved the specificity for malignant lesion detection, as malignant lesions are often stiffer (increased strain ratio) compared with benign lesions. This method is limited by its user dependency and so unsuitable for breast screening. To overcome this limitation, we implemented quasi-static elastography in an automated breast volume scanner (ABVS), which is an operator-independent 3-D ultrasound system and is especially useful for screening women with dense breasts. The study aim was to investigate if 3-D quasi-static elastography implemented in a clinically used ABVS can discriminate between benign and malignant breast lesions. METHODS: Volumetric breast ultrasound radiofrequency data sets of 82 patients were acquired before and after automated transducer lifting. Lesions were annotated and strain was calculated using an in-house-developed strain algorithm. Two strain ratio types were calculated per lesion: using axial and maximal principal strain (i.e., strain in dominant direction). RESULTS: Forty-four lesions were detected: 9 carcinomas, 23 cysts and 12 other benign lesions. A significant difference was found between malignant (median: 1.7, range: [1.0-3.2]) and benign (1.0, [0.6-1.9]) using maximal principal strain ratios. Axial strain ratio did not reveal a significant difference between benign (0.6, [-12.7 to 4.9]) and malignant lesions (0.8, [-3.5 to 5.1]). CONCLUSION: Three-dimensional strain imaging was successfully implemented on a clinically used ABVS to obtain, visualize and analyze in vivo strain images in three dimensions. Results revealed that maximal principal strain ratios are significantly increased in malignant compared with benign lesions.

Value of inversion imaging to diagnosis in differentiating malignant from benign breast masses.

BACKGROUND: We aimed to evaluate the added value of inversion imaging in differentiating between benign and malignant breast masses when combined with the Breast Imaging Reporting and Data System (BI-RADS). METHODS: A total of 364 patients with 367 breast masses (151 benign and 216 malignant) who underwent conventional ultrasound and inversion imaging prior to breast surgery were included. A 5-point inversion score (IS) scale was proposed based on the masses' internal echogenicity and distribution characteristics in the inversion images. The combination of IS and BI-RADS was compared with BI-RADS alone to evaluate the value of inversion imaging for breast mass diagnosis. The diagnostic performance of the BI-RADS and its combination with IS for breast masses were analyzed using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The IS for malignant breast masses (3.96 ± 0.77) was significantly higher than benign masses (2.58 ± 0.98) (P < 0.001). The sensitivity, specificity, accuracy, PPV, and NPV of BI-RADS were 86.1%, 81.5%, 84.2%, 86.9%, and 80.4%, respectively, and an AUC was 0.909. By compared with BI-RADS, 72 breast masses were downgraded from suspected malignancy to benign, and 6 masses were upgraded from benign to suspected malignancy. Thus, the specificity was increased from 81.5 to 84.8%, it allows 72 benign masses avoid biopsy. CONCLUSION: The combination of inversion imaging with BI-RADS can effectively improve the diagnostic efficacy of breast masses, and inversion imaging could help benign masses avoid biopsy.