Differential Diagnosis Of Breast Lesions Research Articles

Assessment of the contribution of the ADC value to the Kaiser score in the differential diagnosis of breast lesions with non-mass enhancement morphology on MRI

PurposeTo investigate the effectiveness of diffusion-weighted imaging (DWI) as a supplementary tool to the Kaiser score (KS) in diagnosing breast cancer in non-mass enhancement (NME) lesions using breast magnetic resonance imaging (MRI). MethodsThis single-center, retrospective study analyzed 360 cases with NME on MRI images. Two breast radiologists independently evaluated each lesion using the Kaiser score (KS) and apparent diffusion coefficient (ADC) values, without knowledge of the pathological outcomes. NME lesions with a KS above 4 and an ADC value below 1.3 × 10-3mm2/s were classified as malignant. Inter-rater reliability was determined using Cohen’s Kappa (κ) statistics. The diagnostic performance of KS, DWI, and their combination was assessed by calculating sensitivity, specificity, and the area under the curve (AUC), and the results were compared across the benign and malignant groups. ResultsThe diagnostic performance of KS surpassed that of DWI in predicting the malignancy of NMEs (p = 0.003). The sensitivity of KS alone was 93 %; however, when ADC data was incorporated, the sensitivity decreased to 86 %, with no significant difference observed (p = 0.060). The specificity of the combined KS and ADC (94 %) was significantly higher than that of KS alone (89 %) and DWI alone (73 %) (p < 0.001). ConclusionOur findings indicated that although the combination of KS and ADC increased specificity and reduced unnecessary biopsies, the resulting decrease in sensitivity was unacceptable. Therefore, KS alone is superior to the KS-ADC combination in detecting malignancy in NME lesions.

Meta-analysis of contrast-enhanced ultrasound in differential diagnosis of breast adenosis and breast cancer.

This systematic review and meta-analysis study aimed to determine the total capacity of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of breast lesions and breast cancer. For collecting papers, four groups of keywords were searched in five databases. The required information was extracted from the selected papers. In addition to the descriptive findings, a meta-analysis was also conducted. Thirty-three of thirty-six studies (91.67%) on the differential diagnosis of various degrees and types of breast lesions showed that CEUS has proper performance. The pooled values related to the sensitivity and specificity of CEUS were computed by 88.00 and 76.17.

Application of the Kaiser score on contrast-enhanced mammography in the differential diagnosis of breast lesions: comparison with breast magnetic resonance imaging.

The Kaiser score (KS) as a clinical decision rule has been proven capable of enhancing the diagnostic efficiency for suspicious breast lesions and obviating unnecessary benign biopsies. However, the consistency of KS in contrast-enhanced mammography (CEM-KS) and KS on magnetic resonance imaging (MRI-KS) is still unclear. This study aimed to evaluate and compare the diagnostic efficacy and agreement of CEM-KS and MRI-KS for suspicious breast lesions. This retrospective study included 207 patients from April 2019 to June 2022. The radiologists assigned a diagnostic category to all lesions using the Breast Imaging Reporting and Data System (BI-RADS). Subsequently, they were asked to assign a final diagnostic category for each lesion according to the KS. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The agreement in terms of the kinetic curve and the KS categories for CEM and MRI were evaluated via the Cohen kappa coefficient. The AUC was higher for the CEM-KS category assignment than for the CEM-BI-RADS category assignment (0.856 vs. 0.776; P=0.047). The AUC was higher for MRI-KS than for MRI-BI-RADS (0.841 vs. 0.752; P =0.015). The AUC of CEM-KS was not significantly different from that of MRI-KS (0.856 vs. 0.841; P=0.538). The difference between the AUCs for CEM-BI-RADS and MRI-BI-RADS was not statistically significant (0.776 vs. 0.752; P=0.400). The kappa agreement for the characterization of suspicious breast lesions using CEM-KS and MRI-KS was 0.885. The KS substantially improved the diagnostic performance of suspicious breast lesions, not only in MRI but also in CEM. CEM-KS and MRI-KS showed similar diagnostic performance and almost perfect agreement for the characterization of suspicious breast lesions. Therefore, CEM holds promise as an alternative when breast MRI is not available or contraindicated.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging

BackgroundTo compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue.MethodsThis prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model.ResultsAlmost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively).ConclusionsQuantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

BackgroundThis study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions.MethodsThis retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADCall b, mADC0–1000), BE (mD, mD*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance.ResultsRF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04).ConclusionsThe radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.

Quantitative Evaluation for Differential Diagnosis of Breast Lesions in Diffusion-Weighted MR Imaging

Diffusion-weighted MR Imaging is a rapidly emerging technique, that allows in-vivo mapping processes of the water diffusion in tissues. It has the potential capabilities for clinical application in breast imaging. The aim of this study was to find out the optimal b-value for calculation of ADC value for differential diagnosis of breast lesions. A total of 124 subjects (mean age 46 years) with 141 lesions were included. The protocol consists of axial T2 sequence for lesion localization and measurement and DW sequence with three sets of b-values of 0, 300, 600, and 1000 s/mm2. The mean ADC values of the breast lesions for b-values (0, 300, 600, and 1000) were 1.75 ± 0.18 × 10−3mm2/sec, 1.66 ± 0.12 × 10−3mm2/sec and 1.57 ± 0.15 × 10−3mm2/sec for the benign lesions and 1.26 ± 0.048 × 10−3mm2/sec, 1.14 ± 0.11 × 10−3mm2/sec and 0.93 ± 0.14 × 10−3mm2/sec for malignant lesions respectively. Statistical significant differences were noted on the ADC value of benign and malignant lesions among the three sets of b values (p = 0.001). ADC values of malignant lesion was significantly lower compared to benign lesions. The AUC (0.998) was substantially large for b-value of 0,600 s/mm2 with a threshold ADC cut off value of 1.28 × 10−3mm2/sec with 98.4% sensitivity, 93.2% specificity and 98.5% positive predictive value(PPV). In conclusion, diffusion weighted imaging has the ability for differential diagnosis of breast lesions with the optimal b value of 0,600 s/ mm2. DWI is a reliable tool for characterising breast lesions and may increase the overall specificity of breast MRI.

The Added Value of a Computer-Aided Diagnosis System in Differential Diagnosis of Breast Lesions by Radiologists With Different Experience.

To evaluate the value of the computer-aided diagnosis system, S-Detect (based on deep learning algorithm), in distinguishing benign and malignant breast masses and reducing unnecessary biopsy based on the experience of radiologists. From February 2018 to March 2019, 266 breast masses in 192 women were included in our study. Ultrasound (US) examination, including S-Detect technique, was performed by the radiologist with about 10 years of clinical experience in breast US imaging. US images were analyzed by four other radiologists with different experience in breast imaging (radiologists 1, 2, 3, and 4 with 1, 4, 9, and 20 years, respectively) according to their clinical experience (with and without the results of S-Detect). Diagnostic capabilities and unnecessary biopsy of radiologists and radiologists combined with S-Detect were compared and analyzed. After referring to the results of S-Detect, the changes made by less experienced radiologists were greater than experienced radiologists (benign or malignant, 44 vs 22 vs 14 vs 2; unnecessary biopsy, 34 vs 25 vs 10 vs 5). When combined with S-Detect, less experienced radiologists showed significant improvement in accuracy, specificity, positive predictive value, negative predictive value, and area under curve (P < .05), but not for experienced radiologists (P > .05). Similarly, the unnecessary biopsy rate of less experienced radiologists decreased significantly (44.4% vs 32.7%, P=.006; 36.8% vs 28.2%, P=.033), but not for experienced radiologists (P > .05). Less experienced radiologists rely more on S-Detect software. And S-Detect can be an effective decision-making tool for breast US, especially for less experienced radiologists.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PurposeWe sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis. MethodsWe comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis. ResultsWe analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86–0.88) and 0.80 (0.78–0.81), respectively. The PLR was 5.09 (4.16–6.24); the NLR was 0.15 (0.13–0.18); and the DOR was 38.95 (28.87–52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82–0.88) and 0.87(0.83–0.90), respectively; the PLR was 5.65 (3.91–8.18); the NLR was 0.17 (0.12–0.26); and the DOR was 38.44 (23.57–62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I2>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*. ConclusionsOur meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

Differential Diagnosis of Breast Lesions in Dual-Energy Contrast-Enhanced Spectral Mammography

Background: Dual-energy contrast-enhanced spectral mammography (CESM) is one of the latest methods for breast lesions characterization, where structural and functional (i.e., vascularization) assessment are combined. Nowadays an interpretation of contrast-enhanced images is based only on the degree of contrast enhancement, but we propose a more detailed assessment of the structure of the hypervascular lesions by highlighting the contrast enhancement patterns. Purpose: To evaluate the diagnostic performance of contrast-enhanced spectral mammography (CESM) using the contrast enhancement patterns in malignant and benign lesions. Material and Methods: Study included 332 women examined from February 2018 to June 2020. The mean age of the women was 50 years. Of 428 lesions totally revealed, 172 (40.2%) were histologically verified as malignant and 256 (59.8%) as benign. We proposed 9 types of contrast enhancement patterns to describe lesions: reticular, granular, annular, diffuse-spherical, lacunar, cloud-like, heterogeneous-annular, point, cotton-like. Results: We showed that diagnostic performance of CESM increased sensitivity if an additional diagnostic feature of contrast enhancement pattern was used: sensitivity increased from 79.7% to 94.8% (p = 0.26), specificity from 82.4% to 95.3% (p = 0.013) and accuracy from 81.3 to 95.1% (p = 0.004), in comparison with using of only one feature of contrast enhancement intensity in the differential diagnosis of malignant and benign lesions. Conclusion: Thus, using contrast enhancement pattern allows to increase the efficiency of CESM in breast cancer detection.

Contrast-enhanced spectral mammography without and with a delayed image for diagnosing malignancy among mass lesions in dense breast.

Aim of the studyTo analyse the diagnostic performance of contrast-enhanced spectral mammography (CESM) based on morphologic and enhancement patterns of mass lesions in dense breast using different protocols: CESM without delayed image and CESM with delayed image.Material and methodsA total of 151 informed women with suspicious for malignancy mass lesions in dense breast were included in this study. All of them underwent CESM using 2 protocols. A total of 155 lesions were pathomorphologically verified. We analysed morphologic patterns on low-energy (LE) images and recombined images (RI) by defining the shape, margin, and dynamic patterns based on delayed images.ResultsThe comparative analysis revealed that the shape and margins on RI were more significant than those on LE images. The dynamic indicators of CESM were found to be highly significant in dense breasts. The correlation between kinetic curve and histological results demonstrated that a persistent type of curve was common for benign lesions, accounting for 15/22 (68.1%); plateau and washout – for malignant lesions, accounting for 24/89 (26.9%) and 61/89 (68.5%), respectively. Delayed image leads to an increase of specificity up to 12.4%, which is statistically significant. The area under the curve (AUC) in CESM with delayed image is larger than that in CESM without delayed image (p < 0.01).ConclusionsCESM is sensitive for the differential diagnosis of breast lesions. CESM with delayed image has higher specificity than CESM without delayed image. Delayed images with plateau and washout are typical for malignancy.

The effectiveness of multiparametric ultrasound and compressive elastography in the early diagnosis of breast cancer

Modern ultrasound examination (US), unlike other modalities, allows to assess the stiffness of the lesions, with the help of a new technology named sonoelastography (SEG). According to many scientific studies, with the addition of SEG to the B-mode, the diagnostic efficiency of ultrasound of breast lesions significantly increases. According to Thomas et al. B-mode has a sensitivity of 91,8% and a specificity of 78%. When a compression SEG is added to a diagnostic study, the specificity increases to 91,5%. The purpose of this study was to determine the diagnostic effectiveness of SEG and compare it with the effectiveness of B-mode and color Doppler mapping (CDC) in the primary diagnosis of breast tumors, as well as to identify diagnostically and statistically significant SEG signs of breast cancer ( ВС ). Materials and methods . The study was performed from 2017 to 2019 on the basis of the «NMRC Oncology. named after N. N. Petrov» Ministry of Health of Russia. 277 women with complaints of lump or pain in the breast were included in the study, for all of them there was performed the multiparametric ultrasound examination with application of CDM and SEG. According to our own results, SEG is more effective at detection of malignant and benign lesions, than ultrasoumd examination in B and CDM modes. Results: 0, 1, 2, 3 elastotypes are the highly significant diagnostic signs of benign process, and the elastotype 5 is a reliable diagnostic marker of malignant process. The effectiveness of CDM and SEG in the differential diagnosis of breast lesions showed: sensitivity — 77,6% vs. 85,5%; specificity — 51,7% vs. 84,6%; accuracy — 58,8% vs. 84,8%. Conclusions: according to our own research results, SEG is more effective in detecting malignant and benign tumors than ultrasound in B and CDM modes.

Shear-Wave Elastography of the Breast: Added Value of a Quality Map in Diagnosis and Prediction of the Biological Characteristics of Breast Cancer.

ObjectiveTo determine the added value of a shear-wave elastography (SWE) quality map (QM) in the diagnosis of breast lesions and in predicting the biological characteristics of invasive breast cancer.Materials and MethodsBetween January 2016 and February 2019, this study included 368 women with 368 pathologically proven breast lesions, which appeared as poor-quality regions in the QM of SWE. To measure shear-wave velocity (SWV), seven regions of interest were placed in each lesion with and without QM guidance. Under QM guidance, poor-quality areas were avoided. Diagnostic performance was calculated for mean SWV (SWVmean), max SWV (SWVmax), and standard deviation (SD) with QM guidance (SWVmean + QM, SWVmax + QM, and SD + QM, respectively) and without QM guidance (SWVmean − QM, SWVmax − QM, and SD − QM, respectively). For invasive cancers, the relationship between SWV findings and biological characteristics was investigated with and without QM guidance.ResultsOf the 368 women (mean age, 47 years; SD, 10.8 years) enrolled, 159 had benign breast lesions and 209 had malignant breast lesions. SWVmean + QM (3.6 ± 1.39 m/s) and SD + QM (1.02 ± 0.84) were significantly different from SWVmean − QM (3.29 ± 1.22 m/s) and SD − QM (1.46 ± 1.06), respectively (all p < 0.001). For differential diagnosis of breast lesions, the sensitivity and areas under the receiver operating characteristic curve (AUC) of SWVmean + QM (sensitivity: 89%; AUC: 0.932) were better than those of SWVmean − QM (sensitivity, 84.2%; AUC, 0.912) (all p < 0.05). There was no significant difference in sensitivity and specificity between SD + QM and SD − QM (all p = 1.000). Among the biological characteristics of invasive cancers, lymphovascular involvement, axillary lymph node metastasis, negative estrogen receptor status, negative progesterone receptor status, positive human epidermal growth factor receptor status, and aggressive molecular subtypes showed higher SWVmean + QM (all p < 0.05), while only lymphovascular involvement showed higher SWVmean − QM (p = 0.036).ConclusionThe use of QM in SWE might improve the diagnostic performance for breast lesions and facilitate prediction of the biological characteristics of invasive breast cancers.

Ультразвуковое исследование с контрастированием в дифференциальной диагностике узловых образований молочной железы

Ультразвуковое исследование с контрастированием в дифференциальной диагностике узловых образований молочной железы

Added Value of Different Types of Elastography in Evaluating Ultrasonography Detected Breast Lesions: A Compared Study With Mammography

BackgroundThe purpose of this study was to compare the diagnostic performance of ultrasonography (US) and mammography in the differential diagnosis of breast lesions after adding different types of elastography to US. Patients and MethodsThis institutional review board-approved study included 316 breast lesions in 289 women between July 2016 and July 2018. All these lesions were evaluated with conventional US, elastography, and mammography before biopsy or surgery. Elastography, including elasticity imaging (EI), virtual touch tissue imaging (VTI), and virtual touch imaging quantification (VTIQ), were used to downgrade US Breast Imaging-Reporting and Data System category 4A lesions. Diagnostic performances were calculated for mammography, US elastography, and the combination of US and elastography. ResultsThe sensitivity of US (100%) was significantly higher than that of mammography (84.6%; P < .001), but the specificity of US (14.5%) was significantly lower than that of mammography (59.1%; P < .001). After adding EI, VTI, and VTIQ to US, the specificity was significantly increased from 14.5% to 69.4%, 72.6%, and 78.0%, respectively (P < .001), and were significantly higher than that of mammography (P = .043, P = .006, and P < .001, respectively). The sensitivity of US + EI (96.2%) and US + VTI (96.2%) was lower than that of US alone, although not significantly (100%; P = .063 and P = .063, respectively). ConclusionThe addition of different types of elastography to US improved the diagnostic performance in the differential diagnosis of breast lesions when compared with mammography.

Brightness Mode and Color Doppler Ultrasound in Differential Diagnosis of Breast Lesions in Saudi Females.

Objective:The aim of the study was to identify the pathological characteristics of benign and malignant breast lesions among Saudi females using brightness mode (B-mode) and color Doppler ultrasound (US).Materials and Methods:This study was retrospectively carried out in a single center in the Radiology and Medical Imaging Department, King Fahad Medical City, Riyadh, Saudi Arabia. A convenient method of sampling was used to include all patients referred for different diagnosis during the period of January 2016 and December 2018. A sample size of 100 cases was selected with 50% of the cases being benign breast lesions, while the rest were malignant. The data collection instruments comprised data collection sheets, while a Philips US system with a 9 MHz linear probe was used to give the differential results. The results were considered significant when P < 0.05. The statistical diagnostic test was used to detect sensitivity, specificity, and accuracy of US in the differential diagnosis of breast lesions in Saudi females.Results:B-mode and color Doppler US findings of breast mass measurements, shape, echotexture, and the presence and absence of vascularity present a sensitivity, specificity, and accuracy of 97.09%, 80.65%, and 93.28% in the diagnosis of benign and malignant breast masses.Conclusion:In Saudi females with dense breasts, the risk of breast cancer development is increased. Moreover, B-mode in combination with color Doppler US was highly determined the results of differential diagnosis for any breast lesions.

Thymidylate Synthase, a New Myoepithelial Biomarker for Breast Lesions.

Background. The identification of myoepithelial cells (MECs) can facilitate the differential diagnosis of breast lesions. We previously found thymidylate synthase (TS) expression in the nuclei of MECs in breast tissues, which prompted us to investigate the usefulness of TS as a sensitive and specific biomarker in the differential diagnosis of breast lesions, similar to other MEC biomarkers (ie, tumor protein [P63] and cluster of differentiation 10 [CD10]). Methods. Immunohistochemistry for TS, P63, and CD10 was performed on paraffin sections from 189 breast specimens. Results. The results showed the intensity of the immunoreactive TS signal to be comparable with that of P63 in the nuclei of MECs. Furthermore, the nuclei of MECs stained strongly for TS and P63 in normal breast tissues (obtained adjacent to invasive breast lesions), benign breast lesions, and carcinoma in situ, whereas the cytoplasm of MECs stained strongly for CD10. The immunoreactive TS signal in the cytoplasm of MECs was variable in 22 out of 32 (65.6%) cases of invasive breast carcinoma and 4 out of 20 cases (20.0%) of ductal carcinoma in situ. We found no immunoreactive TS signal in the nuclei of luminal and stromal cells in breast lesions, although there was a weak positive signal in the cytoplasm of luminal and stromal cells. Conclusions. TS is a sensitive and specific MEC biomarker in the differential diagnosis of breast lesions.

Shear Wave Elastography of Breast Lesions: Quantitative Analysis of Elastic Heterogeneity Improves Diagnostic Performance

The aim of this study was to evaluate whether quantitative analysis of elastic heterogeneity (EH) could improve the diagnostic performance of shear wave elastography (SWE) in breast lesions. From August 2016 to August 2017, 280 patients were enrolled in this prospective study. All lesions were evaluated with the ultrasound Breast Imaging Reporting and Data System (BI-RADS) and SWE with Virtual Touch tissue imaging quantification. The shear wave velocity (SWV) of the three areas of highest stiffness and lowest stiffness within the lesions were measured to calculate the maximum SWV (SWVmax), mean SWV (SWVmean) and EH. The EH was determined as the difference between the averaged highest SWV and lowest SWV. The diagnostic performance—including the area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity of BI-RADS, EH, SWVmax and SWVmean—were analyzed. The AUC of EH, SWVmax and SWVmean were 0.963, 0.949 and 0.937, respectively. The sensitivity of EH was 93.75%, which was significantly higher than that of SWVmax (84.37%) and SWVmean (84.37%) (p < 0.001); there was no significant difference in the specificity among EH, SWVmax and SWVmean (p > 0.05). For category 4A lesions, EH predicted all the malignant lesions, while two cancers were misdiagnosed by SWVmax and SWVmean, respectively. Quantitative analysis of EH can improve the sensitivity of SWE for the differential diagnosis of breast lesions without loss of specificity.

The value of virtual touch tissue imaging quantification in the differential diagnosis between benign and malignant breast lesions.

To evaluate the value and diagnostic performance of virtual touch tissue imaging quantification (VTIQ) and to determine the optimum cut-off value for differential diagnosis between benign and malignant breast lesions. Conventional ultrasonography (US) and VTIQ were performed in 454 patients with 466 breast lesions with a Siemens Acuson S3000 ultrasound machine. All lesions were assessed by an ultrasound Breast Imaging Reporting and Data System (BI-RADS) and confirmed by histopathology. The maximum, mean, and minimum shear wave velocity (SWV) values were quantitatively measured in m/s within the regions of interest (ROIs) and ranged from 0.5 to 10m/s. The sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC) of the VTIQ, BI-RADS, and combined data were compared. Among the 466 breast lesions, 266 were benign and 200 were malignant. All of the SWV values of the malignant lesions were significantly greater than those of the benign ones (P < 0.05). The optimal cut-off values for SWVmax, SWVmin, SWVmean, and SWVmax/SWVmin obtained from ROC analysis were 5.37m/s, 3.08m/s, 4.04m/s, and 1.83, respectively. Logistic regression analysis revealed that BI-RADS was an independent risk factor for the differential diagnosis of breast lesions, whereas SWV values were not independent risk factors. VTIQ is useful in the differential diagnosis between benign and malignant breast lesions. The combination of VTIQ and ultrasonic BI-RADS can improve the diagnostic performance.

Qualitative analysis of contrast-enhanced ultrasound in differential diagnosis of breast lesions

The objective . Revelation of informative markers of qualitative analysis CEUS in the differential diagnosis of breast lesions, determination of effectiveness CEUS in the breast cancer examination. Materials and methods . The work is based on the results of a retrospective comparison ultrasound data with the histological results conducted in 145 women with breast lesions at the age of 17-71 years (average age was 45.5 years) from December 2017 until November 2018 year. Results. Statistical analysis of markers of qualitative assessment CEUS showed that more important markers in the differential diagnosis were - indicators of definition of the boundaries of lesion during CEUS (Se 79,27 Sp 63,41); the velocity of accumulation of contrast agent (Se 78,05 Sp 80,95); wash-out velocity from lesion (Se 68,29 Sp 95,24). Conclusion . Qualitative data analysis of CEUS provides to detail of breast lesions and surround tissues vascular architecture, improves differential diagnosis of breast tumors, which allows to reduce the number of additional methods of examination and biopsies, predict the volume of surgical and complex treatment.