Breast Imaging Reporting And Data System Category Research Articles

A multiparametric classification system for lesions detected by breast magnetic resonance imaging

BackgroundTo validate a new categorisation scheme for suspicious breast lesions according to the well-defined Breast Imaging Reporting and Data System (BI-RADS) magnetic resonance imaging (MRI) lexicon descriptors, apparent diffusion coefficients (ADC), T2-weighted signal intensity (SI), and kinetic curve assessment categories. MethodsThe MRI descriptors and ADC were analysed in 697 lesions detected in 499 subjects. The descriptors were classified into Minor, Intermediate, and Major findings, and were divided into the BI-RADS subcategories 3, 4A, 4B, 4C, and 5 according to the number of descriptors. Positive predictive values (PPV) were calculated for each descriptor. The descriptors were then fitted into a multinomial logistic regression model to determine the odds ratio for a malignant diagnosis. The PPV were measured for the new categories and compared with the assigned PPV of the BI-RADS descriptors. ResultsThe PPV for MRI descriptors ranged from 17.9%–100%. Of the 697 lesions assessed, 19 (2.7 %) were categorized as BI-RADS 3, 27 (3.9 %) as 4A, 53 (7.6 %) as 4B, 174 (25.0 %) as 4C, and 424 (60.8 %) as 5. None of the subjects in BI-RADS category 3 had a malignant diagnosis. The PPV for malignancy increased progressively with increasing BI-RADS category (4A, 11.1 %; 4B, 28.3 %; 4C, 64.4 %; 5, 94.8 %). All descriptor groups were significant in the logistic regression model. ConclusionsThis study shows that using BI-RADS MRI descriptors together with ADC and T2-weighted SI in a multiparametric classification system can yield an applicable categorisation of lesions with PPV values within the recommended ranges for BI-RADS categories.

Value of quantitative sound touch elastography of tissues around breast lesions in the evaluation of malignancy

To assess the value of quantitative analysis of sound touch elastography of tissues around breast lesions to facilitate the evaluation of malignancy of the lesions. With the permission of the Ethics Committee, every patient signed informed consent forms before the study. One hundred and eighty-two solid breast lesions were analysed retrospectively. Postoperative histopathology proved that 63 lesions were malignant and 119 were benign. All lesions were examined by two-dimensional ultrasonography, colour Doppler ultrasonography and ultrasound elastography including sound touch elastography (STE) and strain elastography. Using pathological diagnosis as the reference, the correlation between each ultrasound marker and the malignancy of the solid breast masses was evaluated by chi-square test, and the logistic regression model was constructed to determine the best diagnostic model with multiple markers. The areas under the receiver operating characteristic (ROC) curve (AUCs) of various elastography markers were compared and the markers with the largest AUC values, including quantitative, semi-quantitative, and distance markers were identified. Logistic regression analysis showed that the combination of accuracy of Breast Imaging Reporting and Data System (BI-RADS) classification + age + maximum elasticity value of the tissue around the lesion (EMax_shell) in predicting malignant lesions was higher than that of the other combinations. The prediction model verified that the sensitivity of diagnosis of the mammary lump was 94.12% and the specificity was 84.13%. EMax_shell in the elasticity is the most valuable marker for the diagnosis of breast cancer, and age combined with EMax_shell can effectively improve the diagnostic efficacy of the BI-RADS classification in breast cancer.

Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy

The purpose of this study was to develop, validate and test a prediction model for discriminating malignant from benign breast lesions using conventional ultrasound (US), US elastography of strain elastography and contrast-enhanced ultrasound (CEUS). The study included 454 patients with breast imaging-reporting and data system (BI-RADS) category 4 breast lesions identified on histologic examinations. Firstly, 228 breast lesions (cohort 1) were analyzed by logistic regression analysis to identify the risk factors, and a breast malignancy prediction model was created. Secondly, the prediction model was validated in cohort 2 (84 patients) and tested in cohort 3 (142 patients) by using analysis of the area under the receiver operating characteristic curve (AUC). Univariate regression indicated that age ≥40 y, taller than wide shape on US, early hyperenhancement on CEUS and enlargement of enhancement area on CEUS were independent risk factors for breast malignancy (all p < 0.05). The logistic regression equation was established as follows: p = 1/1+Exp∑[–5.066 + 3.125 x (if age ≥40 y) + 1.943 x (if taller than wide shape) + 1.479 x (if early hyperenhancement) + 4.167 x (if enlargement of enhancement area). The prediction model showed good discrimination performance with an AUC of 0.967 in cohort 1, 0.948 in cohort 2 and 0.920 in cohort 3. By using the prediction model to selectively downgrade category 4a lesions, the re-rated BI-RADS yield an AUC of 0.880 (95% confidence interval [CI], 0.794–0.965) in cohort 2 and 0.870 (95% CI, 0.801–0.939) in cohort 3. The specificity increased from 0.0% (0/35) to 80.0% (28/35) without loss of sensitivity (from 100.0% to 95.9%, p = 0.153) in cohort 2. Similarly, the specificity increased from 0.0% (0/58) to 77.6% (45/58) without loss of sensitivity (from 100.0% to 96.4%, p = 0.081) in cohort 3. Multimodal US showed good diagnostic performance in predicting breast malignancy of BI-RADS category 4 lesions. Although the loss of sensitivity was existing, the addition of multimodal US to US BI-RADS could improve the specificity in BI-RADS category 4 lesions, which reduced unnecessary biopsies.

Diagnostic values of shear wave elastography and strain elastography for breast lesions

Strain elastography (SE) and shear wave elastography (SWE) have high diagnostic yield for breast lesions, but the optimal parameters remain elusive. To evaluate the diagnostic yield of SWE and SE for breast lesions by multivariate logistic regression analysis. A total of 132 patients with 164 breast tumors were enrolled. Breast lesions were classified with the breast imaging reporting and data system (BI-RADS). Maximum (Emax), mean (Emean) and standard deviation (Esd) of elastic modulus, lesion/fat elasticity ratio and elastographic classification were obtained by SWE. Strain ratio (SR) and elastographic score were obtained by SE. A multivariate logistic regression analysis was performed. The diagnostic efficiencies of BI-RADS classification, SWE, SE and their combination were compared plotting ROC curves. There were 110 benign and 54 malignant lesions which had significantly different SWE and SE parameters. The parameters included in the logistic regression were Esd and elastographic classification obtained by SWE and the elastographic score obtained by SE. When combining SWE with SE, Esd, SR and SWE classification were included in the equation. The areas under ROC curves for BI-RADS classification, SWE, SE and their combination were 0.75, 0.88, 0.79 and 0.89, respectively. The diagnostic value of SWE in combination with SE for breast lesions exceeded that of SE or SWE alone. Esd showed a good diagnostic yield when SWE was used alone or combined with SE.

Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics.

To examine the relationship between ultrasonic findings and positive expression of Ki67 and P53 in breast cancer. Surgical resection specimens of 263 breast cancer lesions were examined. Ultrasound examination and pathological examination were performed on each lesion for retrospective analysis. We applied regression analysis to the ultrasonic features related to the positive expression of Ki67 and P53 and obtained the corresponding models. To analyze diagnostic efficiency, we calculated the area under the curve (AUC). Additionally, we created a heat map to show the results of the cluster analysis. Lesions with higher Ki67 expression were associated with posterior acoustic enhancement, absence of an echo halo and a higher Breast Imaging Reporting and Data System (BI-RADS) category. P53-positive cancer were associated with an absence of an echo halo and a higher BI-RADS category. The AUC of the regression models of Ki67 and P53 was 0.78 and 0.71, respectively. Our study revealed that breast cancer ultrasonic findings were closely related to expression of molecular indicators, suggesting that ultrasound can be used to provide useful information to clinicians.

Digital Breast Tomosynthesis Versus Additional Diagnostic Mammographic Views for the Evaluation of Asymmetric Mammographic Densities.

IntroductionMany young females present with an advanced stage of breast cancer, which has a negative effect on the prognosis. Digital breast tomosynthesis is a new emerging imaging technique that aids in improving the specificity of mammography with subsequent early detection of breast cancer, especially in women with radiographically dense breasts. Tomosynthesis is subjectively preferred to conventional mammography and may offer superior diagnostic accuracy for the evaluation of breast lesions.MethodTwo breast radiologists retrospectively reviewed asymmetric densities using protocols that were institutional review board-approved in 185 patients aged 18 - 70 years (mean: 48 years) who underwent diagnostic mammography and tomosynthesis. Each asymmetric density was interpreted once with tomosynthesis and once with supplemental mammographic views; both modes included the mediolateral oblique and craniocaudal views in a fully crossed and balanced design by using a five-category Breast Imaging Reporting and Data System (BI-RADS) assessment and a probability-of-malignancy score. If the abnormality persisted and appeared benign or completely disappeared on both modalities, the agreement between additional views and tomosynthesis was determined by calculating Kappa value. If there was a discrepancy between additional views and tomosynthesis, the abnormality was subjected to ultrasound. In our study, 89 asymmetric densities were subjected to ultrasound.ResultsIn a total of 182 cases, 84 (46.15%) were categorized as BIRADS-0; 97 (53.30 %) as BIRADS-I, and one (0.55 %) as BIRADS-II on an additional view. Among the asymmetric densities categorized as BIRADS-0 on additional mammography views, digital breast tomosynthesis categorized 72, six, five, and one patient as BIRADS-0, BIRADS-I, BIRADS-II, and BIRADS-IV, respectively. For densities categorized as BIRADS-I (97) on additional view, digital breast tomosynthesis categorized 10 and 87 densities as BIRADS-0 and BIRADS-I, respectively.No change in the BIRADS category was observed among BIRADS-II and BIRADS-IV.A significant difference was observed with the chi-square test among BIRADS categories assigned by an additional view and digital breast tomosynthesis with a p-value of < 0.001. There was, however, a substantial agreement among additional views and tomosynthesis with a kappa value of 0.767.ConclusionOur study results suggest that tomosynthesis may be equivalent to, if not more equivalent to, additional imaging in the assessment of mammographically-detected asymmetric densities, thus improving BI-RADS classification and patient management.

Reader characteristics and mammogram features associated with breast imaging reporting scores.

This study aims to explore the reading performances of radiologists in detecting cancers on mammograms using Tabar Breast Imaging Reporting and Data System (BIRADS) classification and identify factors related to breast imaging reporting scores. 117 readings of five different mammogram test sets with each set containing 20 cancer and 40 normal cases were performed by Australian radiologists. Each radiologist evaluated the mammograms using the BIRADS lexicon with category 1 - negative, category 2 - benign findings, category 3 - equivocal findings (Recall), category 4 - suspicious findings (Recall), and category 5 - highly suggestive of malignant findings (Recall). Performance metrics (true positive, false positive, true negative, and false negative) were calculated for each radiologist and the distribution of reporting categories was analyzed in reader-based and case-based groups. The association of reader characteristics and case features among categories was examined using Mann-Whitney U and Kruskal-Wallis tests. 38% of cancer-containing mammograms were reported with category 3 which decreased to 32.3% with category 4 and 16.2% with category 5 while 16.6 and 10.3% of cancer cases were marked with categories 1 and 2. Female readers had less false-negative rates when using categories 1 and 2 for cancer cases than male readers (p < 0.01). A similar pattern as gender category was also found in Breast Screen readers and readers completed breast reading fellowships compared with non-Breast Screen and non-fellowship readers (p < 0.05). Radiologists with low number of cases read per week were more likely to record the cancer cases with category 4 while the ones with high number of cases were with category 3 (p < 0.01). Discrete mass and asymmetric density were the two types of abnormalities reported mostly as equivocal findings with category 3 (47-50%; p = 0.005) while spiculated mass or stellate lesions were mostly selected as highly suggestive of malignancy with category 5 (26%, p = 0.001). Most radiologists used category 3 when reporting cancer mammograms. Gender, working for BreastScreen, fellowship completion, and number of cases read per week were factors associated with scoring selection. Radiologists reported higher Tabar BIRADS category for specific types of abnormalities on mammograms than others. The study identified factors associated with the decision of radiologists in assigning a BIRADS Tabar score for mammograms with abnormality. These findings will be useful for individual training programs to improve the confidence of radiologists in recognizing abnormal lesions on screening mammograms.

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

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. 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. 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. 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.

The diagnostic value of MRI for architectural distortion categorized as BI-RADS category 3-4 by mammography.

Architectural distortion is a common mammographic sign that can be benign or malignant. This study investigated the diagnostic value of magnetic resonance imaging (MRI) for architectural distortions that were category 3-4 under the breast imaging reporting and data system (BI-RADS) by mammography. We retrospectively analyzed 219 pathologically confirmed lesions in 208 patients who had BI-RADS category 3-4 architectural distortion in mammography images. Two radiologists described and categorized the architectural distortion and assigned the BI-RADS categories to the corresponding lesions on MRI images. Using the postoperative pathological diagnosis as the gold standard, we performed receiver operating characteristic (ROC) analysis for the efficacy of mammography and MRI in differentiating patients with benign or malignant lesions. Totally 151 benign lesions and 68 malignant lesions were confirmed. According to the full-field digital mammography (FFDM), 82 lesions were in BI-RADS category 3, 104 lesions in 4A, 29 lesions in 4B, and 4 lesions in 4C. The positive predictive values of FFDM for BI-RADS categories 3, 4A, 4B, and 4C were 13.4% (11/82), 27.9% (29/104), 82.8% (24/29), and 100.0% (4/4), respectively. According to MRI, 59 lesions were in BI-RADS categories 1-2, 87 lesions in 3, 39 lesions in 4, and 34 lesions in 5, with their positive predictive values being 0.0% (0/58), 2.3% (2/87), 89.7% (35/39), and 100.0% (34/34), respectively. The area under the ROC curve (AUC) of breast benign and malignant lesions differentiated by FFDM was 0.647, and the diagnostic sensitivity, specificity, and Youden index were 86.3%, 41.7%, and 0.280, respectively. The AUC of FFDM combined with dynamic contrast-enhanced MRI (DCE-MRI) in differentiating breast benign vs. malignant lesions was 0.851, and the diagnostic sensitivity, specificity, and Youden index were 89.2%, 80.7%, and 0.699, respectively. The AUC of FFDM combined with DCE-MRI and the apparent diffusion coefficient (ADC) in differentiating benign vs. malignant lesions was 0.983, and the diagnostic sensitivity, specificity, and Youden index were 98.1%, 97.5%, and 0.956, respectively. MRI can improve the diagnostic efficiency of mammography in diagnosing BI-RADS category 3-4 architectural distortions and can help in the qualitative diagnosis of architectural distortion lesions.

High-Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network.

A generative adversarial network could be used for high-resolution (HR) medical image synthesis with reduced scan time. To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HRpre and HRpost images based on their corresponding low-resolution (LR) images (LRpre and LRpost ). This was a retrospective analysis of a prospectively acquired cohort. In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set. Dynamic contrast-enhanced (DCE) MRI with a 1.5T scanner. Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI-RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI-RADS) categories were calculated between the three readers. Wilcoxon signed-rank tests evaluated differences among the multireader ranking scores. The mean overall image quality scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI-RADS categories had good agreements among the three readers (all ICCs >0.75). DCGAN was capable of generating HR of the breast from fast pre- and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study. 3 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1852-1858.

Combined use of strain elastography and superb microvascular imaging with grayscale ultrasound according to the BI-RADS classification for differentiating benign from malignant solid breast masses.

To evaluate the combined use of strain elastography (SE) and superb microvascular imaging (SMI) with grayscale ultrasound (US) according to the Breast Imaging Reporting and Data System (BI-RADS) classification for differentiating benign from malignant breast masses. In total, 166 patients with 177 breast masses categorized as BI-RADS 3-5 were included in the study between April 2016 to September 2017. US, SMI and SE were performed to evaluate each mass. The following outcome measures were compared between US and the combinations of US, SMI, and SE: area under the receiver operating characteristic curve (AUC), sensitivity, specificity. The addition of SMI and SE to US increased the AUC from 0.816 to 0.948 (P < .001); and the specificity from 43.7% to 80.8% (P < .001), without a significant decrease in the sensitivity. Meanwhile, 66.1% (37 of 56) of the benign masses that were indicated for unnecessary biopsy were correctly changed to follow-up. The addition SMI and SE to US could improve the diagnostic performance in differentiating benign from malignant breast masses compared to US alone. When US was combined with SMI and SE, the specificity were increased, thus significantly reducing unnecessary biopsies.

Mammographic density changes during neoadjuvant breast cancer treatment: NeoDense, a prospective study in Sweden

ObjectivesTo assess if mammographic density (MD) changes during neoadjuvant breast cancer treatment and is predictive of a pathological complete response (pCR). MethodsWe prospectively included 200 breast cancer patients assigned to neoadjuvant chemotherapy (NACT) in the NeoDense study (2014–2019). Raw data mammograms were used to assess MD with a fully automated volumetric method and radiologists categorized MD using the Breast Imaging-Reporting and Data System (BI-RADS), 5th Edition. Logistic regression was used to calculate odds ratios (OR) for pCR comparing BI-RADS categories c vs. a, b, and d as well as with a 0.5% change in percent dense volume adjusting for baseline characteristics. ResultsThe overall median age was 53.1 years, and 48% of study participants were premenopausal pre-NACT. A total of 23% (N = 45) of the patients accomplished pCR following NACT. Patients with very dense breasts (BI-RADS d) were more likely to have a positive axillary lymph node status at diagnosis: 89% of the patients with very dense breasts compared to 72% in the entire cohort. A total of 74% of patients decreased their absolute dense volume during NACT. The likelihood of accomplishing pCR following NACT was independent of volumetric MD at diagnosis and change in volumetric MD during treatment. No trend was observed between decreasing density according to BI-RADS and the likelihood of accomplishing pCR following NACT. ConclusionsThe majority of patients decreased their MD during NACT. We found no evidence of MD as a predictive marker of pCR in the neoadjuvant setting.

Heterogenous Effect of Risk Factors on Breast Cancer across the Breast Density Categories in a Korean Screening Population.

We evaluated the heterogeneity of the effect of known risk factors on breast cancer development based on breast density by using the Breast Imaging-Reporting and Data System (BI-RADS). In total, 4,898,880 women, aged 40–74 years, who participated in the national breast cancer screening program in 2009–2010 were followed up to December 2018. Increased age showed a heterogeneous association with breast cancer (1-year hazard ratio (HR) = 0.92, 1.00 (reference), 1.03, and 1.03 in women with BI-RADS density category 1, 2, 3, and 4, respectively; P-heterogeneity < 0.001). More advanced age at menopause increased breast cancer risk in all BI-RADS categories. This was more prominent in women with BI-RADS density category 1 but less prominent in women in other BI-RADS categories (P-heterogeneity = 0.009). In postmenopausal women, a family history of breast cancer, body mass index ≥ 25 kg/m2, and smoking showed a heterogeneous association with breast cancer across all BI-RADS categories. Other risk factors including age at menarche, menopause, hormone replacement therapy after menopause, oral contraceptive use, and alcohol consumption did not show a heterogeneous association with breast cancer across the BI-RADS categories. Several known risk factors of breast cancer had a heterogeneous effect on breast cancer development across breast density categories, especially in postmenopausal women.

Analysis of the prognostic factors of triple-negative breast cancer and the clinical efficacy of postoperative radiotherapy in early cases.

e12502 Background: To explore the prognostic value of pretherapeutic peripheral blood parameters and breast imaging-reporting and data system (BI-RADS) classification of triple-negative breast cancer (TNBC) and the effect of postoperative radiotherapy in early cases. Methods: A total of 278 TNBC patients’ medical records between January 2013 and December 2018 were retrospectively collected, including white blood cells, neutrophils, lymphocytes, platelets, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), serum tumor markers, BI-RADS classification, TNM staging and therapeutic methods. We used the log-rank test and Kaplan-Meier curves to assess the progression-free survival (PFS) of enrolled patients. Multivariate prognostic analysis was performed by the Cox regression model. Results: The median PFS of all 278 patients was 20 months (4-72 months) at the end of follow-up. Operable patients who obtained adjuvant chemotherapy had a better median PFS (20 vs 18.5 months, P= 0.018, HR = 5.943, 95% CI: 1.36-25.92) than those without. Lymph node metastasis (52%) and chest well relapse (28%) were the critical failure forms followed by other metastases (12% lungs, 9.3% bones, 4% liver and 2.7% brain) with p value less than 0.001. Multivariate analysis indicated that platelet counts &gt; 113.5×109/L (p = 0.024), PLR ≤ 111.7 (p = 0.036) and CA15-3 ≤ 8.4 U/mL (p = 0.003) were significantly associated with the favourable prognosis of PFS in TNBC patients. Furthermore, BI-RADS of grade 2-4 had a better median PFS compared with grade of 5-6(20 vs 17.5 months, p = 0.008, HR = 2.154, 95% CI: 1.219-3.805). In subgroup analysis, forty-three early cases with pN1 stage earned additional benefits from postoperative radiotherapy in terms of low risk of distant metastasis and recurrence (p = 0.014). Residual tumor after surgery was another important factor of poor prognosis (p = 0.029). Conclusions: Adjuvant chemotherapy improved median PFS for operable TNBC patients and pretherapeutic platelets, PLR, CA15-3 and BI-RADS were the independent prognostic factors of survival. Lymph node metastasis and chest well relapse were the main patterns of treatment failure. Radiotherapy could reduce the risk of disease progression for postoperative patients with N1 stage.

Cancer Yield and Patterns of Follow-up for BI-RADS Category 3 after Screening Mammography Recall in the National Mammography Database.

Background The literature supports the use of short-interval follow-up as an alternative to biopsy for lesions assessed as probably benign, Breast Imaging Reporting and Data System (BI-RADS) category 3, with an expected malignancy rate of less than 2%. Purpose To assess outcomes from 6-, 12-, and 24-month follow-up of probably benign findings first identified at recall from screening mammography in the National Mammography Database (NMD). Materials and Methods This retrospective study included women recalled from screening mammography with BI-RADS category 3 assessment at additional evaluation from January 2009 through March 2018 from 471 NMD facilities. Only the first BI-RADS category 3 occurrence for women aged 25 years or older with no personal history of breast cancer was analyzed, with biopsy or 2-year imaging follow-up. Cancer yield and positive predictive value of biopsies performed (PPV3) were determined at each follow-up. Results Among 45 202 women (median age, 55 years; range, 25-90 years) with a BI-RADS category 3 lesion, 1574 (3.5%) underwent biopsy at the time of lesion detection, yielding 72 cancers (cancer yield, 4.6%; 72 of 1574 women). For the remaining 43 628 women who accepted surveillance, 922 were seen within 90 days (with 78 lesions biopsied and 12 [15%] classified as malignant). The women still in surveillance (31 465 of 43 381 women [72.5%]) underwent follow-up mammography at 6 months. Of 3001 (9.5%) lesions biopsied, 456 (15.2%) were malignant (cancer yield, 1.5%; 456 of 31 465 women; 95% confidence interval [CI]: 1.3%, 1.6%). Among 18 748 of 25 997 women (72.1%) in surveillance who underwent follow-up at 12 months, 1219 (6.5%) underwent biopsy with 230 (18.9%) malignant lesions found (cancer yield, 1.2%; 230 of 18 748 women; 95% CI: 1.1%, 1.4%). Through 2-year follow-up, the biopsy rate was 11.2% (4894 of 43 628 women) with a cancer yield of 1.86% (810 malignancies found among 43 628 women; 95% CI: 1.73%, 1.98%) and a PPV3 of 16.6% (810 malignancies found among 4894 women). Conclusion In the National Mammography Database, Breast Imaging Reporting and Data System (BI-RADS) category 3 use is appropriate, with 1.86% cumulative cancer yield through 2-year follow-up. Of 810 malignancies, 468 (57.8%) were diagnosed at or before 6 months, validating necessity of short-interval follow-up of mammographic BI-RADS category 3 findings. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Moy in this issue.

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms.

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Cost-effectiveness of stereotactic vacuum-assisted biopsy for nonpalpable breast lesions

PurposeTo examine the potential cost-savings of stereotactic vacuum-assisted biopsy (SVAB) over open surgical biopsy (OSB) in diagnosis of nonpalpable lesions on mammography and to estimate the cost-saving effect on lesions at different levels of malignant probability. MethodsThis retrospective study was approved by our Institutional Review Board. We retrospectively reviewed 276 (33.8 %) SVAB and 541 (66.2 %) OSB medical records at a medical center. Direct costs included patients’ self-paid and national health insurance claim charges. Indirect costs were calculated using sick days, average salary, and age-adjusted employment rate. One-way and two-way sensitivity analyses were conducted. Lesion classification was determined by the assessment categories of Breast Imaging Reporting and Data System (BI-RADS), 4th or 5th editions. ResultsSVAB decreased the direct cost by $90.3 (10.1 %) per diagnosis. The indirect cost was decreased by $560.2 (96.0 %). Overall, SVAB saved 43.9 % of resource utilization for each biopsy. Taking the cost of the subsequent malignant surgery into account, from the healthcare providers’ perspective, SVAB was cost-effective if a lesion had less than 19 % likelihood of malignancy. From the societal perspective, SVAB reduced productivity loss for all the lesions. Based on the positive predictive value of the BI-RADS categories, SVAB was more suitable for the lesions of category 4A and category 3, resulting in greater savings in both medical and societal resources. ConclusionsSVAB is a cost-effective diagnostic option for nonpalpable breast lesions. The cost-saving effect is greater for the lesions of category 4A and category 3.

A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.

Tumor Proliferation and Invasiveness Derived From Ultrasound Appearances of Invasive Breast Cancers: Moving Beyond the Routine Differential Diagnosis.

To investigate the correlation between ultrasound (US) appearances of invasive breast cancers and tumor proliferation and invasiveness measured according to the histologic grade, Ki-67 expression, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI). This study evaluated 676 patients who underwent primary surgical treatment of invasive breast cancers. The preoperative US reports and postoperative pathologic and immunohistochemical results of the patients were retrospectively reviewed. Ultrasound characteristics were evaluated according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon. Logistic regression analyses were used to identify independent predictive US features that were correlated with tumor proliferation and invasiveness of breast cancers. Odds ratios (ORs) were calculated. Posterior acoustic enhancement and calcifications on US images were independent predictive factors of a higher histologic grade and a higher Ki-67 level (OR, 1.69-6.54; P < .05). Meanwhile, a noncircumscribed margin (OR, 2.61; P < .05) and posterior acoustic shadow (OR, 1.62; P < .05) were independent predictors of ALNM. An irregular shape (OR, 2.13; P < .05) and calcifications (OR, 1.69; P < .05) were independent risk factors for LVI. Infiltrative breast cancers scored as BI-RADS category 5 had higher probability to be associated with ALNM (OR, 3.33; P < .0005) and LVI (OR, 2.87; P < .0005). Ultrasound features of invasieve breast cancers might have a predictive value for tumor proliferation and invasiveness. The US features correlated with a high cellular proliferation rate were different from those associated with ALNM. The tumor shape, margin, posterior acoustic pattern, and calcifications at US are suggested to be considered by clinicians when making clinical decisions.

Breast non-mass-like lesions on contrast-enhanced ultrasonography: Feature analysis, breast image reporting and data system classification assessment

BACKGROUNDBreast non-mass-like lesions (NMLs) account for 9.2% of all breast lesions. The specificity of the ultrasound diagnosis of NMLs is low, and it cannot be objectively classified according to the 5th Edition of the Breast Imaging Reporting and Data System (BI-RADS). Contrast-enhanced ultrasound (CEUS) can help to differentiate and classify breast lesions but there are few studies on NMLs alone.AIMTo analyze the features of benign and malignant breast NMLs in grayscale ultrasonography (US), color Doppler flow imaging (CDFI) and CEUS, and to explore the efficacy of the combined diagnosis of NMLs and the effect of CEUS on the BI-RADS classification of NMLs.METHODSA total of 51 breast NMLs verified by pathology were analyzed in our hospital from January 2017 to April 2019. All lesions were examined by US, CDFI and CEUS, and their features from those examinations were analyzed. With pathology as the gold standard, binary logic regression was used to analyze the independent risk factors for malignant breast NMLs, and a regression equation was established to calculate the efficiency of combined diagnosis. Based on the regression equation, the combined diagnostic efficiency of US combined with CEUS (US + CEUS) was determined. The initial BI-RADS-US classification of NMLs was adjusted according to the independent risk factors identified by CEUS, and the diagnostic efficiency of CEUS combined with BI-RADS (CEUS + BI-RADS) was calculated based on the results. ROC curves were drawn to compare the diagnostic values of the three methods, including US, US + CEUS, and CEUS + BI-RADS, for benign and malignant NMLs.RESULTSMicrocalcification, enhancement time, enhancement intensity, lesion scope, and peripheral blood vessels were significantly different between benign and malignant NMLs. Among these features, microcalcification, higher enhancement, and lesion scope were identified as independent risk factors for malignant breast NMLs. When US, US + CEUS, and CEUS + BI-RADS were used to identify the benign and malignant breast NMLs, their sensitivity rates were 82.6%, 91.3%, and 87.0%, respectively; their specificity rates were 71.4%, 89.2%, and 92.9%, respectively; their positive predictive values were 70.4%, 87.5%, and 90.9%, respectively; their negative predictive values were 83.3%, 92.6%, and 89.7%, respectively; their accuracy rates were 76.5%, 90.2%, and 90.2%, respectively; and their corresponding areas under ROC curves were 0.752, 0.877 and 0.903, respectively. Z tests showed that the area under the ROC curve of US was statistically smaller than that of US + CEUS and CEUS + BI-RADS, and there was no statistical difference between US + CEUS and CEUS + BI-RADS.CONCLUSIONUS combined with CEUS can improve diagnostic efficiency for NMLs. The adjustment of the BI-RADS classification according to the features of contrast-enhanced US of NMLs enables the diagnostic results to be simple and intuitive, facilitates the management of NMLs, and effectively reduces the incidence of unnecessary biopsy.