Breast Imaging Reporting And Data System Category Research Articles

Does breast density measured through population-based screening independently increase breast cancer risk in Asian females?

PurposeThe purpose of this study was to investigate the effects of breast density on breast cancer risk among women screened via a nationwide mammographic screening program.Patients and methodsWe conducted a nested case–control study for a randomly selected population of 1,561 breast cancer patients and 6,002 matched controls from the National Cancer Screening Program. Breast density was measured and recorded by two independent radiologists using the Breast Imaging Reporting and Data System (BI-RADS). Associations between BI-RADS density and breast cancer risk were evaluated according to screening results, time elapsed since receiving non-recall results, age, and menopausal status after adjusting for possible covariates.ResultsBreast cancer risk for women with extremely dense breasts was five times higher (adjusted odds ratio [aOR] =5.0; 95% confidence interval [CI]) =3.7–6.7) than that for women with an almost entirely fatty breast, although the risk differed between recalled women (aOR =3.3, 95% CI =2.3–3.6) and women with non-recalled results (aOR =12.1, 95% CI =6.3–23.3, P-heterogeneity =0.001). aORs for BI-RADS categories of breast density were similar when subjects who developed cancer after showing non-recall findings during initial screening were grouped according to time until cancer diagnosis thereafter (<1 and ≥1 year). The prevalence of dense breasts was higher in younger women, and the association between a denser breast and breast cancer was stronger in younger women (heterogeneously dense breast: aOR =7.0, 95% CI =2.4–20.3, women in their 40s) than older women (aOR =2.5, 95% CI =1.1–6.0, women in their 70s or more). In addition, while the positive association remained, irrespective of menopausal status, the effect of a dense breast on breast cancer risk was stronger in premenopausal women.ConclusionThis study confirmed an increased risk of breast cancer with greater breast density in Korean women which was consistent regardless of BI-RADS assessment category, time interval after initially non-recall results, and menopausal status.

Breast MRI as a Problem-solving Study in the Evaluation of BI-RADS Categories 3 and 4 Microcalcifications: Is it Worth Performing?

We aimed to investigate the utility of problem-solving breast magnetic resonance imaging (MRI) for mammographic Breast Imaging Reporting and Data System (BI-RADS) categories 3 and 4 microcalcifications. Between January 1, 2010 and December 31, 2011, 138 women with 146 areas of categories 3 and 4 microcalcifications without sonographic correlates underwent breast MRI and had a stereotactic core biopsy using an 11-gauge needle or follow-up at least for 24 months. Positive predictive value (PPV), negative predictive value, sensitivity, and specificity were calculated on the basis of BI-RADS category, with categories 1-3 being considered benign and categories 4 and 5 being considered malignant. Twenty-four cases (16.4%) were malignant (18 ductal carcinoma in situ, 6 invasive). MRI increased PPV and specificity from 43% to 68% and from 80% to 93% (P = .054 and .005) compared to mammography. Within 102 category 3 microcalcifications, 5 carcinomas were assessed correctly as category 4 by MRI. Within 44 category 4 microcalcifications, a correct diagnosis was made by MRI in 77% (34 of 44) as opposed to 43% (19 of 44) by mammography, and 80% (20 of 25) of unnecessary biopsies could have been avoided. Within the 24 carcinomas, 5 were negative at MRI. MRI-negative carcinomas have a significantly higher possibility of being low grade (ductal carcinoma in situ or invasive) (P = .0362). Breast MRI has the potential to improve the diagnosis of category 3 or 4 microcalcifications and could alter indications for biopsy. Breast MRI could help predict the presence or absence of higher-grade carcinoma for category 3 or 4 microcalcifications.

A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes

Background and objectiveConventional computer-aided diagnosis (CAD) systems for breast ultrasound (BUS) are trained to classify pathological classes, that is, benign and malignant. However, from a clinical perspective, this kind of classification does not agree totally with radiologists’ diagnoses. Usually, the tumors are assessed by using a BI-RADS (Breast Imaging-Reporting and Data System) category and, accordingly, a recommendation is emitted: annual study for category 2 (benign), six-month follow-up study for category 3 (probably benign), and biopsy for categories 4 and 5 (suspicious of malignancy). Hence, in this paper, a CAD system based on BI-RADS categories weighted by pathological information is presented. The goal is to increase the classification performance by reducing the common class imbalance found in pathological classes as well as to provide outcomes quite similar to radiologists’ recommendations. MethodsThe BUS dataset considers 781 benign lesions and 347 malignant tumors proven by biopsy. Moreover, every lesion is associated to one BI-RADS category in the set {2, 3, 4, 5}. Thus, the dataset is split into three weighted classes: benign, BI-RADS 2 in benign lesions; probably benign, BI-RADS 3 and 4 in benign lesions; and malignant, BI-RADS 4 and 5 in malignant lesions. Thereafter, a random forest (RF) classifier, denoted by RFw, is trained to predict the weighted BI-RADS classes. In addition, for comparison purposes, a RF classifier is trained to predict pathological classes, denoted as RFp. ResultsThe ability of the classifiers to predict the pathological classes is measured by the area under the ROC curve (AUC), sensitivity (SEN), and specificity (SPE). The RFw classifier obtained AUC=0.872,SEN=0.826, and SPE=0.919, whereas the RFp classifier reached AUC=0.868,SEN=0.808, and SPE=0.929. According to a one-way analysis of variance test, the RFw classifier statistically outperforms (p < 0.001) the RFp classifier in terms of the AUC and SEN. Moreover, the classification performance of RFw to predict weighted BI-RADS classes is given by the Matthews correlation coefficient that obtained 0.614. ConclusionsThe division of the classification problem into three classes reduces the imbalance between benign and malignant classes; thus, the sensitivity is increased without degrading the specificity. Therefore, the CAD based on weighted BI-RADS classes improves the classification performance of the conventional CAD systems. Additionally, the proposed approach has the advantage of being capable of providing a multiclass outcome related to radiologists’ recommendations.

MRI in the Assessment of BI-RADS® 4 lesions

The American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) lexicon, which is used ubiquitously to standardize reporting of breast magnetic resonance imaging (MRI), provides 7 BI-RADS assessment categories to indicate the level of suspicion of malignancy and guide further management. A BI-RADS category 4 assessment is assigned when an imaging abnormality does not fulfill the typical criteria for malignancy, but is suspicious enough to warrant a recommendation for biopsy. The BI-RADS category 4 assessment covers a wide range of probability of malignancy, from >2 to <95%. MRI is an essential noninvasive technique in breast imaging and the role of MRI in the assessment of ACR BI-RADS 4 lesions is manifold. In lesions classified as suspicious on imaging with mammography, digital breast tomosynthesis, and sonography, MRI can aid in the noninvasive differentiation of benign and malignant lesions and obviate unnecessary breast biopsies. When the suspicion of cancer is confirmed with MRI, concurrent staging of disease for treatment planning can be accomplished. This article will provide a comprehensive overview of the role of breast MRI in the assessment of ACR BI-RADS 4 lesions. In addition, we will discuss strategies to decrease false positives and avoid false negative results when reporting MRI of the breast.

The Association of Mammographic Density and Molecular Breast Cancer Subtype.

Background: Mammographic density (MD) is associated with increased breast cancer risk, yet limited data exist on an association between MD and breast cancer molecular subtypes.Methods: Women ages 18 years and older with breast cancer and available mammograms between 2003 and 2012 were enrolled in a larger study on MD. MD was classified by the Breast Imaging Reporting and Data System (BI-RADS) classification and by volumetric breast percent density (Volpara Solutions). Subtype was assigned by hormone receptor status, tumor grade, and mitotic score (MS). Subtypes included: Luminal-A (ER/PR+ and grade = 1; ER/PR+ and grade = 2 and MS = 1; ER+/PR- and grade = 1; n = 233); Luminal-B (ER+ and grade = 3 or MS = 3; ER+/PR- and grade = 2; ER/PR+ and grade = 2 and MS = 2; n = 79); Her-2-neu+ (H2P; n = 59); triple-negative (ER/PR-, Her-2-; n = 86). Precancer factors including age, race, body mass index (kg/m2), family history of breast cancer, and history of lobular carcinoma in situ were analyzed.Results: A total of 604 patients had invasive cancer; 457 had sufficient information for analysis. Women with H2P tumors were younger (P = 0.011) and had the highest volumetric percent density (P = 0.002) among subgroups. Multinomial logistic regression (LA = reference) demonstrated that although quantitative MD does not significantly differentiate between all subtypes (P = 0.123), the association between MD and H2P tumors is significant (OR = 1.06; confidence interval, 1.01-1.12). This association was not seen using BI-RADS classification in bivariable analysis but was statistically significant (P = 0.047) when controlling for other precancer factors.Conclusions: Increased MD is more strongly associated with H2P tumors when compared with LA.Impact: Delineating risk factors specific to breast cancer subtype may promote development of individualized risk prediction models and screening strategies. Cancer Epidemiol Biomarkers Prev; 26(10); 1487-92. ©2017 AACR.

B-Mode Ultrasound Combined with Color Doppler and Strain Elastography in the Diagnosis of Non-mass Breast Lesions: A Prospective Study

Non-mass breast lesions on ultrasound (US) are areas without an associated mass. The purpose of this study was to evaluate whether combining B-mode US with color Doppler US and strain elastography (SE) improves US differentiation between benign and malignant non-mass breast lesions and the decision for biopsy. In this prospective study, three different radiologists analyzed the US images of 77 non-mass lesions independently and recorded Breast Imaging Reporting and Data System (BI-RADS) categories for four data sets. The image characteristics and BI-RADS categories of the four data sets were analyzed by another radiologist. The final diagnosis was made on the basis of pathologic findings. Values for area under the receiver operating curve (AUC), sensitivity, specificity and accuracy were compared among the data sets. The AUC of B-mode US combined with both color Doppler US and SE was greater than that of B-mode US alone (0.666 vs. 0.828) (p = 0.011). The specificity of making the decision for biopsy increased from 6.5% to 38.7% when B-mode US was combined with color Doppler and SE, without a statistically significant change in sensitivity (p < 0.001). Combined use of color Doppler and SE could improve the diagnostic value of B-mode US in distinguishing benign from malignant non-mass breast lesions and the specificity of making the decision for biopsy of non-mass breast lesions.

Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System.

This work focused on extracting novel and validated digital high-throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. First, the phase congruency approach was used to segment the tumors automatically. Second, high-throughput features were designed and extracted on the basis of each BI-RADS category. Then features were selected based on the basis of a Student t test and genetic algorithm. Finally, the AdaBoost classifier was used to differentiate benign tumors from malignant ones. Experiments were conducted on a database of 138 pathologically proven breast tumors. The system was compared with 6 state-of-art BI-RADS feature extraction methods. By using leave-one-out cross-validation, our system achieved a highest overall accuracy of 93.48%, a sensitivity of 94.20%, a specificity of 92.75%, and an area under the receiver operating characteristic curve of 95.67%, respectively, which were superior to those of other methods. The experiments demonstrated that our computerized BI-RADS feature system was capable of helping radiologists detect breast cancers more accurately and provided more guidance for final decisions.

Sonographic features that can be used to differentiate between small triple-negative breast cancer and fibroadenoma.

PurposeOur study investigated whether any sonographic findings could be useful for differentiating between small triple-negative breast cancer (TNBC) and fibroadenoma.MethodsThis retrospective study was approved by our Institutional Review Board, which waived the requirement for patient consent. From January 2009 to December 2010, the sonographic features of 181 pathologically proven TNBC tumors and 172 fibroadenomas measuring less than or equal to 2 cm in the longest dimension were reviewed and analyzed according to the fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) lexicon. Mean tumor roundness was also measured using in-house software.ResultsThe median longest lesion dimension was 16 mm (range, 13 to 18 mm) in TNBCs and 13 mm (range, 10 to 16 mm) in fibroadenomas. In comparison to fibroadenomas, small TNBC tumors presented with a higher incidence of irregular shapes (66.9%), noncircumscribed margins (91.7%), hypoechoic echotexture (59.1%), posterior acoustic enhancement (65.2%), and associated features (24.4%). Most TNBCs were classified as BI-RADS category 4 (65.2%) or 5 (28.2%). The mean tumor roundness of small TNBCs was greater than that of fibroadenomas (60%±12% vs. 53%±13%). Multivariate analysis showed that older patient age, irregular shape, nonparallel orientation, posterior acoustic enhancement, associated features, a BI-RADS final assessment category of 4 or 5, and greater tumor roundness were significant independent factors indicative of TNBCs.ConclusionTNBC tumors tend to demonstrate more suspicious sonographic features and greater tumor roundness than fibroadenomas. These features may have the potential to help differentiate between small TNBCs and fibroadenomas.

Can magnetic resonance imaging obviate the need for biopsy for microcalcifications?

Although microcalcifications detected with mammography (MG) are usually biopsied, biopsies cannot be performed in all cases. We sought to determine if magnetic resonance imaging (MRI) findings could indicate whether stereotactic vacuum-assisted biopsy (SVAB) is necessary. Patients with mammographically detected Breast Imaging-Reporting and Data System (BI-RADS) 3, 4, and 5 microcalcifications were analyzed from April 2012 to September 2014. All patients underwent MRI. All patients with enhancing lesions in the region of the microcalcifications underwent SVAB. Non-enhancing lesions were followed or biopsied, depending on the patient's preferences. MRI findings were classified as either malignant-suspicious or benign-suspicious ("none" or "nonspecific"), and we evaluated the positive predictive value (PPV) and negative predictive value (NPV) of these classifications for predicting malignancy. A total of 87 patients underwent both MRI and SVAB. The NPV of MRI was 100% in the group with no enhancement. In BI-RADS category 3, there were 57 benign-suspicious lesions on MRI, of which eight were malignant (NPV of MRI: 85.0%). It may be possible to omit SVAB for microcalcifications if there is no enhancement on MRI; however, any kind of enhancement indicates the need for biopsy in cases of BI-RADS 3 calcifications on MG.

Diagnostic Performance of Ultrasound-Guided Fine-Needle Aspiration of Nonpalpable Breast Lesions in a Multidisciplinary Setting: The Institut Curie's Experience.

To assess the diagnostic performance of ultrasound-guided fine-needle aspiration (USFNA) in nonpalpable breast lesions (NPBLs) in a multidisciplinary setting. In total, 2,601 NPBLs underwent USFNA by a radiologist-pathologist team. Gold-standard diagnosis was based on surgery, core-needle biopsy, or 1-year imaging follow-up. USFNA's diagnostic performance was analyzed in different clinical and imaging subgroups. USFNA's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were, respectively, 92.6% (95% confidence interval [CI], 90.8%-94.2%), 96.8% (95% CI, 95.8%-97.6%), 94.8% (95% CI, 93.2%-96.1%), and 95.4% (95% CI, 94.3%-96.4%). The best PPV was achieved in Breast-Imaging Reporting and Data System (BI-RADS) categories 4C and 5 and the best NPV in BI-RADS categories 2, 3, and 4A and in patients younger than 50 years. The mitotic count, BI-RADS categories, associated palpable cancer, and age (<50 or ≥50 years) were statistically independent factors ( P < .05) between USFNA's false-negative and true-positive results. USFNA is a robust diagnostic procedure in NPBLs. Age and the BI-RADS category of the lesion are important factors determining its performance.

Mammographic breast density and breast cancer risk in a Mediterranean population: a nested case-control study in the EPIC Florence cohort.

Mammographic breast density (MBD) has been consistently associated with breast cancer (BC) risk, and at the same time it is modulated by established BC risk factors related to reproductive and hormonal history and to lifestyle. We aimed to evaluate the association between the clinical breast imaging reporting and data system (BI-RADS), a qualitative MBD classification used in clinical setting, and BC risk through a case-control nested in the EPIC Florence cohort where baseline information on reproductive history, lifestyle and anthropometry were collected. The study includes 136 newly diagnosed BC cases and 635 controls from the 10,083 healthy women enroled in the cohort between 1993 and 1998 and followed for 6years on average. MBD was assessed on a negative mammogram performed at least one year before diagnosis in cases and on a mammogram performed in the same period for controls matched for age, enrolment date and menopausal status. Multivariate analyses adjusted for education, body mass index, parity, number of children, breastfeeding, BC family history, history of breast biopsies and Hormone Replacement Therapy use were performed. An increase in BC risk across BI-RADS categories emerged with adjusted odds ratios (OR) 1.79 (95% CI 1.06-3.01), OR 2.09 (95% CI 1.17-3.74) and OR 2.67 (95% CI 1.08-6.62) for categories 2, 3 and 4 in comparison with the reference category (p for trend=0.008). We confirm in this Mediterranean population the association of increasing MBD, classified according to BI-RADS with BC risk also taking into account other well-known risk factors for this neoplasm.

Diagnostic value of breast imaging reporting and data system combined with wire-guided localization biopsy for breast microcalcifications in impalpable breast cancer

Objective To explore the diagnosis value of X-ray breast imaging reporting and data system (BI-RADS) combined with wire-guided localization biopsy for breast microcalcifications in impalpable breast cancer. Methods 192 palpation negative patients with 205 microcalcification lesions were detected by mammography. All lesions were classified according to BI-RADS descriptors for calcification and were categorized by the BI-RADS. The patients with BI-RADS category 4a and above underwent X-ray positioning guide wire-guided biopsy and pathological diagnosis. Results In 205 microcalcification lesions, 74 (36.1%) were malignant lesions, 131 (63.9%) were less than benign lesions. The positive predictive value of malignant breast lesions in clustered, segmental, regional linear branching calcifications were higher [83.3% (5/6), 100.0% (11/11), 100.0% (1/1)], followed by clustered, linear, segmental, regional pleomorphic calcifications [55.9% (38/68), 50.0% (1/2), 40.0% (8/20) and 33.3% (4/12), respectively]. The positive predictive values of malignant in linear branching calcifications and pleomorphic calcifications were significantly higher than those of coarse heterogeneous calcifications, amorphous or indistinct calcifications (χ2 values were 34.44, 51.87, 16.71, 29.86, all P < 0.05). The linear branching calcification had the highest possibility. The proportions of malignant lesions in four different types of glands were extremely dense 40.5% (30/74), heterogeneously dense39.2% (29/74), scattered areas of fibroglandular density 10.8% (8/74) and fat 9.5% (7/74), respectively. Conclusions BI-RADS categorization for breast microcalcification lesions can improve the detection rate of impalpable breast cancer. Linear branching calcification has higher predictive value for malignant lesions. Dense breast is the risk factors of breast cancer, which should be attached great importance. Key words: Mammography; Microcalcification; Biopsy; Palpation; Breast neoplasms

Automated annotation and classification of BI-RADS assessment from radiology reports

The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities.The objective of this study is to develop a natural language processing (NLP) system for automated BI-RADS categories extraction from breast radiology reports. We evaluated an existing rule-based NLP algorithm, and then we developed and evaluated our own method using a supervised machine learning approach. We divided the BI-RADS category extraction task into two specific tasks: (1) annotation of all BI-RADS category values within a report, (2) classification of the laterality of each BI-RADS category value. We used one algorithm for task 1 and evaluated three algorithms for task 2. Across all evaluations and model training, we used a total of 2159 radiology reports from 18 hospitals, from 2003 to 2015.Performance with the existing rule-based algorithm was not satisfactory. Conditional random fields showed a high performance for task 1 with an F-1 measure of 0.95. Rules from partial decision trees (PART) algorithm showed the best performance across classes for task 2 with a weighted F-1 measure of 0.91 for BIRADS 0-6, and 0.93 for BIRADS 3-5. Classification performance by class showed that performance improved for all classes from Naïve Bayes to Support Vector Machine (SVM), and also from SVM to PART.Our system is able to annotate and classify all BI-RADS mentions present in a single radiology report and can serve as the foundation for future studies that will leverage automated BI-RADS annotation, to provide feedback to radiologists as part of a learning health system loop.

Breast Cancer Detected at Screening US: Survival Rates and Clinical-Pathologic and Imaging Factors Associated with Recurrence.

Purpose To determine the survival rates and clinical-pathologic and imaging factors associated with recurrence in women with breast cancer detected at screening ultrasonography (US). Materials and Methods This study was approved by the institutional review board, and the requirement to obtain informed consent was waived. A retrospective review of the databases of four institutions identified 501 women (median age, 47 years; range, 27-74 years) with breast cancer (425 invasive cancers and 76 ductal carcinoma in situ) detected at screening US between January 2004 and March 2011. Five-year overall survival (OS) and recurrence-free survival (RFS) rates were estimated, and the clinical-pathologic and imaging data were collected. Multivariate analysis was performed by using Cox proportional hazard regression to determine factors associated with recurrence. Results At a median follow-up of 7.0 years (range, 5.0-12.1 years), 15 (3.0%) recurrences were detected: five in ipsilateral breast and 10 in contralateral breast. The 5-year OS and RFS rates were 100% and 98.0% (95% confidence interval [CI]: 96.8%, 99.2%), respectively. In patients with invasive cancers, age younger than 40 years (hazard ratio: 3.632 [95% CI: 1.099, 11.998]; P = .032), the triple-negative subtype (hazard ratio: 7.498 [95% CI: 2.266, 24.816]; P = .001), and Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions (hazard ratio: 5.113 [95% CI: 1.532, 17.195]; P = .008) were associated with recurrence. Conclusion Women with breast cancers detected at screening US have excellent outcomes, with a 5-year RFS rate of 98.0%. However, in patients with invasive breast cancer, age younger than 40 years, the triple-negative subtype, and BI-RADS category 4A lesions were associated with recurrence. © RSNA, 2017 Online supplemental material is available for this article.

Abstract P4-02-13: Breast ultrasound surveillance with image fusion technique in a short-interval follow-up for BI-RADS category 3 mass lesions

Abstract PURPOSE: Breast ultrasound (US) is a diagnostic imaging modality that is now widely used as an adjunct to mammography for evaluation of breast abnormalities. In the Breast Imaging-Reporting and Data System (BI-RADS) lexicon, a solid mass with an oval shape, well-circumscribed margins and parallel orientation is classified as category 3 (BI-RADS category 3 mass lesions) . This mass should have a risk of malignancy of &amp;lt; 2%. Surveillance breast US in short-interval follow-up for BI-RADS category 3 mass lesions is an acceptable alternative to biopsy. The surveillance US is recommended 6 month after initial lesion identification and then every year for at least 2 y. However, inconsistent reproducibility for US due to operator dependence is still a clinical issue. The aim of our study was to verify the utility of US using real-time virtual sonography (RVS) - to coordinate present US images with past US images reconstructed from previously acquired US volume data using magnetic tracking system – in a short-interval follow--up for BI-RADS category 3 mass lesions. METHOD AND MATERIALS: We enrolled 20 women (23 lesions) with more than 24 months of follow-up after classification as BI-RADS category 3 during initial US. US surveillance was scheduled at 6, 12 and 24 months. Three-dimensional assessment of morphologic features was performed while serially checking past US volume data corresponding to the present US probe position. Measurement of the target lesion diameter was performed after the probe was adjusted to include the maximum diameter of a past US image at each visit. RESULTS: RVS was technically successful in 100% of patients. All target lesions were detected, including two iso-echoic lesions. The mean target lesion diameters at baseline and at 6, 12 and 24 months were 8.2+4.2, 8.4+4.5, 8.1+4.5 and 8.3+5.0 mm, respectively . Statistical analysis using a Friedman test with multiple comparisons revealed no significant difference between the diameters at each time point (p = 0.785). RVS was used to directly compare the US morphologic characteristics and sized of lesions. Furthermore, the RVS data can be reproduced in their entirety for independent review at a later date by using stored US volume data. CONCLUSION:Our results suggest that RVS is reproducible, operator-independent technique for comparison of US images of BI-RADS category 3 mass lesions obtained at different time points. Citation Format: Nakano S, Fujii K, Kousaka J, Mouri Y, Ando T. Breast ultrasound surveillance with image fusion technique in a short-interval follow-up for BI-RADS category 3 mass lesions [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-02-13.

Automated Breast Density Computation in Digital Mammography and Digital Breast Tomosynthesis: Influence on Mean Glandular Dose and BIRADS Density Categorization

The study aimed to compare the breast density estimates from two algorithms on full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and to analyze the clinical implications. We selected 561 FFDM and DBT examinations from patients without breast pathologies. Two versions of a commercial software (Quantra 2D and Quantra 3D) calculated the volumetric breast density automatically in FFDM and DBT, respectively. Other parameters such as area breast density and total breast volume were evaluated. We compared the results from both algorithms using the Mann-Whitney U non-parametric test and the Spearman's rank coefficient for data correlation analysis. Mean glandular dose (MGD) was calculated following the methodology proposed by Dance etal. Measurements with both algorithms are well correlated (r ≥ 0.77). However, there are statistically significant differences between the medians (P < 0.05) of most parameters. The volumetric and area breast density median values from FFDM are, respectively, 8% and 77% higher than DBT estimations. Both algorithms classify 35% and 55% of breasts into BIRADS (Breast Imaging-Reporting and Data System) b and c categories, respectively. There are no significant differences between the MGD calculated using the breast density from each algorithm. DBT delivers higher MGD than FFDM, with a lower difference (5%) for breasts in the BIRADS d category. MGD is, on average, 6% higher than values obtained with the breast glandularity proposed by Dance etal. Breast density measurements from both algorithms lead to equivalent BIRADS classification and MGD values, hence showing no difference in clinical outcomes. The median MGD values of FFDM and DBT examinations are similar for dense breasts (BIRADS d category).

Utility of supplemental screening with breast ultrasound in asymptomatic women with dense breast tissue who are not at high risk for breast cancer.

Objective:To assess the results of an initial round of supplemental screening with hand-held bilateral breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue who are not at high risk for breast cancer.Materials and Methods:A retrospective, Health Insurance Portability and Accountability Act compliant, Institutional Research Board approved study was performed at a single academic tertiary breast center. Informed consent was waived. A systematic review of the breast imaging center database was conducted to identify and retrieve data for all asymptomatic women, who were found to have heterogeneously dense or extremely dense breast tissue on screening bilateral mammograms performed from July 1, 2010 through June 30, 2012 and who received a mammographic final assessment American College of Radiology's (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 1 or BI-RADS category 2. Hand-held screening ultrasound was performed initially by a technologist followed by a radiologist. Chi-square and t-test were used and statistical significance was considered at P < 0.05.Results:A total of 1210 women were identified. Of these, 394 underwent the offered supplemental screening ultrasound. BI-RADS category 1 or 2 was assigned to 323 women (81.9%). BI-RADS category 3 was assigned to 50 women (12.9%). A total of 26 biopsies/aspirations were recommended and performed in 26 women (6.6%). The most common finding for which biopsy was recommended was a solid mass (88.5%) with an average size of 0.9 cm (0.5–1.7 cm). Most frequent pathology result was fibroadenoma (60.8%). No carcinoma was found.Conclusion:Our data support the reported occurrence of a relatively high number of false positives at supplemental screening with breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue, who are not at a high risk of developing breast cancer, and suggests that caution is necessary in establishing wide implementation of this type of supplemental screening for all women with dense breast tissue without considering other risk factors for breast cancer.

Usefulness of combined BI-RADS analysis and Nakagami statistics of ultrasound echoes in the diagnosis of breast lesions

To develop a method combining the statistics of the ultrasound backscatter and the Breast Imaging-Reporting and Data System (BI-RADS) classification to enhance the differentiation of breast tumours. The Nakagami shape parameter m was used to characterise the scatter properties of breast tumours. Raw data from the radiofrequency (RF) echo-signal and B-mode images from 107 (32 malignant and 75 benign) lesions and their surrounding tissue were recorded. Three different characteristic values of the shape parameters of m (maximum [mLmax], minimum [mLmin] and average [mLavg]) and differences between m parameters (Δmmax, Δmmin, Δmavg) of the lesions and their surrounding tissues were assessed. A lesion with a BI-RADS score of 3 was considered benign, while a lesion with a score of 4 was considered malignant (a cut-off of BI-RADS 3/4 was set for all patients). The area under the receiver operating characteristic (ROC) curve (AUC) was equal to 0.966 for BI-RADS, with 100% sensitivity and 54.67% specificity. All malignant lesions were diagnosed correctly, whereas 34 benign lesions were biopsied unnecessarily. In assessing the Nakagami statistics, the sum of the sensitivity and specificity was the best for mLavg (62.5% and 93.33%, respectively). Only four of 20 lesions were found over the cut-off value in BI-RADS of 4a. When comparing the differences in m parameters, Δmavg had the highest sensitivity of 90% (only three of 32 lesions were false negative). These three lesions were classified as BI-RADS category 4c. The combined use of B-mode and mLmin parameter improve the AUC up to 0.978 (p=0.088), compared to BI-RADS alone. The combination of the parametric imaging and the BI-RADS assessment does not significantly improve the differentiation of breast lesions, but it has the potential to better identify the group of patients with mainly benign lesions that have a low level of suspicion for malignancy with a BI-RADS score of 4a.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation

ObjectivesTo examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. MethodsFrom April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. ResultsWith pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P<0.05). When applying Qual1=Color pattern 1 for downgrading and Qual1=Color pattern 5 for upgrading the BI-RADS categories, we obtained the highest Az value (0.951), and achieved a significantly higher specificity (86.56%, P=0.002) than that of the US (81.18%) with the same sensitivity (94.96%). ConclusionsThe qualitative classification proposed in this study may be representative of SWE parameters and has potential to be relevant assistance in breast mass diagnoses.

Differential Diagnosis of Breast Category 3 and 4 Nodules Through BI-RADS Classification in Conjunction with Shear Wave Elastography

Ultrasound (US) has become one of the important imaging methods for differentiating benign from malignant breast lesions. In 2013, the American College of Radiology published the fifth edition of the Breast Imaging-Reporting and Data System (BI-RADS). BI-RADS is a guide with recommendations for the standardization of breast imaging (US, mammography and magnetic resonance imaging) reports and for the auditing of centers employing such methods. Its objective is to standardize the nomenclature used in the reports. However, current US examinations are neither adequately sensitive nor sufficiently specific enough. The average Young's modulus was measured through shear wave elastography (SWE) to evaluate the diagnostic value of the BI-RADS classification in conjunction with SWE in differentiating BI-RADS 3 and 4 nodules. A total of 100 consecutive women with 126 breast lesions, including 65 benign and 61 malignant lesions, were included. The average Young's modulus of breast nodules and peri-nodule tissue (Emean1 and Emean2) was also determined through SWE. A receiver operating characteristic curve was drawn on the basis of pathologic results. The highest cut-off values were C1 and C2. At Emean1 > C1 or Emean2 > C2, BI-RADS 3 was increased to 4a and BI-RADS 4a was increased to 4b. At Emean1 ≤ C1 and Emean2 ≤ C2, BI-RADS 4b was decreased to 4a. Other BI-RADS classifications remained unchanged. BI-RADS 3 and 4a were considered benign. BI-RADS 4b and 4c were malignant. The area under the curve, sensitivity and specificity of the BI-RADS classification in conjunction with SWE were 0.952, 93.4% and 95.4%, respectively. The area under the curve, sensitivity and specificity of the original BI-RADS classification were 0.883, 82.0% and 87.7%, respectively. Differences were statistically significant (p = 0.028, Z-test). The diagnostic sensitivity and specificity were increased effectively. As a new method, BI-RADS classification in conjunction with SWE that combines the average Young's modulus yields a high value in terms of the differential diagnosis of breast nodules.