BI-RADS Category 5 Assessments at Diagnostic Breast Imaging:Outcomes Analysis Based on Lesion Descriptors

RATIONALE AND OBJECTIVES

The Breast Imaging-Reporting and Data System (BI-RADS) atlas defines category 5 assessments as appropriate only for lesions that are almost certainly cancer, with a positive predictive value (PPV) of ≥95%. This study aims to demonstrate the feasibility of classifying lesions at diagnostic breast imaging with sufficiently high PPV to merit category 5 assessments, and to identify those lesion descriptors that yield such a high PPV.

MATERIALS AND METHODS

For this Health Insurance Portability and Accountability Act compliant and IRB exempt study, we reviewed diagnostic breast imaging examinations (mammography and/or ultrasound) assessed as highly suggestive of malignancy (BI-RADS category 5). Pathology diagnosis was considered the gold standard. PPV₃ (biopsy performed) was calculated, and the BI-RADS descriptors for each lesion were analyzed.

RESULTS

Among 22,564 consecutive diagnostic breast imaging examinations between January 2010 and September 2015, we identified 239 exams (1.1%) assessed as BI-RADS category 5 (mean age 62.5 years). Malignancy (invasive breast carcinoma and/or ductal carcinoma in situ) was diagnosed in 233 examinations (PPV₃ 97.5% and 95% confidence interval: 96.2%-98.8%). The most common lesion types were mass (170) and calcifications (116). Of the 220 examinations involving both mammography and ultrasound, no category 5 lesions had <3 suspicious BI-RADS descriptors, only three lesions had three suspicious descriptors, but the remaining 217 lesions (98.6%) had ≥4 suspicious descriptors.

CONCLUSION

In clinical practice, it is feasible to make BI-RADS category 5 assessments with the intended ≥95% PPV. To justify a category 5 assessment, at least four suspicious BI-RADS descriptors should be identified at the combination of diagnostic mammography and ultrasound examinations.

Associations between mammographic density and tumor characteristics in Chinese women with breast cancer

PURPOSE

Mammographic density (MD) is a strong risk factor for breast cancer, yet its relationship with tumor characteristics is not well established, particularly in Asian populations.

METHODS

MD was assessed from a total of 2001 Chinese breast cancer patients using Breast Imaging Reporting and Data System (BI-RADS) categories. Molecular subtypes were defined using immunohistochemical status on ER, PR, HER2, and Ki-67, as well as tumor grade. Multinomial logistic regression was used to test associations between MD and molecular subtype (luminal A = reference) adjusting for age, body mass index (BMI), menopausal status, parity, and nodal status.

RESULTS

The mean age at diagnosis was 51.7 years (SD = 10.7) and the average BMI was 24.7 kg/m² (SD = 3.8). The distribution of BI-RADS categories was 7.4% A = almost entirely fat, 24.2% B = scattered fibroglandular dense, 49.4% C = heterogeneously dense, and 19.0% D = extremely dense. Compared to women with BI-RADS = A/B, women with BI-RADS = D were more likely to have HER2-enriched tumors (OR = 1.81, 95% CI 1.08-3.06, p = 0.03), regardless of menopausal status. The association was only observed in women with normal (< 25 kg/m²) BMI (OR = 2.43, 95% CI 1.24-4.76, p < 0.01), but not among overweight/obese women (OR: 0.98, 95% CI 0.38-2.52, p = 0.96).

CONCLUSIONS

Among Chinese women with normal BMI, higher breast density was associated with HER2-enriched tumors. The results may partially explain the higher proportion of HER2+ tumors previously reported in Asian women.

Improvement of early detection of breast cancer through collaborative multi-country efforts: Observational clinical study

AIM

The aim of this paper is to present baseline imaging data and the improvement that was achieved by the participating centers after applying practice-specific interventions that were identified during the course of a multicentric multinational research coordinated project.

INTRODUCTION

The incidence and mortality rates from breast cancer are rising worldwide and particularly rapidly across the countries with limited resources. Due to lack of awareness and screening options it is usually detected at a later stage. Breast cancer screening programs and even clinical services on breast cancer have been neglected in such countries particularly due to lack of available equipment, funds, organizational structure and quality criteria.

MATERIALS AND METHODS

A harmonized form was designed in order to facilitate uniformity of data collection. Baseline data such as type of equipment, number of exams, type and number of biopsy procedures, stage of cancer at detection were collected from 10 centers (9 countries: Bosnia-Herzegovina, Costa Rica, Egypt, India, North Macedonia, Pakistan, Slovenia, Turkey, Uganda) were collected. Local practices were evaluated for good practice and specific interventions such as training of professionals and quality assurance programs were identified. The centers were asked to recapture the data after a 2-year period to identify the impact of the interventions.

RESULTS

The data showed increase in the number of training of relevant professionals, positive changes in the mammography practice and image guided interventions. All the centers achieved higher levels of success in the implementation of the quality assurance procedures.

CONCLUSION

The study has encountered different levels of breast imaging practice in terms of expertise, financial and human resources, infrastructure and awareness. The most common challenges were the lack of appropriate quality assurance programs and lack of trained skilled personnel and lack of high-quality equipment. The project was able to create higher levels of breast cancer awareness, collaboration amongst participating centers and professionals. It also improved quality, capability and expertise in breast imaging particularly in centers involved diagnostic imaging.

Solitary, Well-Circumscribed, T2 Hyperintense Masses on MRI Have Very Low Malignancy Rates

OBJECTIVE

The purpose of this study was to determine the malignancy rate of solitary MRI masses with benign BI-RADS descriptors.

METHODS

A retrospective review was conducted of all breast MRI reports that described a mass with a final BI-RADS assessment of 3, 4, or 5, from February 1, 2005, through February 28, 2014 (n = 1510). Studies were excluded if the mass was not solitary, did not meet formal criteria for a mass, or had classically suspicious BI-RADS features (e.g., washout kinetics, and spiculated margin). The masses were reviewed by 2 fellowship-trained breast radiologists who reported consensus BI-RADS mass margin, shape, internal-enhancement, and kinetics descriptors. The T2 signal was reported as hyperintense if equal to or greater than the signal intensity of the axillary lymph nodes. Pathology results or 2 years of imaging follow-up were recorded. Comparisons were made between mass descriptors and clinical outcomes.

RESULTS

There were 127 women with 127 masses available for analysis. There were 76 (60%) masses that underwent biopsy for an overall malignancy rate of 4% (5/127): 2 ductal carcinoma in situ (DCIS) and 3 invasive ductal carcinoma. The malignancy rate was 2% (1/59) for T2 hyperintense solitary masses. The malignancy rate was greater than 2% for all of the following BI-RADS descriptors: oval (3%, 3/88), round (5%, 2/39), circumscribed (4%, 5/127), homogeneous (4%, 3/74), and dark internal septations (4%, 2/44).

CONCLUSION

T2 hyperintense solitary masses without associated suspicious features have a low malignancy rate, and they could be considered for a BI-RADS 3 final assessment.

Can acoustic structural quantification be used to characterize the ultrasound echotexture of the peripheral zone of breast lesions?

BACKGROUND:

Besides mammography, breast ultrasound is the most important imaging modality for women with suspected breast cancer. New software tools bear high potential for improved detectability and specification of malignant breast lesions.

OBJECTIVE:

To compare the halo depicted around malignant breast lesions by ultrasound using Acoustic Structure Quantification (ASQ) of raw image data with the echogenic rim seen in B-mode ultrasound.

METHODS:

This retrospective study included 37 women for whom conventional B-mode ultrasound of the breast and ASQ were available as well as histopathology findings for comparison. Software tools were used to measure the halo area or echogenic rim and tumor area and calculate halo-to-lesion ratios for the two ultrasound modes. Six inexperienced readers characterized the breast lesions based on this information. Specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were determined. ANOVA, the Wilcoxon test, and ROC curve analysis were performed.

RESULTS:

There was a linear relationship between ASQ-based and B-mode-based halo-to-lesion ratios; however, a systematic error was also noted. ASQ-derived ratios tended to be higher for breast lesions with lymphangioinvasion (p = 0.051, n.s.) and higher N-stages (p > 0.925, n.s.), while there was no correlation with other markers. Because of the significantly greater conspicuity of peritumoral halos in the ASQ mode, inexperienced readers achieved greater sensitivity (78% vs. 74%) and specificity (75% vs. 71%) and higher NPVs (75% vs. 71%) and PPVs (78% vs. 74%) compared with B-mode images. Greater halo conspicuity affected the identification of malignant lesions with both modes; ASQ was found to be particularly well suited (F_Bimage (1,100) = 19.253, p < 0.001; F_ASQ (1,100) = 52.338, p < 0.001). The inexperienced readers were significantly more confident about their diagnosis using the ASQ maps (z = –3.023, p = 0.003).

CONCLUSIONS:

We conclude that the halo in ASQ and the echogenic rim in B-mode ultrasound are attributable to different morphologic correlates. ASQ improves diagnostic accuracy and confidence of inexperienced examiners because of improved halo visibility.

The relationship between terminal duct lobular unit features and mammographic density among Chinese breast cancer patients

Extensive mammographic density (MD), a well-established breast cancer risk factor, is a radiological representation of stromal and epithelial breast tissue content. In studies conducted predominantly among Caucasian women, histologic measures of reduced terminal duct lobular unit (TDLU) involution have been correlated with extensive MD, but independently associated with breast cancer risk. We therefore examined associations between TDLU measures and MD among Chinese women, a low-risk population but with high prevalence of dense breasts. Diagnostic pre-treatment digital mammograms were obtained from 144 breast cancer cases at a tertiary hospital in Beijing and scored using the Breast Imaging Reporting and Data System (BI-RADS) density classification. TDLU features were assessed using three standardized measures (count/100 mm² , span [μm], and acini count/TDLU) in benign tissues. Associations between each of TDLU measures and MD were examined using generalized linear models for TDLU count and span and polytomous logistic regression for acini count with adjustment for potential confounders stratified by age. Among women ≥50 years, 63% had dense breasts; cases with dense breasts (BI-RADS, c-d) had greater TDLU count (21.1 [SE = 2.70] vs. 9.0 [SE = 1.83]; p = 0.0004), longer span (480.6 μm [SE = 24.6] vs. 393.8 μm [SE = 31.8]; p = 0.03), and greater acini count (OR_trend = 16.1; 95%CI = 4.08-63.1; p_trend < 0.0001) compared to those with non-dense breasts (BI-RADS, a-b). Among women <50 years, 91% had dense breasts, precluding our ability to detect associations. Our findings are consistent with previously reported associations between extensive MD and reduced TDLU involution, supporting the hypothesis that breast cancer risk associated with extensive MD may be related to the amount of "at-risk" epithelium.

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

Objective:

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

Methods:

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

Result:

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

Conclusion:

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

[Comparison of Kinetic Curve between Gadodiamide Hydrate and Gadobutrol on Breast Dynamic Contrast-enhanced Magnetic Resonance Imaging in Invasive Ductal Carcinoma]

The shape of the kinetic curve for gadobutrol is reportedly different compared with that for other conventional contrast agents. We speculate that the shape of gadobutrol kinetic curve may be influenced by different magnetic resonance imaging (MRI) protocols and evaluation methods. The purpose of our study was to assess the influence between gadobutrol and other conventional contrast agent (gadodiamide hydrate) on the kinetic curve in invasive ductal carcinoma (IDC). We assessed 139 women of IDC in this study. Gadodiamide hydrate (2 ml/s) was administered to 69 women, and gadobutrol (1 ml/s) was administrated to 70 women, both contrast agents at 0.1 mmol/kg BW. When the kinetic curves of contrast agents were evaluated between by Breast Imaging Reporting and Data System (BI-RADS) 4th edition and BI-RADS 5th edition, suggested that the analysis method of BI-RADS may affect. Patient group who were administered gadobutrol demonstrated a lower washout rate when compared with patient group who were administered gadodiamide hydrate administration (P<0.01). These results suggest that the kinetic curve characteristics of gadobutrol are an important consideration in diagnosis. Therefore, it is necessary to perform image diagnosis by considering the influence of the contrast agent and the analysis method, when image diagnostic doctor perform image diagnosis.

Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy

Background

The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T₂‐weighted and dynamic contrast‐enhanced (DCE) MRI sequences. The addition of diffusion‐weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE‐MRI, DWI, and T₂‐weighted imaging are most strongly associated with a breast cancer diagnosis.

Purpose/Hypothesis

To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI‐RADS recommended descriptors for breast MRI with DCE, T₂‐weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.

Study Type

Retrospective.

Subjects

In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.

Field Strength/Sequence

IR inversion recovert DCE‐MRI dynamic contrast‐enhanced magnetic resonance imaging VIBE Volume‐Interpolated‐Breathhold‐Examination FLASH turbo fast‐low‐angle‐shot TWIST Time‐resolved angiography with stochastic Trajectories.

Assessment

Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T₂‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T₂‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10⁻³ mm²/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.

Statistical Tests

χ² test, Fisher's exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer–Lemeshow test of goodness‐of‐fit, receiver operating characteristics analysis.

Results

In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T₂‐weighted imaging variables were not included in the final models.

Data Conclusion

mpMRI with DCE‐MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE‐MRI and DWI identifies breast cancer with a high diagnostic accuracy. T₂‐weighted imaging does not significantly contribute to breast cancer diagnosis.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:864–874.

Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use

OBJECTIVES

With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist's opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist's opinion (in the form of BI-RADS classification) in addition to image parameters improved the accuracy of prediction of histology using three machine learning algorithms vis-à-vis algorithms using image parameters alone.

MATERIALS AND METHODS

BI-RADS data sets were obtained from the University of California, Irvine Machine Learning Repository (data set 1) and the Digital Database for Screening Mammography repository (data set 2); three machine learning algorithms were trained using 10-fold cross-validation. Two sets of models were trained: M1, using lesion shape, margin, density, and patient age for data set 1 and image texture parameters for data set 2, and M2, using the previous image parameters and the BI-RADS classification provided by radiologists. The area under the curve and the Gini coefficient for M1 and M2 were compared for the validation data set.

RESULTS

The models using the radiologist-provided BI-RADS classification performed significantly better than the models not using them (P < .0001).

CONCLUSION

AI and radiologist working together can achieve better results, helping in case-based decision making. Further evaluation of the metrics involved in predictor handling by AI algorithms will provide newer insights into imaging.

Identification of Preoperative Magnetic Resonance Imaging Features Associated with Positive Resection Margins in Breast Cancer: A Retrospective Study

Objective

To determine which preoperative breast magnetic resonance imaging (MRI) findings and clinicopathologic features are associated with positive resection margins at the time of breast-conserving surgery (BCS) in patients with breast cancer.

Materials and Methods

We reviewed preoperative breast MRI and clinicopathologic features of 120 patients (mean age, 53.3 years; age range, 27–79 years) with breast cancer who had undergone BCS in 2015. Tumor size on MRI, multifocality, patterns of enhancing lesions (mass without non-mass enhancement [NME] vs. NME with or without mass), mass characteristics (shape, margin, internal enhancement characteristics), NME (distribution, internal enhancement patterns), and breast parenchymal enhancement (BPE; weak, strong) were analyzed. We also evaluated age, tumor size, histology, lymphovascular invasion, T stage, N stage, and hormonal receptors. Univariate and multivariate logistic regression analyses were used to determine the correlation between clinicopathological features, MRI findings, and positive resection margins.

Results

In univariate analysis, tumor size on MRI, multifocality, NME with or without mass, and segmental distribution of NME were correlated with positive resection margins. Among the clinicopathological factors, tumor size of the invasive breast cancer and in situ components were significantly correlated with a positive resection margin. Multivariate analysis revealed that NME with or without mass was an independent predictor of positive resection margins (odds ratio [OR] = 7.00; p < 0.001). Strong BPE was a weak predictor of positive resection margins (OR = 2.59; p = 0.076).

Conclusion

Non-mass enhancement with or without mass is significantly associated with a positive resection margin in patients with breast cancer. In patients with NME, segmental distribution was significantly correlated with positive resection margins.

Breast pathology and mammography BI-RADS category correlation study - A single institute experience

Although recent technological advances, there is still discordance between mammography findings and pathologic diagnoses, especially for certain racial/ethnic populations. In this study we correlated the mammography BI-RADS categories with pathologic diagnoses, aiming to evaluate the performance of mammography in breast cancer detection in a unique poor population consisting of mostly Hispanics and African Americans. A total of 3935 female patients with a breast mammography and a subsequent breast pathology report within 90 days were retrospectively analyzed. There were 875 (22.2%) patients with a negative or probably benign mammography (BI-RADS 1, 2 and 3), and 33 (3.8%) of them had a malignant pathologic diagnosis. Patients with malignant pathologic diagnoses were older, higher in socioeconomic status (SES), and more likely to be African American or White, compared to those with non-malignant pathologic findings. They mostly presented with related symptoms (e.g. breast pain, mass or discharge) and/or family history or past history of breast cancers, which triggered secondary imaging examination and subsequent breast biopsy/excision, and eventually resulted to the diagnosis of breast cancers. In conclusion, our studies indicated that the performance of mammography is comparable in detection of breast cancers among Hispanics, African American and White populations, if it was done in the same facility. Our results also suggested that for patients with presenting symptoms, past history of breast cancer or strong family history of breast cancer, a secondary breast imaging examination may be warranted following a negative to probably benign mammography (BI-RADS 1-3).

Breast ultrasound: recommendations for information to women and referring physicians by the European Society of Breast Imaging

This article summarises the information that should be provided to women and referring physicians about breast ultrasound (US). After explaining the physical principles, technical procedure and safety of US, information is given about its ability to make a correct diagnosis, depending on the setting in which it is applied. The following definite indications for breast US in female subjects are proposed: palpable lump; axillary adenopathy; first diagnostic approach for clinical abnormalities under 40 and in pregnant or lactating women; suspicious abnormalities at mammography or magnetic resonance imaging (MRI); suspicious nipple discharge; recent nipple inversion; skin retraction; breast inflammation; abnormalities in the area of the surgical scar after breast conserving surgery or mastectomy; abnormalities in the presence of breast implants; screening high-risk women, especially when MRI is not performed; loco-regional staging of a known breast cancer, when MRI is not performed; guidance for percutaneous interventions (needle biopsy, pre-surgical localisation, fluid collection drainage); monitoring patients with breast cancer receiving neo-adjuvant therapy, when MRI is not performed. Possible indications such as supplemental screening after mammography for women aged 40-74 with dense breasts are also listed. Moreover, inappropriate indications include screening for breast cancer as a stand-alone alternative to mammography. The structure and organisation of the breast US report and of classification systems such as the BI-RADS and consequent management recommendations are illustrated. Information about additional or new US technologies (colour-Doppler, elastography, and automated whole breast US) is also provided. Finally, five frequently asked questions are answered.

TEACHING POINTS

• US is an established tool for suspected cancers at all ages and also the method of choice under 40. • For US-visible suspicious lesions, US-guided biopsy is preferred, even for palpable findings. • High-risk women can be screened with US, especially when MRI cannot be performed. • Supplemental US increases cancer detection but also false positives, biopsy rate and follow-up exams. • Breast US is inappropriate as a stand-alone screening method.

Downgrading and Upgrading Gray-Scale Ultrasound BI-RADS Categories of Benign and Malignant Masses With Optoacoustics: A Pilot Study

OBJECTIVE

False-positive findings remain challenging in breast imaging. This study investigates the incremental value of optoacoustic imaging in improving BI-RADS categorization of breast masses at ultrasound.

SUBJECTS AND METHODS

The study device is an optoacoustic breast imaging device with a handheld duplex laser and internal gray-scale ultrasound probe, fusing functional and morphologic information (optoacoustic ultrasound). In this prospective multisite study, breast masses assessed as BI-RADS category 3, 4A, 4B, 4C, or 5 by site radiologists underwent both gray-scale ultrasound and optoacoustic imaging with the study device. Independent reader radiologists assessed internal gray-scale ultrasound and optoacoustic ultrasound features for each mass and assigned a BI-RADS category. The percentage of mass reads for which optoacoustic ultrasound resulted in a downgrade or upgrade of BI-RADS category relative to internal gray-scale ultrasound was determined.

RESULTS

Of 94 total masses, 39 were biopsy-proven malignant, 44 were biopsy-proven benign, and 11 BI-RADS category 3 masses were stable at 12-month follow-up. The sensitivity of both optoacoustic ultrasound and internal gray-scale ultrasound was 97.1%. The specificity was 44.3% for optoacoustic ultrasound and 36.4% for internal gray-scale ultrasound. Using optoacoustic ultrasound, 41.7% of benign masses or BI-RADS category 3 masses that were stable at 12-month follow-up were downgraded to BI-RADS category 2 by independent readers; 36.6% of masses assigned BI-RADS category 4A were downgraded to BI-RADS category 3 or 2, and 10.1% assigned BI-RADS category 4B were downgraded to BI-RADS category 3 or 2. Using optoacoustic ultrasound, independent readers upgraded 75.0% of the malignant masses classified as category 4A, 4B, 4C, or 5, and 49.4% of the malignant masses were classified as category 4B, 4C, or 5.

CONCLUSION

Optoacoustic ultrasound resulted in BI-RADS category downgrading of benign masses and upgrading of malignant masses compared with gray-scale ultrasound.

Does fine-needle aspiration biopsy still have a place in the diagnosis of breast lesions?

Introduction

Core needle biopsy is a preferable breast biopsy technique under ultrasound guidance. However, fine-needle biopsy is considered equally popular.

Aim of the study

To redefine the role of fine-needle aspiration biopsy (FNAB) in the diagnosis of breast lesions.

Material and methods

We retrospectively analysed the medical records of 680 patients who underwent breast ultrasound examination. In most cases, no pathologic structures were observed within the mammary glands. In 321 patients, the presence of focal lesions was revealed, and 107 patients in this group qualified for FNAB according to current recommendations. Patients with suspicious smears were referred for core needle or surgical biopsy. Patients with benign cytological smears underwent repeated ultrasound checks at 6-month intervals during the following year.

Results

All the smears were diagnostic. The vast majority of the results were categorised as benign lesions. Cancer cells were detected in six women. In one patient the lesion was classified as suspicious, probably malignant. In all of these cases, open biopsy was performed, and histopathological examination confirmed the presence of a malignant tumour. The patients were given appropriate oncological treatment. For women with benign or suspicious, but probably benign, lesions, breast ultrasound was performed twice at six-month intervals. Control tests showed no significant changes compared to the baseline examination. None of the patients required extensive additional diagnostic tests.

Conclusions

FNAB is a reliable method of assessing pathologic lesions in mammary glands.

Sonographic features of invasive ductal breast carcinomas predictive of malignancy grade

Context:

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

Aims:

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

Settings and Design:

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

Materials and Methods:

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

Statistical Analysis Used:

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

Results:

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

Conclusions:

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

Ultrasound shear wave elastography of breast lesions: correlation of anisotropy with clinical and histopathological findings

Background

Ultrasound shear-wave elastography (SWE) may increase specificity of breast lesion assessment with ultrasound, but elasticity measurements may change with transducer orientation, defined as anisotropy. In this study, we aimed to observe the anisotropy of SWE of breast lesions, and its correlation with clinical and histopathological findings.

Methods

This retrospective study was approved by institutional review board. From June 2014 to June 2015, a total of 276 women (mean age, 48.75 ± 12.12 years) with 276 breast lesions (174 malignant, 102 benign) were enrolled for conventional ultrasound and SWE before surgical excision. Elasticity modulus in the longest diameter and orthogonal diameter were recorded, including maximum elasticity (Emax), mean elasticity (Emean), standard deviation (Esd) and ratio between mean elasticity of lesion and normal fatty tissue (Eratio). Anisotropy coefficients including anisotropic difference (AD) and anisotropy factors (AF) were calculated, and correlations with malignancy, tumor size, palpability, movability, lesion location and histopathology were analyzed.

Results

The average Emax, Emean, Esd and Eratio of the longest diameter were significantly higher than orthogonal diameter (P < 0.05). AUCs of ADs and AFs were inferior to quantitative parameters (P < 0.001), with AUCs of AFs superior to ADs (P < 0.001). ADs showed no significant correlation with malignancy, palpability, movability, distance from nipple and skin, and histopathological patterns. ADmean was significantly higher in inner half than outer half of the breast (P = 0.034). Higher AFs were significantly correlated with larger lesion size (P = 0.042), palpability (P < 0.05), shorter distance from nipple and skin (P < 0.05) and higher suspicion for malignancy (P < 0.001). AFs were significantly higher in IDC than DCIS (P < 0.05), higher in Grade II/III than Grade I IDC (P < 0.001), and correlated with ER/PR(+) (P < 0.05).

Conclusions

AF of SWE was an indicator for malignancy and more aggressive breast cancer.

Patient Perceptions of Breast Cancer Risk in Imaging-Detected Low-Risk Scenarios and Thresholds for Desired Intervention: A Multi-Institution Survey

PURPOSE

To determine women's perceptions of breast cancer risk and thresholds for desiring biopsy when considering BI-RADS 3 and 4A scenarios and recommendations, respectively.

MATERIALS AND METHODS

Women presenting for screening mammography from five geographically diverse medical centers were surveyed. Demographic information and baseline anxiety were queried. Participants were presented with scenarios of short-term imaging follow-up recommendations (ie, BI-RADS 3) and biopsy recommendations (ie, BI-RADS 4A) for low-risk mammographic abnormalities and asked to estimate their breast cancer risk for each scenario. Participants reported the threshold (ie, likelihood of cancer) where they would feel comfortable undergoing short-term imaging follow-up and biopsy and their anticipated regret for choosing short-term follow-up versus biopsy.

RESULTS

Analysis of 2,747 surveys showed that participants estimated breast cancer risk of 32.8% for a BI-RADS 3 and 41.1% for a BI-RADS 4A scenarios are significantly greater rates than clinically established rates (<2% [P < .001] and 2%-10% [P < .001], respectively). Over one-half (55.4%) of participants reported they would never want imaging follow-up if there was any chance of cancer; two-thirds (66.2%) reported they would desire biopsy if there was any chance of cancer. Participants reported greater anticipated regret (P < .001) and less relief and confidence (P < .001) with the decision to undergo follow-up imaging versus biopsy.

CONCLUSION

Women overestimate breast cancer risk associated with both BI-RADS 3 and 4A scenarios and desire very low biopsy thresholds. Greater anticipated regret and less relief and confidence was reported with the choice to undergo short-term imaging follow-up compared with biopsy.

Evaluation of Quantra Hologic Volumetric Computerized Breast Density Software in Comparison With Manual Interpretation in a Diverse Population

Objective:

Increased mammographic breast density is a well-established risk factor for breast cancer development, regardless of age or ethnic background. The current gold standard for categorizing breast density consists of a radiologist estimation of percent density according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) criteria. This study compares paired qualitative interpretations of breast density on digital mammograms with quantitative measurement of density using Hologic’s Food and Drug Administration–approved R2 Quantra volumetric breast density assessment tool. Our goal was to find the best cutoff value of Quantra-calculated breast density for stratifying patients accurately into high-risk and low-risk breast density categories.

Methods:

Screening digital mammograms from 385 subjects, aged 18 to 64 years, were evaluated. These mammograms were interpreted by a radiologist using the ACR’s BI-RADS density method, and had quantitative density measured using the R2 Quantra breast density assessment tool. The appropriate cutoff for breast density–based risk stratification using Quantra software was calculated using manually determined BI-RADS scores as a gold standard, in which scores of D3/D4 denoted high-risk densities and D1/D2 denoted low-risk densities.

Results:

The best cutoff value for risk stratification using Quantra-calculated breast density was found to be 14.0%, yielding a sensitivity of 65%, specificity of 77%, and positive and negative predictive values of 75% and 69%, respectively. Under bootstrap analysis, the best cutoff value had a mean ± SD of 13.70% ± 0.89%.

Conclusions:

Our study is the first to publish on a North American population that assesses the accuracy of the R2 Quantra system at breast density stratification. Quantitative breast density measures will improve accuracy and reliability of density determination, assisting future researchers to accurately calculate breast cancer risks associated with density increase.

A case-oriented web-based training system for breast cancer diagnosis

BACKGROUND AND OBJECTIVE

Breast cancer is still considered as the most common form of cancer as well as the leading causes of cancer deaths among women all over the world. We aim to provide a web-based breast ultrasound database for online training inexperienced radiologists and giving computer-assisted diagnostic information for detection and classification of the breast tumor.

METHODS

We introduce a web database which stores breast ultrasound images from breast cancer patients as well as their diagnostic information. A web-based training system using a feature scoring scheme based on Breast Imaging Reporting and Data System (BI-RADS) US lexicon was designed. A computer-aided diagnosis (CAD) subsystem was developed to assist the radiologists to make scores on the BI-RADS features for an input case. The training system possesses 1669 scored cases, where 412 cases are benign and 1257 cases are malignant. It was tested by 31 users including 12 interns, 11 junior radiologists, and 8 experienced senior radiologists.

RESULTS

This online training system automatically creates case-based exercises to train and guide the newly employed or resident radiologists for the diagnosis of breast cancer using breast ultrasound images based on the BI-RADS. After the trainings, the interns and junior radiologists show significant improvement in the diagnosis of the breast tumor with ultrasound imaging (p-value < .05); meanwhile the senior radiologists show little improvement (p-value > .05).

CONCLUSIONS

The online training system can improve the capabilities of early-career radiologists in distinguishing between the benign and malignant lesions and reduce the misdiagnosis of breast cancer in a quick, convenient and effective manner.

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

BACKGROUND AND OBJECTIVE

Conventional 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.

METHODS

The 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 RF_w, 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 RF_p.

RESULTS

The 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 RF_w classifier obtained AUC=0.872,SEN=0.826, and SPE=0.919, whereas the RF_p classifier reached AUC=0.868,SEN=0.808, and SPE=0.929. According to a one-way analysis of variance test, the RF_w classifier statistically outperforms (p < 0.001) the RF_p classifier in terms of the AUC and SEN. Moreover, the classification performance of RF_w to predict weighted BI-RADS classes is given by the Matthews correlation coefficient that obtained 0.614.

CONCLUSIONS

The 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.

A deep learning method for classifying mammographic breast density categories

PURPOSE

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.

METHODS

In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.

RESULTS

The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.

CONCLUSIONS

Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.

The Value of Lesion Size as an Adjunct to the BI-RADS-MRI 2013 Descriptors in the Diagnosis of Solitary Breast Masses

Purpose:

This study aimed to evaluate the MRI findings of breast solitary masses in diagnostic procedures to decide the appropriate category based on American College of Radiology (ACR) BI-RADS-MRI 2013, with the focus on lesion size.

Methods:

A retrospective review of 2,603 consecutive breast MRI reports identified 250 pathologically-proven solitary breast masses. Dynamic-contrast enhanced images and diffusion-weighted images were performed on a 3.0/1.5 Tesla Scanner with a 16/4 channel dedicated breast coil. MRI findings were re-evaluated according to ACR BI-RADS-MRI 2013. BI-RADS-MRI descriptors, lesion size and minimum apparent diffusion coefficient (ADC) value were statistically analyzed using univariate/multivariate logistic regression analysis and receiver operator characteristic (ROC) analysis. Based on the results, a diagnostic decision tree was constructed.

Results:

Of the 250 lesions, 152 (61%) were malignant and 98 (39%) were benign. In univariate logistic regression analysis, most of the BI-RADS descriptors, lesion size, and ADC value were significant. Lesion size and ADC value were binarized with optimal cut-off values of 12 mm and 1.1 × 10⁻³ mm²/s, respectively. Multivariate logistic regression analysis showed that lesion size (≥12 mm or not), margin (circumscribed or not), kinetics (washout or not) and internal enhancement characteristics (IEC) (rim enhancement present or absent) significantly contributed to the diagnosis (P < 0.05). Using these four significant parameters, a decision tree was constructed to categorize lesions into detailed assessment categories/subcategories (Category 4A, 4B, 4C and 5).

Conclusion:

Lesion size is an independent contributor in diagnosing solitary breast masses. Adding the information of lesion size to BI-RADS-MRI 2013 descriptors will allow more detailed categorizations.

Background echotexture classification in breast ultrasound: inter-observer agreement study

Background According to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS), background echotexture in breast ultrasound (US) can be categorized as homogeneous or heterogeneous. Purpose To prospectively evaluate the inter-observer agreement of a four-category classification in background echotexture assessments of breast US and to determine whether background echotexture is related to breast cancer risk factors, including mammography density. Material and Methods Thirty-eight healthy women (age range, 25-72) were recruited. Eleven radiologists performed breast US on all participants and classified each background echotexture into four categories (homogeneous, mild, moderate, and marked heterogeneous). The inter-observer agreement in the assessments was measured using kappa statistics (к). The association between background echotexture and breast cancer risk factors, including mammographic density, menopausal status, and parity, were evaluated using Spearman's correlation coefficient (ρ) and multiple linear regression analysis. Results There was moderate inter-observer agreement between the radiologists for the four categories of background echotexture (average к = 0.45). Heterogeneity of the background echotexture was positively correlated with mammographic density in both pre- and postmenopausal women (premenopausal, ρ = 0.42, P < 0.0001; postmenopausal, ρ = 0.56, P < 0.0001). Multiple linear regression analysis revealed that mammographic density and parity were significantly associated with background echotexture. Conclusion Background echotexture assessment of breast US using a four-category classification showed moderate inter-observer agreement, and more heterogeneous background echotexture was associated with denser breasts and lower parity.