Conventional Digital Mammography Research Articles

Comparing breast tomosynthesis with conventional digital mammography in the evaluation of the breast

Comparing breast tomosynthesis with conventional digital mammography in the evaluation of the breast

A randomised controlled trial of digital breast tomosynthesis vs digital mammography as primary screening tests: Screening results over subsequent episodes of the Proteus Donna study.

Proteus Donna is a randomised controlled trial aimed at prospectively evaluating screening with digital breast tomosynthesis (DBT), including interval cancer detection (ICD) and cancer detection (CD) in the analysis as a cumulative measure over subsequent screening episodes. Consenting women aged 46 to 68 attending the regional Breast Screening Service were randomly assigned to conventional digital mammography (DM, control arm) or DBT in addition to DM (DBT, study arm). At the subsequent round all participants underwent DM. Thirty-six months follow-up allowed for the identification of cancers detected in the subsequent screening and interscreening interval. Relative risk (RR) and 95% confidence interval (95% CI) were computed. Cumulative CD and Nelson-Aalen incidence were analysed over the follow-up period. Between 31 December 2014 and 31 December 2017, 43 022 women were randomised to DM and 30 844 to DBT. At baseline, CD was significantly higher (RR: 1.44, 95% CI: 1.21-1.71) in the study arm. ICD did not differ significantly between the two arms (RR: 0.92, 95% CI: 0.62-1.35). At subsequent screening with DM, the CD was lower (nearly significant) in the study arm (RR: 0.83, 95% CI: 0.65-1.06). Over the follow-up period, the cumulative CD (comprehensive of ICD) was slightly higher in the study arm (RR: 1.15, 95% CI: 1.01-1.31). The Nelson-Aalen cumulative incidence over time remained significantly higher in the study arm for approximately 24 months. Benign lesions detection was higher in the study arm at baseline and lower at subsequent tests. Outcomes are consistent with a lead time gain of DBT compared to DM, with an increase in false positives and moderate overdiagnosis.

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT)

The aim of this retrospective study is to compare the radiation dose received during CEDM, short and long protocol (CEDM SP and CEDM LP), with dose received during DM and DBT on patients with varying breast thickness, age and density. Between January 2019 and December 2019, patients having 6214 DM, 3662 DBT and 173 CEDM examinations in our department were analyzed. Protocol total single breast AGD has been evaluated for all clinical imaging protocols, extracting AGD values and exposure data from the dose DICOM Structured Report (SR) information stored in the hospital PACS system. Protocol AGD was calculated as the sum of single projection AGDs carried out in every exam for each clinical protocol. A total amount of 23,383 exams for each breast were analyzed. Protocol AGDs, stratified as a function of patient breast compression thickness, age, and breast density were assessed. The total protocol AGD median values for each protocol are: 2.8 mGy for DM, 3.2 mGy for DBT, 6.0 mGy for DM+DBT, 4.5 mGy for CEDM SP, 7.4 mGy for CEDM SP_DBT (CEDM SP protocol with DBT), 8.4 mGy for CEDM LP and 11.6 mGy for CEDM LP_DBT (CEDM LP protocol with DBT). CEDM SP AGD median value is 59% higher than DM AGD median value and 40% lesser than DM+DBT AGD median; this last difference was statistically confirmed with a p-value <0.001. AGD value for each standard breast CEDM SP projection results to be below 3-mGy limit. AGD value for each standard breast CEDM SP projection results to be below 3 mGy, as required by international legislation. For dense breasts, the AGD median value is 4.2 mGy, with the first and third quartile of 3.3 mGy and 6.0 mGy respectively; for non-dense breasts, the AGD median value is 4.7 mGy, with first and third quartile of 3.5 mGy and 6.3 mGy respectively. The difference between the two groups was statistically tested and confirmed, with a p-value of 0.039. CEDM SP results in higher radiation exposure compared with conventional DM and DBT but lower than the Combo mode. The dose administered during the CEDM SP is lower in patients with dense breasts regardless of their size. An interesting outcome, considering the ongoing studies on CEDM screening in patients with dense breasts.

OD123 - Small-size details detection performance of digital breast tomosynthesis, synthetic 2D and conventional full-field digital mammography images for different mammography systems: a multicenter study

OD123 - Small-size details detection performance of digital breast tomosynthesis, synthetic 2D and conventional full-field digital mammography images for different mammography systems: a multicenter study

Special issue on future hybrid artificial intelligence and machine learning for smart expert systems

Artificial Intelligence (AI) has grown extensively in recent times and already encroaches into our lives, for example, with smart cars and smart homes. AI current and future trends in areas such as smart cars, face recognition, robotic surgeries, or automatic disease classification, focus on hybridization to improve the performance of smart expert systems. Despite the huge growth of various AI and machine learning techniques, such as decision trees, data mining, or deep convergence learning, there are still many challenges and threats which limit the performance of these smart expert systems. This special issue focuses on new directions in hybrid AI and machine learning methodologies to address current research problems. It focuses on bringing together leading-edge articles on hybrid AI techniques for smart expert systems for various applications. From about 25 articles submitted, 4 papers were selected based on the review process. Each paper was reviewed by at least two independent reviewers and undertook at least one round of review. Their contributions are summarized below. Raghavendra et al. (2019) has proposed a research work on accurate and early detection of breast cancer using effective imaging modalities with medical image analysis. The research results have shown that two-dimensional synthesized mammogram imaging and conventional full-field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold cross validation. The obtained results can be used as a distinct system in rural hospitals. Gupta et al. (2020) has done their research studies on to detect infected leaves using machine learning techniques. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private data set of 236 images. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for the classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. Hammad et al. (2020) has found a new authentication approach based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals. This work shows two novel deep neural network (DNN) models (convolutional neural network (CNN) and ResNet-Attention) using ECG signals for human authentication and the proposed CNN algorithm achieved an accuracy of 98.59% and 99.72% using PTB and CYBHi, respectively. The results obtained by the developed model proved that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. Teles et al. (2020) has devised a decision support system on credit operations which can provide different estimations of expected recovery based on the same data sets. This research work classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. Results reveal that while linear regression can be used for the prediction of a continuous variable in the credit process using collateral, it is not suitable for categorical data. Out of linear and logistic regressions, linear regression is more applicable in determining groups of variables that can significantly predict an outcome. We would like to take this opportunity to thank Dr. Jon G. Hall, Editor-in-Chief of Expert Systems, The Journal of Knowledge Engineering, for the privilege to edit this special issue. Gustavo Ramírez González is a professor at department of telematics engineering in Universidad del Cauca, Colombia. His research interests include image processing, secure communication, and machine learning. He has published several research papers in reputed journals and served as a Guest Editor for several Special Issues at many journals.

Added value of contrast-enhanced spectral mammography in symptomatic patients with dense breasts

BackgroundBreast cancer is the most common malignancy in females around the world representing 25.1% of all cancers.The high prevalence and need for early treatment of breast malignancy highlight the importance of early and accurate diagnosis. In order to achieve this target, it is necessary to select the most appropriate modality for investigation.Early detection of breast cancer by conventional mammography tends to reduce mortality; however, it has a low sensitivity and specificity in young females with dense breasts owing to reduced contrast between a possible tumor and the surrounding breast tissue with superimposition of the glandular tissue obscuring underlying lesions.Our study included 25 patients with dense breasts presented with different breast symptoms, yet the breast lump was the most common complaint. The aim of our study is to evaluate the supplementary value of contrast-enhanced spectral mammography in the assessment of symptomatic patients with dense breasts.ResultsIn our study, the enrolled subjects underwent both contrast-enhanced spectral mammography (CESM) and conventional full-field digital mammography (FFDM). CESM was shown to be better than FFDM in terms of sensitivity, positive predictive value, negative predictive value, and accuracy, measuring 100%, 77.8%, 100%, and 84%, compared to 56%, 75%, 46%, and 60%, respectively, yet both modalities showed low specificity, measuring 63.6% and 66.6% for CESM and FFDM, respectively.The added value of CESM was assessed in terms of ability to detect and correctly characterize the lesions in correlation to histopathological results where CESM could detect 88% of the lesions included in our study and correctly characterized 84% of the lesions; on the other side, FFDM detected only 20% of the lesions and correctly characterized 60% of the lesions. CESM changed the treatment plan to a more extensive surgery +/− neoadjuvant chemotherapy in 57% out of fourteen cases diagnosed with breast cancer emphasizing the role of CESM in assessing the extent of the disease, multicentricity, and multifocality and consequently tailoring the most appropriate treatment plan suitable for each patient.ConclusionContrast-enhanced spectral mammography is superior to full-field digital mammography in patients with dense breasts with a significant supplementary value in detection, characterization of lesions, and tailoring the appropriate treatment plan.

COMPARISON OF PRE-OP 2D MAMMOGRAPHY AND 3D MAMMOGRAPHY (TOMOCYNTHESIS) FINDINGS IN PATIENTS DUE TO BREAST LESION

Ponte Academic JournalJul 2021, Volume 77, Issue 7 COMPARISON OF PRE-OP 2D MAMMOGRAPHY AND 3D MAMMOGRAPHY (TOMOCYNTHESIS) FINDINGS IN PATIENTS DUE TO BREAST LESIONAuthor(s): Vugar Samadli ,Sevinc Dagistanli, Mehmet Cingoz, Suleyman SonmezJ. Ponte - Jul 2021 - Volume 77 - Issue 7 doi: 10.21506/j.ponte.2021.7.1 Abstract:Aim: Conventional digital mammography (CDM) is a basic screening method especially used for women over 40 years of age. CDM enable multiple images from different angles and different depth levels. To avoid superposition of healthy and ill breast tissue the Digital Breast Tomosynthesis (DBT) reveals to be a certain method. Thus, we aimed to compare CDM and DBT (Digital Breast Tomosynthesis) images of preoperative lesions to detect their significance. Material and method: We retrospectively compared 646 patients divided into two groups with their CDM and DBT results at our Institution. Only patients with type C and D breast tissue paterns were included. In the first group (76 patients) lesions detected on CDM or DBT images were compared according to 6 parameters with 4 histopathological parameters correlation. In the second group 570 patients with lesions detected on CDM and/or DBT and without histopathological correlation were compared due to the parameters. Findings: There was a statistical significant result for detecting nodulary opacity and distortion with DBT compared to CDM (p<0.05). A parenchymal distorsion-area correlated with histopathology as a maligant lesion was significant better identified on DBT compared to CDM (p<0.05). Detection for nodulary opacity was significant better with DBT compared to CDM (p<0.05). Result: CDM is the gold Standard for detecting preoperative breast lesions and reveals to be the most commonly used method together with ultrasound. According to our study DBT is more successful at detecting findings like nodulary opacities and parenchymal distortion. Download full text:Check if you have access through your login credentials or your institution Username Password

Utilidad clínica de la mamografía con contraste (CEM): una revisión de la literatura

Contrast-enhanced mammography (CEM) is a promising new imaging technique based on neovascularisation of the tumour, similar to magnetic resonance imaging of the breast. The results of clinical studies show that its diagnostic performance is significantly better than that of conventional digital mammography, and that its sensitivity and specificity are comparable to those of magnetic resonance imaging. Its current and proposed uses include further evaluation of symptomatic patients or patients with abnormal results on previous examinations, assessment of the local extension of newly diagnosed breast tumours, follow-up of neoadjuvant chemotherapy and detection of breast cancer in high-risk patients. Although the technique was approved for clinical use in 2010, it is still little known. In this review, we present the technique of CEM and its results in terms of diagnostic performance compared with conventional mammography and breast MRI. We also discuss the various scenarios in which it can be used successfully, both in the diagnosis of breast disease and in the local staging of breast cancer, as well as its potential role in breast cancer screening programmes.

EVALUATION OF DIAGNOSTIC ACCURACY OF ULTRASOUND-GUIDED SEMI-AUTOMATED CORE NEEDLE BIOPSY OF BREAST SUSPICIOUS MICROCALCIFICATIONS

Back ground: Breast microcalcifications are considered an early mammographic sign of breast cancer which are present with tiny bright spots of different morphology. In an ultrasound (US) image, the presence of microcalcifications within breast is an important indicator of malignancy.With an improved sonographic detection of microcalcification, ultrasound could be used for biopsy guidance for target sampling of tissue containing suspicious microcalcifications. If the biopsied lesions had a suspicious microcalcifications, specimen radiograph is mandatory to confirm the presence of microcalcifications in which a core containing any calcification on specimen radiography was defined as asuccessfully retrieved core. 14-G(Gauge) semi-automated core needle biopsy(CNB) device is less costly than other biopsy device. Methods:A prospective study was conducted on thirty female patients with thirty- two lesions their mean age was 48.53 years (age range, 21-75 years) radiologically classified as BIRADS (Breast Imaging Reporting and Data system) 4 (b, c) and5 from those referred to women imaging health unit in Radiology department for Sono- Mammography and those patients were referred from the inpatient wards and outpatient clinics of the surgery departments during the period from September 2018 till May 2019.All patients underwent conventional digital mammography and B-mode ultrasound examination and Ultrasound guided biopsy, specimen radiography then tissue samples were sent to histopathology department in which slide picture was done.Then (28) females done surgery and (2) patient still for follow up. Results: Radiological diagnosis of our lesions using BIRADS(Breast Imaging Reporting and Data system) categorical method were identified as two intermediate suspicious lesions (BIRADS 4B 6.3 %), twelve moderate suspicious lesions (BIRADS 4C 34.4 %), and 19 highly suggestive of malignancy lesions (BIRADS 5 59.4 %). Retrieval rate of calcifications according to specimen radiography were successfully retrieved in 30/32 lesions (93.8 %) and confirmed on specimen radiography .CNB( core needle biopsy) identified 28 lesions (87.5 %) malignant and four (12.5 %) benign lesions. Distribution of lesions according to pathological diagnosis in successful and fail retrieval group, the successful retrieval group comprised 26 malignant (22 IDC(Invasive ductal carcinoma), 2 DCIS(Ductal carcinoma Insitu), 2 ILC(Invasive Lobuler carcinoma) ) and four benign lesions.After CNB( core needle biopsy), 30 lesions underwent surgical excision and final pathology results were provided. All 24 IDC on 14-G semi-automated CNB (core needle biopsy) were confirmed to be IDC(Invasive ductal carcinoma) on final pathology. Of the two DCIS(Ductal carcinoma Insitu) on US(Ultrasound) guided CNB( core needle biopsy), one DCIS, were upgraded to IDC, the other one confirmed to be DCIS on final pathology,the two ILC were confirmed to be ILC on final pathology.Of the four benign lesions, the two ADH(Atypical Ductal hyperplasia) underwent surgical excision, finally diagnosed as DCIS,the other two benign lesions (fibroadenoma and fibroadenosis) their BIRADS assessments were category 4C and were recommended for follow-up.The overall accuracy of US-guided 14-G(Gauge) semi-automated CNB( core needle biopsy) was 90.0 % (27/32). Conclusion: The present study confirms the good retrieval rate (93.8%) of US-guided 14-G semi-automated CNB and diagnostic accuracy of (90.0%). Thus, this could be useful procedure for suspicious microcalcifications apparent on sonography.Specimen radiography proved to be a great help in deciding the accurate retrieval of microclcifications by core biopsy. It should be done in all cases of core biopsy for calcifications with marking of the core containing calcifications to be specially assessed by histopathology. We should try to do core biopsy before using VAB (Vaccum-Assisted Biobsy)for calcifications if well seen by ultrasound.

Contrast-enhanced spectral mammography: A potential exclusion diagnosis modality in dense breast patients.

BackgroundChina has an increasing burden of breast cancer. However, with a large population of dense breast patients, the diagnostic efficiency of conventional digital mammography is attenuated.MethodsFrom July 2017 to October 2018, we retrospectively reviewed 397 dense breast patients who underwent contrast‐enhanced spectral mammography (CESM) in West China Hospital. Among them, 53 patients who had both CESM and dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) results and 114 patients who had pathological diagnoses were finally enrolled. All images were reviewed by two independent radiologists according to the 2013 Breast Imaging Reporting and Data System (BI‐RADS) with all disagreements handed to an associate professor for final decisions. Correlation analyses between CESM and DCE‐MRI were conducted. The diagnostic performance of CESM were investigated.ResultsThe kappa value of the BI‐RADS scores between CESM and DCE‐MRI was 0.607 (P < .001), indicating high correspondence between CESM and DCE‐MRI. As for lesion size measurement, moderate correlation (Kendall's tau coefficient: 0.556, P < .001) was detected between CESM and DCE‐MRI. Using pathological diagnoses as the reference standard, the sensitivity, specificity, and area under the curve (AUC) of CESM were 82.4%, 96.4%, and 0.894, respectively.ConclusionCESM demonstrated excellent overall diagnostic accuracy and a moderate correlation in lesion size estimation against DCE‐MRI in dense breast patients, supporting it to be an alternative to DCE‐MRI in breast cancer detection and diagnosis, especially for exclusion diagnosis.

The Diagnostic Performance of Contrast Enhanced Spectral Mammography in Detecting Occult Breast Lesions in Patients with Dense Breasts

Abstract Background: Contrast Enhanced Spectral Mammography (CESM) is an evolving imaging technique using contrast media to enhance the sensitivity for breast cancer detection and to extend the capability of mammography. Aim of Study: To evaluate the added benefit of Contrast Enhanced Mammography (CESM) in detecting occult breast lesions that escaped conventional Digital Mammography (DM). Patients and Methods: 68 female patients (121 breasts) with dense breasts were enrolled in this study. CESM was performed after conventional DM which was negative. CESM results were compared to sonography and histopathologic results. Diagnostic accuracy of CESM in differentiating malignant from benign lesions was determined. Results: By using CESM, 18 cancers were detected in 68 patients with negative conventional mammographic studies. CESM had sensitivity 88.9%, specificity was 100%, PPV was 100%, NPV was 60% while the overall diagnostic accuracy of CESM was 85.7% in cancer detection. Conclusion: CESM has the potential to increase breast cancer detection rate over conventional DM especially in women with dense breasts. CESM has high specificity and positive predictive value in identifying malignant breast lesions. CESM is a promising tool for screening women with dense breasts and high risk for breast cancer.

Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening With Digital Breast Tomosynthesis in the United States.

Digital breast tomosynthesis (DBT) is increasingly being used for routine breast cancer screening. We projected the long-term impact and cost-effectiveness of DBT compared to conventional digital mammography (DM) for breast cancer screening in the United States. Three Cancer Intervention and Surveillance Modeling Network breast cancer models simulated US women ages 40 years and older undergoing breast cancer screening with either DBT or DM starting in 2011 and continuing for the lifetime of the cohort. Screening performance estimates were based on observational data; in an alternative scenario, we assumed 4% higher sensitivity for DBT. Analyses used federal payer perspective; costs and utilities were discounted at 3% annually. Outcomes included breast cancer deaths, quality-adjusted life-years (QALYs), false-positive examinations, costs, and incremental cost-effectiveness ratios (ICERs). Compared to DM, DBT screening resulted in a slight reduction in breast cancer deaths (range across models 0-0.21 per 1000 women), small increase in QALYs (1.97-3.27 per 1000 women), and a 24-28% reduction in false-positive exams (237-268 per 1000 women) relative to DM. ICERs ranged from $195026 to $270135 per QALY for DBT relative to DM. When assuming 4% higher DBT sensitivity, ICERs decreased to $130533-$156624 per QALY. ICERs were sensitive to DBT costs, decreasing to $78731 to $168883 and $52918 to $118048 when the additional cost of DBT was reduced to $36 and $26 (from baseline of $56), respectively. DBT reduces false-positive exams while achieving similar or slightly improved health benefits. At current reimbursement rates, the additional costs of DBT screening are likely high relative to the benefits gained; however, DBT could be cost-effective at lower screening costs.

Power-law analysis of nonlinear active-pixel detector responses as a function of mammographic energy

Abstract This study investigates the imaging performance of a complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) array coupled with a gadolinium oxysulfide phosphor. A wide range of mammographic energy was considered, which corresponds to an aluminum-equivalent thickness in the range of 0.515 to 0.689 mm in terms of half-value layers. The performance metrics include the modulation-transfer function (MTF), Wiener noise-power spectrum (NPS), and detective quantum efficiency (DQE). The detector showed a nonlinear response at low exposure ranges, so it was described using a power-law model to linearize image data before evaluating the MTF, NPS, and DQE. We show that the NPS, which is measured from images without linearization but corrected by the exponent of the power function (viz., nonlinearity correction), can be consistent with that obtained using the linearization procedure. Negligible difference in MTFs measured with and without linearization showed good agreement between the nonlinearity-corrected and linearized DQEs. The DQE performance of the detector was comparable to published data for conventional digital mammography detectors. However, the detector design with a CMOS APS array in conjunction with a thin phosphor was energy-sensitive. The overall spatial-resolution performance (the size of effective aperture) improved by about 13%, the noise performance was degraded by about 22%, and the zero-frequency DQE was reduced by about 26% when measured with a Mo-target beam quality of 25 kV to 31 kV and a similar exposure of about 6 mR. The detector design may be appropriate for operation in a narrow range of energy. Further design considerations of the phosphor with output that tolerates variations in energy could lead to applications in convertible mammography and tomosynthesis.

Breast tomosynthesis: What do we know and where do we stand?

Digital breast tomosynthesis (DBT) is a new imaging technology that addresses the limitation caused by overlapping structures in conventional two-dimensional digital mammography owing to the acquisition of a series of low-dose projection images. This unique technique provides a dual benefit to patients screened for breast cancer. First, DBT increases the cancer detection rate mostly by highlighting architectural distortions and allowing better assessment of masses shape and margins. Second, DBT helps reduce recall rate by discarding asymmetries related to overlapping tissue. However, DBT is not included in the majority of cancer screening programs worldwide. Several issues still need to be addressed such as over-diagnosis and over-treatment, lack of reduction of interval breast cancer, quality control and storage, and radiation dose. In the diagnostic setting, DBT increases the diagnostic accuracy and reduces the number of indeterminate lesions in symptomatic women. Its aforementioned performances regarding asymmetries, masses and architectural distortions allow reducing the number of additional views while working-up a screening-detected lesion. Tumor size is also better assessed at DBT as well as multicentricity, two significant benefits in the staging of breast cancer. Finally, DBT allows a better analysis of scars and helps reduce the rate of indeterminate findings after surgery. Although somewhat limited by high breast density, DBT globally outperforms digital mammography in both screening and diagnostic breast imaging. Additional research is however needed, particularly on relevant screening outcomes. This review describes the main performances of breast DBT in breast cancer screening and diagnosis and the resulting consequences in both settings.

Comparison of Contrast-Enhanced Mammography With Conventional Digital Mammography in Breast Cancer Screening: A Pilot Study

PurposeTo perform a pilot evaluation of contrast-enhanced mammography (CEM) for screening to determine whether it can improve accuracy and reader confidence in diagnosis. Methods and MaterialsThis institutional review board–approved reader study was comprised of 64 de-identified CEM cases acquired from December 1, 2014, to June 7, 2016, including 48 negative, 5 biopsy-proven benign, and 11 biopsy-proven malignancies. Negative cases were followed for at least 2 years without evidence of cancer. Ten breast imagers of varying experience first rated the low-energy (LE) mammogram and then the CEM examination using BI-RADS categories and a 5-point Likert scale for confidence in diagnosis. ResultsThere were 635 out a total possible 640 complete reader interpretations included in this analysis. The remaining five incomplete interpretations were excluded. Median sensitivity and specificity improved with the addition of CEM (sensitivity: 0.86 [95% confidence interval {CI}: 0.74-0.95] versus 1 [95% CI: 0.83-1.00], specificity: 0.85 [95% CI: 0.64-0.94] versus 0.88 [95% CI: 0.80-0.92]). Individual receiver operating characteristic curves showed significant improvement with CEM (mean area under the curve increase = 0.056 [95% CI: 0.015-0.097], P = .002). The addition of CEM significantly improved average confidence in 5 of 10 readers when compared with LE (P < .0001) and improved pooled confidence across all tissue density categories, except the almost entirely fatty category. There was a trend toward improved confidence with increasing tissue density with CEM. Degree of background parenchymal enhancement did not affect readers’ level of improvement in confidence when interpreting CEM. SummaryCEM improved reader performance and confidence compared with viewing only LE, suggesting a role for CEM in breast cancer screening for which larger trials are warranted.

Accuracy of Synthetic 2D Mammography Compared With Conventional 2D Digital Mammography Obtained With 3D Tomosynthesis.

OBJECTIVE. The purpose of this study was to compare the diagnostic performance of synthetic 2D imaging generated from 3D tomosynthesis (DBT) with traditional 2D full-field digital mammography (FFDM) by use of the most up-to-date software algorithm in an urban academic radiology practice. MATERIALS AND METHODS. The records of patients undergoing screening mammography with DBT, synthetic 2D imaging, and FFDM between August 13, 2014, and January 31, 2016, were retrospectively collected. The cohort included all biopsy-proven breast cancers detected with screening mammography during the study period (n = 89) and 100 cases of negative or benign (BI-RADS category 1 or 2) findings after 365 days of follow-up. In separate sessions, three readers blinded to outcome reviewed DBT plus synthetic 2D or DBT plus FFDM screening mammograms and assigned a BI-RADS category and probability of malignancy to each case. The diagnostic performance of each modality was assessed by calculating sensitivity and specificity. Reader performance was assessed by ROC analysis to estimate the AUC of the likelihood of malignancy. RESULTS. No statistically significant difference was found in diagnostic accuracy (sensitivity, specificity, positive predictive value, or negative predictive value) between DBT plus synthetic 2D mammography and DBT plus FFDM. There was no statistically significant difference between the AUC of DBT plus synthetic 2D mammography and the AUC of DBT plus FFDM for any reader. CONCLUSION. DBT plus synthetic 2D mammography performs as well as and not worse than DBT plus FFDM in measures of diagnostic accuracy and may be a viable alternative for decreasing radiation dose without sacrificing diagnostic performance.

Diagnostic Performance of Digital Breast Tomosynthesis for Breast Suspicious Calcifications From Various Populations: A Comparison With Full-field Digital Mammography

The diagnostic performance difference between digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) for breast suspicious calcifications from various populations is unclear. The objective of this study is to determine whether DBT exhibits the diagnostic advantage for breast suspicious calcifications from various populations compared with FFDM. Three hundred and five patients were enrolled (of which seven patients with bilateral lesions) and 312 breasts images were retrospectively analyzed by three radiologists independently. The postoperative pathology of breast calcifications was the gold standard. Breast cancer was diagnosed utilizing DBT and FFDM with sensitivities of 92.9% and 88.8%, specificities of 87.9% and 75.2%, positive predictive values of 77.8% and 62.1%, negative predictive values of 96.4% and 93.6%, respectively. DBT exhibited significantly higher diagnostic accuracy for benign calcifications compared with FFDM (87.9% vs 75.2%), and no advantage in the diagnosis of malignant calcifications. DBT diagnostic accuracy was notably higher than FFDM in premenopausal (88.4% vs 78.8%), postmenopausal (90.2% vs 77.2%), and dense breast cases (89.4% vs 81.9%). There was no significant difference in non-dense breast cases. In our study, DBT exhibited a superior advantage in dense breasts and benign calcifications cases compared to FFDM, while no advantage was observed in non-dense breasts or malignant calcifications cases. Thus, in the breast cancer screening for young women with dense breasts, DBT may be recommended for accurate diagnosis. Our findings may assist the clinicians in applying the optimal techniques for different patients and provide a theoretical basis for the update of breast cancer screening guideline.

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

Calibrated Breast Density Measurements

Mammographic density is an important risk factor for breast cancer, but translation to the clinic requires assurance that prior work based on mammography is applicable to current technologies. The purpose of this work is to evaluate whether a calibration methodology developed previously produces breast density metrics predictive of breast cancer risk when applied to a case-control study. A matched case control study (n = 319 pairs) was used to evaluate two calibrated measures of breast density. Two-dimensional mammograms were acquired from six Hologic mammography units: three conventional Selenia two-dimensional full-field digital mammography systems and three Dimensions digital breast tomosynthesis systems. We evaluated the capability of two calibrated breast density measures to quantify breast cancer risk: the mean (PGm) and standard deviation (PGsd) of the calibrated pixels. Matching variables included age, hormone replacement therapy usage/duration, screening history, and mammography unit. Calibrated measures were compared to the percentage of breast density (PD) determined with the operator-assisted Cumulus method. Conditional logistic regression was used to generate odds ratios (ORs) from continuous and quartile (Q) models with 95% confidence intervals. The area under the receiver operating characteristic curve (Az) was also used as a comparison metric. Both univariate models and models adjusted for body mass index and ethnicity were evaluated. In adjusted models, both PGsd and PD were statistically significantly associated with breast cancer with similar Az of 0.61-0.62. The corresponding ORs and confidence intervals were also similar. For PGsd, the OR was 1.34 (1.09, 1.66) for the continuous measure and 1.83 (1.11, 3.02), 2.19 (1.28, 3.73), and 2.20 (1.26, 3.85) for Q2-Q4. For PD, the OR was 1.43 (1.16, 1.76) for the continuous measure and 0.84 (0.52, 1.38), 1.96 (1.19, 3.23), and 2.27 (1.29, 4.00) for Q2-Q4. The results for PGm were slightly attenuated and not statistically significant. The OR was 1.22 (0.99, 1.51) with Az = 0.60 for the continuous measure and 1.24 (0.78, 1.97), 0.98 (0.60, 1.61), and 1.26, (0.77, 2.07) for Q2-Q4 with Az = 0.60. The calibrated PGsd measure provided significant associations with breast cancer comparable to those given by PD. The calibrated PGm performed slightly worse. These findings indicate that the calibration approach developed previously replicates under more general conditions.

Diagnostic Role of Digital Breast Tomosynthesis

Since its introduction in 2011, digital breast tomosynthesis has been adopted rapidly worldwide for screening mammography. Digital breast tomosynthesis is a new technology that acquires a series of low-dose images of the compressed breast from different angles while the x-ray tube moves along an overhead arc.1 This projection image dataset is then used to reconstruct 1-mm thin-section images in standard mammographic views. Displaying the breast tissue in 1-mm-thin slices results in decreased tissue overlap, improved lesion visibility, better delineation of lesion margins, and more accurate characterization of lesion architecture.1 Numerous studies have established that digital breast tomosynthesis increases the sensitivity and specificity of screening mammography. With the addition of digital breast tomosynthesis to conventional digital mammography, Skaane et al2 documented a 27% increase in the detection rate for in situ and invasive cancers combined, and a 40% increase in the detection rate for invasive cancers. Haas et al3 reported a significant reduction in screening recall rates from 12.0% to 8.4% with combined digital breast tomosynthesis and digital mammography, especially in women younger than age 50 years and those with dense breasts.