Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.

The evaluation of mammographic breast density, a critical indicator of breast cancer risk, is traditionally performed by radiologists via visual inspection of mammography images, utilizing the Breast Imaging-Reporting and Data System (BI-RADS) breast density categories. However, this method is subject to substantial interobserver variability, leading to inconsistencies and potential inaccuracies in density assessment and subsequent risk estimations. To address this, we present a deep learning-based automatic detection algorithm (DLAD) designed for the automated evaluation of breast density. Our multicentric, multi-reader study leverages a diverse dataset of 122 full-field digital mammography studies (488 images in CC and MLO projections) sourced from three institutions. We invited two experienced radiologists to conduct a retrospective analysis, establishing a ground truth for 72 mammography studies (BI-RADS class A: 18, BI-RADS class B: 43, BI-RADS class C: 7, BI-RADS class D: 4). The efficacy of the DLAD was then compared to the performance of five independent radiologists with varying levels of experience. The DLAD showed robust performance, achieving an accuracy of 0.819 (95% CI: 0.736-0.903), along with an F1 score of 0.798 (0.594-0.905), precision of 0.806 (0.596-0.896), recall of 0.830 (0.650-0.946), and a Cohen's Kappa (κ) of 0.708 (0.562-0.841). The algorithm achieved robust performance that matches and in four cases exceeds that of individual radiologists. The statistical analysis did not reveal a significant difference in accuracy between DLAD and the radiologists, underscoring the model's competitive diagnostic alignment with professional radiologist assessments. These results demonstrate that the deep learning-based automatic detection algorithm can enhance the accuracy and consistency of breast density assessments, offering a reliable tool for improving breast cancer screening outcomes.

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra's estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

Digital Breast Tomosynthesis With Synthetic Mammography: Are We Missing Calcifications That Matter?

OBJECTIVE: To determine whether there are differences in the biopsy outcomes for suspicious calcifications detected with screening mammography using the digital breast tomosynthesis and synthetic 2D (DBT/SM) technique compared to calcifications detected using the full-field digital (DM) technique. METHODS: This retrospective study was IRB approved. The records for all stereotactic biopsies performed for suspicious calcifications detected on screening mammograms using DM in 2011-2014 and DBT/SM in 2017-2020 were reviewed. We collected patient, imaging, and pathology data from the breast imaging database and from retrospective review of a subset of mammograms. The biopsy outcome results were categorized as benign, benign with upgrade potential (BWUP), and malignant based on final pathology. Frequencies and proportions of outcomes were calculated and compared using Mann-Whitney U tests and Wilcoxson signed-rank tests with P-values and 95% confidence intervals (95% CIs). RESULTS: From 2011 to 2014 (DM), 1274 stereotactic biopsies of calcifications yielded 74.2% (945/1274) benign, 11.5% (147/1274) BWUP, and 14.3% (182/1274) malignant outcomes. From 2017 to 2020 (DBT/SM), 1049 stereotactic biopsies yielded 65.2% (684/1049) benign, 15.6% (164/1049) BWUP, and 19.2% (201/1049) malignant outcomes. With DBT/SM, benign biopsy outcomes decreased (9.0%, 95% CI 0.87-11.53, P < 0.05), whereas malignant biopsy outcomes increased (4.9%, 95% CI 0.94-8.36, P < 0.05). There was no significant difference in BWUP biopsy outcomes and total biopsy rates between techniques (P > 0.05). CONCLUSION: Calcifications detected with screening DBT/SM technique were significantly more likely to be malignant than those found using DM. These results support using the DBT/SM technique without obtaining concurrent DM images.

Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.

Introduction: To date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms. Methods: To this end, a total of six binary classifiers were trained for comparison. The first three classifiers were trained using images only from Emory Breast Imaging Dataset (EMBED) using ResNet50, ResNet101, and ResNet152 architectures. The next three classifiers were trained using images from EMBED, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), and Digital Database for Screening Mammography (DDSM) datasets. All six models were tested only on digital mammograms from EMBED. Results: The results showed that performance degradation to the customized ResNet models was statistically significant overall when EMBED dataset was augmented with CBIS-DDSM/DDSM. While the performance degradation was observed in all racial subgroups, some races are subject to more severe performance drop as compared to other races. Discussion: The degradation may potentially be due to ( 1) a mismatch in features between film-based and digital mammograms ( 2) a mismatch in pathologic and radiological information. In conclusion, use of both film and digital mammography during training may hinder modern models designed for breast cancer screening. Caution is required when combining film-based and digital mammograms or when utilizing pathologic and radiological information simultaneously.

Impact of the systematic introduction of tomosynthesis on breast biopsies: 10 years of results.

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs.

Identification and diagnosis of mammographic malignant architectural distortion using a deep learning based mask regional convolutional neural network.

Background: Architectural distortion (AD) is a common imaging manifestation of breast cancer, but is also seen in benign lesions. This study aimed to construct deep learning models using mask regional convolutional neural network (Mask-RCNN) for AD identification in full-field digital mammography (FFDM) and evaluate the performance of models for malignant AD diagnosis. Methods: This retrospective diagnostic study was conducted at the Second Affiliated Hospital of Guangzhou University of Chinese Medicine between January 2011 and December 2020. Patients with AD in the breast in FFDM were included. Machine learning models for AD identification were developed using the Mask RCNN method. Receiver operating characteristics (ROC) curves, their areas under the curve (AUCs), and recall/sensitivity were used to evaluate the models. Models with the highest AUCs were selected for malignant AD diagnosis. Results: A total of 349 AD patients (190 with malignant AD) were enrolled. EfficientNetV2, EfficientNetV1, ResNext, and ResNet were developed for AD identification, with AUCs of 0.89, 0.87, 0.81 and 0.79. The AUC of EfficientNetV2 was significantly higher than EfficientNetV1 (0.89 vs. 0.78, P=0.001) for malignant AD diagnosis, and the recall/sensitivity of the EfficientNetV2 model was 0.93. Conclusion: The Mask-RCNN-based EfficientNetV2 model has a good diagnostic value for malignant AD.

Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach.

Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs. The method was trained and validated on 435 screening and diagnostic FFDMs from two separate datasets. We then used our approach to segment MCs on magnification views of 248 cases with amorphous MCs. We modeled the extracted features using gradient tree boosting to classify each case as benign or malignant. Compared to state-of-the-art comparison methods, our approach achieved superior mean intersection over the union (0.670 ± 0.121 per image versus 0.524 ± 0.034 per image), intersection over the union per MC object (0.607 ± 0.250 versus 0.363 ± 0.278) and true positive rate of 0.744 versus 0.581 at 0.4 false positive detections per square centimeter. Features generated using our approach outperformed the comparison method (0.763 versus 0.710 AUC) in distinguishing amorphous calcifications as benign or malignant.

Performance of Wide-Angle Tomosynthesis with Synthetic Mammography in Comparison to Full Field Digital Mammography.

RATIONALE AND OBJECTIVES: The purpose of this study was to test for superiority of wide-angle digital breast tomosynthesis plus synthetic mammography (Insight 2D) in comparison to full-field digital mammography (FFDM). MATERIALS AND METHODS: In this study, twenty readers interpreted 350 screening and diagnostic cases of wide-angle digital breast tomosynthesis (DBT) plus Insight 2D and FFDM in two separate reading sessions separated by at least a 6-week washout period. Breast-level estimates of the area under the curve and sensitivity along with subject-level recall rate were measured and compared between wide-angle DBT plus Insight 2D and FFDM. The same measures were also assessed for dense breasts. A hierarchical analysis plan was used to control the study's type I error rate at 0.05. RESULTS: The mean breast-level area under the curve for distinguishing breasts with cancer from non-cancer breasts was 0.893 with DBT plus Insight 2D versus 0.837 with FFDM, showing superiority of DBT plus Insight 2D (p < 0.001). Breast-level sensitivity was significantly superior for DBT plus Insight 2D in comparison to FFDM (0.852 vs. 0.805, p = 0.043). Subject-level recall rate for DBT plus Insight 2D was significantly lower in comparison to FFDM (0.344 vs. 0.473, p < 0.001). For dense breasts, the readers' accuracy with DBT plus Insight 2D was superior to their accuracy with FFDM (0.875 vs. 0.830, p = 0.026), and their recall rate was significantly lower for DBT plus Insight 2D in comparison to FFDM (0.338 vs. 0.441, p = 0.003). CONCLUSION: Reader performance with wide-angle DBT plus Insight 2D is superior to that with FFDM, showing significantly higher breast-level accuracy and sensitivity and significantly lower recall rates.

Dose comparison of mammography with two photographic modes / 中华放射医学与防护杂志

Objective:To compare radiation dose between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM), and explore the correlation of average glandular dose(AGD) with breast density and compression thickness.Methods:The mammographic data of patients with breast diseases who underwent digital breast tomosynthesis (DBT) and the population who underwent full-field digital mammography (FFDM) screening in the First Affiliated Hospital of Kunming Medical University from October 2020 to May 2022 were retrospectively collected. The compression thickness, compression force and AGD were recorded. According to the 2013 ACR BI-RADS MAMMOGRAPHY, the breast gland density was classified into 4 types: a(glandular tissue<25%), b(glandular tissue 25%~50%), c(glandular tissue 50%~75%) and d(glandular tissue >75%), by two senior doctors engaged in breast imaging diagnosis. The relationships between different gland densities, different compression thicknesses and AGD under FFDM and DBT mode were analyzed.Results:In both FFDM and DBT modes, the AGD increased significantly with the increase of breast density( F=861.63, 617.83, 330.33, 451.45, 290.47, P<0.001), and AGD a<AGD b<AGD c<AGD d. For type c and d breasts undergoing FFDM, AGD was lowest when the compression thickness was 31~40 mm. Under the same compression thickness, The AGD DBT was significantly higher than the AGD FFDMin all types (Type a: t=-17.88, -42.19, -29.90, -28.14, -24.95, P<0.001; Type b: t=-49.18, -35.94, -27.25, -28.37, -24.10, P<0.001; Type c: t=-11.78, -32.90, -23.13, -20.51, -18.24, P<0.001; Type d: t=-7.94, -26.24, -17.24, -15.44, -13.81, P<0.001). The difference between two AGDs of Type d with compression thickness of 61~70 mm was the largest, which was 1.07 mGy (95% CI: 0.92~1.22). The AGD was positively correlated with breast density and compression thickness, and the relationship of FFDM was stronger than that of DBT. Conclusions:The AGD is positively correlated with breast density and compression thickness in mammography. Compared with FFDM, DBT can increase AGD, The AGD would increase in DBT than FFDM but be safe. DBT would be beneficial to patients with breast diseases in clinical practice.

Discrepancy between Tumor Size Assessed by Full-Field Digital Mammography or Ultrasonography (cT) and Pathology (pT) in a Multicenter Series of Breast Metaplastic Carcinoma Patients.

Metaplastic breast cancer (BC-Mp) presents diagnostic and therapeutic complexities, with scant literature available. Correct assessment of tumor size by ultrasound (US) and full-field digital mammography (FFDM) is crucial for treatment planning. METHODS: A retrospective cohort study was conducted on databases encompassing records of BC patients (2012-2022) at the National Research Institutes of Oncology (Warsaw, Gliwice and Krakow Branches). Inclusion criteria comprised confirmed diagnosis in postsurgical pathology reports with tumor size details (pT) and availability of tumor size from preoperative US and/or FFDM. Patients subjected to neoadjuvant systemic treatment were excluded. Demographics and clinicopathological data were gathered. RESULTS: Forty-five females were included. A total of 86.7% were triple-negative. The median age was 66 years (range: 33-89). The median pT was 41.63 mm (6-130), and eight patients were N-positive. Median tumor size assessed by US and FFDM was 31.81 mm (9-100) and 34.14 mm (0-120), respectively. Neither technique demonstrated superiority (p > 0.05), but they both underestimated the tumor size (p = 0.002 for US and p = 0.018 for FFDM). Smaller tumors (pT1-2) were statistically more accurately assessed by any technique (p < 0.001). Only pT correlated with overall survival. CONCLUSION: The risk of underestimation in tumor size assessment with US and FFDM has to be taken into consideration while planning surgical procedures for BC-Mp.

Local Diagnostic Reference Levels for Full-Field Digital Mammography and Digital Breast Tomosynthesis in a Tertiary Hospital in Malaysia.

A set of national diagnostic reference levels (DRLs) was established in Malaysia for a range of breast thicknesses in 2013, but no updates for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Due to the increasing number of DBTs used and concern over radiation exposure, this study aimed to explore and establish local diagnostic reference levels for FFDM and DBT in Malaysia health facilities at different compressed breast thickness (CBT) ranges. The CBT, kilovoltage peak (kVp), Entrance surface dose (ESD), and average glandular dose (AGD) were retrospectively extracted from the mammography Digital Imaging and Communications in Medicine (DICOM) header. The 75th and 95th percentile values were obtained for the AGD distribution of each mammography projection for three sets of CBT range. The difference in AGD values between FFDM and DBT at three CBT ranges was determined. The DRLs for FFDM were 1.13 mGy, 1.52 mGy, and 2.87 mGy, while DBT were 1.18 mGy, 1.88 mGy, and 2.78 mGy at CBT ranges of 20−39 mm, 40−59 mm, and 60−99 mm, respectively. The AGD of DBT was significantly higher than FFDM for both mammographic views (p < 0.005). All three CBT groups showed a significant difference in AGD values for FFDM and DBT (p < 0.005). The local DRLs from this study were lower than the national DRLs, with the AGD of FFDM significantly lower than DBT.

Digital Breast Tomosynthesis versus MRI as an Adjunct to Full-Field Digital Mammography for Preoperative Evaluation of Breast Cancer according to Mammographic Density.

OBJECTIVE: To compare digital breast tomosynthesis (DBT) and MRI as an adjunct to full-field digital mammography (FFDM) for the preoperative evaluation of women with breast cancer based on mammographic density. MATERIALS AND METHODS: This retrospective study enrolled 280 patients with breast cancer who had undergone FFDM, DBT, and MRI for preoperative local tumor staging. Three radiologists independently sought the index cancer and additional ipsilateral and contralateral breast cancers using either FFDM alone, DBT plus FFDM, or MRI plus FFDM. Diagnostic performances across the three radiologists were compared among the reading modes in all patients and subgroups with dense (n = 186) and non-dense breasts (n = 94) according to mammographic density. RESULTS: Of 280 patients, 46 (16.4%) had 48 additional (39 ipsilateral and nine contralateral) cancers in addition to the index cancer. For index cancers, both DBT plus FFDM and MRI plus FFDM showed sensitivities of 100% in the non-dense group. In the dense group, DBT plus FFDM showed lower sensitivity than that of MRI plus FFDM (94.6% vs. 99.6%, p < 0.001). For additional ipsilateral cancers, DBT plus FFDM showed specificity and positive predictive value (PPV) of 100% in the non-dense group, but sensitivity and negative predictive value (NPV) were not statistically different from those of MRI plus FFDM (p > 0.05). In the dense group, DBT plus FFDM showed higher specificity (98.2% vs. 94.1%, p = 0.005) and PPV (83.1% vs. 65.4%; p = 0.036) than those of MRI plus FFDM, but lower sensitivity (59.9% vs. 75.3%; p = 0.049). For contralateral cancers, DBT plus FFDM showed higher specificity than that of MRI plus FFDM (99.0% vs. 96.7%, p = 0.014), however, the other values did not differ (all p > 0.05) in the dense group. CONCLUSION: DBT plus FFDM showed an overall higher specificity than that of MRI plus FFDM regardless of breast density, perhaps without substantial loss in sensitivity and NPV in the diagnosis of additional cancers. Thus, DBT may have the potential to be used as a preoperative breast cancer staging tool.

Regression Analysis between the Different Breast Dose Quantities Reported in Digital Mammography and Patient Age, Breast Thickness, and Acquisition Parameters.

Breast cancer is the leading cause of cancer death among women worldwide. Screening mammography is considered the primary imaging modality for the early detection of breast cancer. The radiation dose from mammography increases the patients' risk of radiation-induced cancer. The mean glandular dose (MGD), or the average glandular dose (AGD), provides an estimate of the absorbed dose of radiation by the glandular tissues of a breast. In this paper, MGD is estimated for the craniocaudal (CC) and mediolateral-oblique (MLO) views using entrance skin dose (ESD), X-ray spectrum information, patient age, breast glandularity, and breast thickness. Moreover, a regression analysis is performed to evaluate the impact of mammography acquisition parameters, age, and breast thickness on the estimated MGD and other machine-produced dose quantities, namely, ESD and organ dose (OD). Furthermore, a correlation study is conducted to evaluate the correlation between the ESD and OD, and the estimated MGD per image view. This retrospective study was applied to a dataset of 2035 mammograms corresponding to a cohort of 486 subjects with an age range of 28-86 years who underwent screening mammography examinations. Linear regression metrics were calculated to evaluate the strength of the correlations. The mean (and range) MGD for the CC view was 0.832 (0.110-3.491) mGy and for the MLO view was 0.995 (0.256-2.949) mGy. All the mammography dose quantities strongly correlated with tube exposure (mAs): ESD (R2 = 0.938 for the CC view and R2 = 0.945 for the MLO view), OD (R2 = 0.969 for the CC view and R2 = 0.983 for the MLO view), and MGD (R2 = 0.980 for the CC view and R2 = 0.972 for the MLO view). Breast thickness showed a better correlation with all the mammography dose quantities than patient age, which showed a poor correlation. Moreover, a strong correlation was found between the calculated MGD and both the ESD (R2 = 0.929 for the CC view and R2 = 0.914 for the MLO view) and OD (R2 = 0.971 for the CC view and R2 = 0.972 for the MLO view). Furthermore, it was found that the MLO scan views yield a slightly higher dose compared to CC scan views. It was also found that the glandular absorbed dose is more dependent on glandularity than size. Despite being more reflective of the dose absorbed by the glandular tissue than OD and ESD, MGD is considered labor-intensive and time-consuming to estimate.

Comparison between two packages for pectoral muscle removal on mammographic images.

BACKGROUND: Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. PURPOSE: To compare performance of the two packages on a single database of FFDM images. METHODS: Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA's and OpenBreast's removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal-Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. RESULTS: FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA's Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P < 0.05). No dependence on breast density or laterality has been found (Kruskal-Wallis test P > 0.05). CONCLUSION: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation.

Associating Automated Breast Ultrasound (ABUS) and Digital Breast Tomosynthesis (DBT) with Full-Field Digital Mammography (FFDM) in Clinical Practice in Cases of Women with Dense Breast Tissue.

The purpose of the present study was to evaluate the value of full-field digital mammography (FFDM) and automated breast ultrasound (ABUS) in the diagnosis of breast cancer compared to FFDM associated with digital breast tomosynthesis (DBT). Methods: This retrospective study included 50 female patients with a denser framework of connective tissue fibers, characteristic of young women who underwent FFDM, DBT, handheld ultrasound (HHUS), and ABUS between January 2017 and October 2018. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and accuracy of FFDM+ABUS were 81.82% (95% CI [48.22-97.72]), 89.74% (95% CI [75.78-97.13]), 69.23% (95% CI [46.05-85.57]), 94.59% (95% CI [83.26-98.40]), and 88% (95% CI [75.69-95.47]), while for FFDM+DBT, the values were as follows: 91.67% (95% CI [61.52-99.79]), 71.79% (95% CI [55.13-85.00]), 50% (95% CI [37.08-62.92]), 96.55% (95% CI [80.93-99.46]), 76.47% (95% CI [62.51-87.21]). We found an almost perfect agreement between the two readers regarding FFDM associated with ABUS, and substantial agreement regarding FFDM+DBT, with a kappa coefficient of 0.896 and 0.8, respectively; p < 0.001. Conclusions: ABUS and DBT are suitable as additional diagnostic imaging techniques to FFDM in women at an intermediate risk of developing breast cancer through the presence of dense breast tissue. In this study, DBT reduced the number of false negative results, while the use of ABUS resulted in an increase in specificity.

Comparison of diagnostic performances in the evaluation of breast microcalcifications: synthetic mammography versus full-field digital mammography.

BACKGROUND: Although several studies proved that SM could substitute for FFDM, the efficacy of SM in microcalcification evaluation remains controversial. AIMS: To investigate the diagnostic performance of synthetic mammography (SM) in the evaluation of microcalcifications in comparison with full-field digital mammography (FFDM). METHODS: In this retrospective study, 76 mammograms of 76 patients who underwent FFDM and digital breast tomosynthesis (DBT) acquisitions concomitantly between 2018 and 2019 and whose final mammography interpretation revealed microcalcifications (28 malignant microcalcifications and 48 benign microcalcifications) were included. All mammograms were reviewed independently by three radiologists with different levels of breast imaging experience. Readers were blinded to patient outcomes and interpreted each case in two separate reading sessions (first FFDM, second SM + DBT), according to the BI-RADS lexicon. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated using ROC analysis in all cases for FFDM and SM + DBT sessions. The readers also assigned conspicuity scores to mammograms. The interobserver agreement was calculated using intraclass correlation coefficients (ICC). RESULTS: The overall AUCs for malignant microcalcifications were 0.80 (95% CI: 0.75-0.85) in FFDM and 0.85 (95% CI: 0.80-0.89) in SM, and no significant difference was found between the groups (p = 0.0603). The sensitivity of the readers increased slightly with experience. The ICC values of BI-RADS categorization between readers were 0.93 (95% CI: 0.90-0.95) and 0.94 (95% CI: 0.91-0.96) for FFDM and SM, respectively. CONCLUSIONS: SM had similar diagnostic performance in the evaluation of breast microcalcifications in comparison with FFDM, regardless of reader experience levels.

Digital Breast Tomosynthesis Complements Two-Dimensional Synthetic Mammography for Secondary Examination of Breast Cancer.

OBJECTIVE: To compare the performance of two-dimensional synthetic mammography (SM) combined with digital breast tomosynthesis (DBT) (SM/DBT) and full-field digital mammography (FFDM) including women with DBT (FFDM/DBT) undergoing secondary examination for breast cancer. MATERIAL AND METHODS: Out of 186 breasts, including 52 with breast cancers; FFDM/DBT and SM/DBT findings were interpreted by four expert clinicians. Radiation doses of FFDM, SM/DBT, and FFDM/DBT were determined. Inter-rater reliabilities were analyzed between readers and between FFDM/DBT and SM/DBT by Cohen's Kappa coefficients. Diagnostic accuracy was compared between SM/DBT and FFDM/DBT by Fisher's exact tests. Two representative cancer cases were examined for differences in the interpretation between FFDM and SM. RESULTS: A higher radiation dose was required in FFDM/DBT than in SM/DBT (median: 1.50 mGy vs. 2.95 mGy). Inter-rater reliabilities were similar between both readers and modalities. Both sensitivity and specificity were equivalent in FFDM/DBT and SM/DBT (p = 0.874-1.00). Compared with FFDM, SM did not clearly show abnormalities with subtle margins in the two representative cancer cases. CONCLUSION: SM/DBT had a similar performance to FFDM/DBT in detecting breast abnormalities but requires less radiation. DBT complements SM to improve accuracy to a level equivalent to that of FFDM. Taken together, SM/DBT may be a good substitute for FFDM/DBT for the secondary examination of breast cancer.

Evaluation of Breast Galactography Using Digital Breast Tomosynthesis: A Clinical Exploratory Study.

OBJECTIVES: To compare the application value of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in breast galactography. MATERIALS AND METHODS: A total of 128 patients with pathological nipple discharge (PND) were selected to undergo galactography. DBT and FFDM were performed for each patient after injecting the contrast agent; the radiation dose of DBT and FFDM was calculated, and the image quality was evaluated in consensus by two senior breast radiologists. Histopathologic data were found in 49 of the 128 patients. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for both FFDM- and DBT-galactography were calculated using histopathologic results as a reference standard. Data were presented as percentages along with their 95% confidence intervals (CI). RESULTS: The average age of the 128 patients was 46.53 years. The average glandular dose (AGD) of DBT-galactography was slightly higher than that of FFDM-galactography (p < 0.001). DBT-galactography was 30.7% higher than FFDM-galactography in CC view, while DBT-galactography increased by 21.7% compared with FFDM-galactography in ML view. Regarding catheter anatomic distortion, structure detail, and overall image quality groups, DBT scores were higher than FFDM scores, and the differences were significant for all measures (p < 0.05). In 49 patients with pathological nipple discharge, we found that the DBT-galactography had higher sensitivity, specificity, PPV, and NPV (93.3%, 75%, 97.7%, and 50%, respectively) than FFDM-galactography (91.1%, 50%, 95.3%, and 33.3%, respectively). CONCLUSIONS: Compared to FFDM-galactography, within the acceptable radiation dose range, DBT-galactography increases the sensitivity and specificity of lesion detection by improving the image quality, providing more confidence for the diagnosis of clinical ductal lesions.

DETECTION OF BREAST CANCERS REPRESENTED AS ARCHITECTURAL DISTORTION: A COMPARISON OF FULL-FIELD DIGITAL MAMMOGRAPHY AND DIGITAL BREAST TOMOSYNTHESIS.

OBJECTIVE: The aim: The aim of our study was to determine if digital breast tomosynthesis improves breast cancer detection associated with architectural distortion in comparison with full-field digital mammography in the absence of appropriate history of trauma or surgery. PATIENTS AND METHODS: Materials and methods: The overall rate of breast cancer involvement for the 34 patients with architectural distortion was 15 cases (44,1%) (invasive breast cancers, n=12 (36,4%); ductal cancer in situ, n= 3 (8,8%)) other findings associated with architectural distortion were high-risk lesions and benign findings (radial scar, n=5 (14,7%); sclerosing adenosis, n=9 (26,5%); typical lobular hyperplasia, n=3 (8,8%); typical ductal hyperplasia, n=2 cases (5,9%)). RESULTS: Results: Overall of 17/34 (50.0%) architectural distortions were identified at digital breast tomosynthesis that were missed at full-field digital mammography what was statistically significant difference ([95% CI, 2.56-7.45]; p=0.00001). Analysis of the results showed that sensitivity of full-field digital mammography for digital breast tomosynthesis detected breast cancers associated with architectural distortion was 53.3% [95% CI, 26.59% to 78.73%] and specificity was 52.63% [95% CI, 28.86% to 75.55%]. CONCLUSION: Conclusions: Our study suggests that digital breast tomosynthesis detects more breast cancers represented as architectural distortion which are occult on full-field digital mammography. Presence of microcalcifications within architectural distortion, in the absence of appropriate history of trauma or surgery, has a high likelihood of malignancy and obligatorily requires biopsy.