Inter- and intra-observer variability of qualitative visual breast-composition assessment in mammography among Japanese physicians: a first multi-institutional observer performance study in Japan.

BACKGROUND: Visual assessment of mammographic breast composition remains the most common worldwide, although subjective variability limits its reproducibility. This study aimed to investigate the inter- and intra-observer variability in qualitative visual assessment of mammographic breast composition through a multi-institutional observer performance study for the first time in Japan. METHODS: This study enrolled 10 Japanese physicians from five different institutions. They used the new Japanese breast-composition classification system 4th edition to subjectively evaluate the breast composition in 200 pairs of right and left normal mediolateral oblique mammograms (number determined using precise sample size calculations) twice, with a 1-month interval (median patient age: 59 years [range 40-69 years]). The primary endpoint of this study was the inter-observer variability using kappa (κ) value. RESULTS: Inter-observer variability for the four and two classes of breast-composition assessment revealed moderate agreement (Fleiss' κ: first and second reading = 0.553 and 0.587, respectively) and substantial agreement (Fleiss' κ: first and second reading = 0.689 and 0.70, respectively). Intra-observer variability for the four and two classes of breast-composition assessment demonstrated substantial agreement (Cohen's κ, median = 0.758) and almost perfect agreement (Cohen's κ, median = 0.813). Assessments of consensus between the 10 physicians and the automated software Volpara® revealed slight agreement (Cohen's κ; first and second reading: 0.104 and 0.075, respectively). CONCLUSIONS: Qualitative visual assessment of mammographic breast composition using the new Japanese classification revealed excellent intra-observer reproducibility. However, persistent inter-observer variability, presenting a challenge in establishing it as the gold standard in Japan.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.

BACKGROUND: Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression. METHODS: The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5 years, range: 29-90 years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer. RESULTS: The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively. CONCLUSIONS: Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Beyond BI-RADS: Nonmass Abnormalities on Breast Ultrasound.

Abnormalities on breast ultrasound (US) images which do not meet the criteria for masses are referred to as nonmass lesions. These features and outcomes have been investigated in several studies conducted by Asian researchers. However, the term "nonmass" is not included in the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) 5th edition for US. According to the Japan Association of Breast and Thyroid Sonology guidelines, breast lesions are divided into mass and nonmass. US findings of nonmass abnormalities are classified into five subtypes: abnormalities of the ducts, hypoechoic areas in the mammary glands, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. These findings can be benign or malignant; however, focal or segmental distributions and presence of calcifications suggest malignancy. Intraductal, invasive ductal, and lobular carcinomas can present as nonmass abnormalities. For the nonmass concept to be included in the next BI-RADS and be widely accepted in clinical practice, standardized terminologies, an interpretation algorithm, and outcome-based evidence are required for both screening and diagnostic US.

Nonmass Lesions on Breast US: An International Perspective on Clinical Use and Outcomes.

Nonmass lesions (NMLs) on breast US are defined as discrete areas of altered echotexture compared to surrounding breast tissue and lack the 3-dimensionality of a mass. They are not a component of American College of Radiology BI-RADS, but they are a finding type included in the Japan Association of Breast and Thyroid Sonology lexicon. Use of the NML finding is routine in many Asian practices, including the Samsung Medical Center and Seoul National University Hospital, and their features and outcomes have been investigated in multiple studies. Nonmass lesions are most often observed when US is used to evaluate mammographic asymmetries, suspicious calcifications, and nonmass enhancement on MRI and contrast-enhanced mammography. Nonmass lesions can be described by their echogenicity, distribution, presence or absence of associated calcifications, abnormal duct changes, architectural distortion, posterior shadowing, small cysts, and hypervascularity. Malignant lesions, especially ductal carcinoma in situ, can manifest as NMLs on US. There is considerable overlap between the US features of benign and malignant NMLs, and they also must be distinguished from normal variants. The literature indicates that NMLs with linear or segmental distribution, associated calcifications, abnormal duct changes, posterior shadowing, and hypervascularity are suggestive of malignancy, whereas NMLs with only interspersed small cysts are usually benign fibrocystic changes. In this article, we introduce the concepts of NMLs, illustrate US features suggestive of benign and malignant etiologies, and discuss our institutional approach for evaluating NMLs and an algorithm that we use to guide interpretation in clinical practice.

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer (n = 20) and those with invasive cancer (n = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49-0.62], 0.67 (95% CI 0.62-0.74), 0.71 (95% CI 0.65-0.75), and 0.75 (95% CI 0.69-0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.

The Japan Society of Ultrasonics in Medicine guidelines on non-mass abnormalities of the breast.

It is possible to appropriately diagnose non-mass abnormalities by elucidating ultrasound non-mass abnormality findings and sharing the concept. If non-mass abnormalities can be diagnosed early, the number of curable cases could increase, leading to fewer breast cancer deaths. The Japan Society of Ultrasonics in Medicine (JSUM) Terminology/Diagnostic Criteria Committee has classified non-mass abnormalities into five subtypes: hypoechoic area in the mammary gland, abnormalities of the ducts, architectural distortion, multiple small cysts, and echogenic foci without a hypoechoic area. We herein define the findings for each of these subtypes and present a summary of the JSUM guidelines on non-mass abnormalities of the breast generated based on those findings.

Radiation-induced angiosarcoma of the breast: individual participant meta-analysis of Japanese population.

BACKGROUND: Radiation-induced angiosarcoma (RIAS) of the breast is a very rare and poor prognostic disease. According to previous studies, the efficacy of chemotherapy for RIAS is still controversial. However, no study has assessed the prognosis of RIAS and the prognostic impact of preoperative or postoperative chemotherapy in Japanese patients. Our study aimed to assess them in Japanese people using publication data with our three patients. METHODS: Thirty-nine patients diagnosed with RIAS, including 36 patients from 34 published case series, and three patients from our hospital were used for analysis. Disease-free survival (DFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed. RESULTS: Among the 39 patients, 36 patients (92.3%) underwent surgery. The median DFS and OS periods were 14 months (range 1-75 months) and 23 months (range 4-84 months), respectively. Chemotherapy with taxane-based regimen was administered in 13 cases (33.2%) pre- or post-operatively. DFS was significantly improved with chemotherapy in addition to surgery (p = 0.037). However, addition of chemotherapy to surgery did not improve DDFS (p = 0.09) and OS (p = 0.878). In multivariate analysis, age ≥ 70 years was an independent but poor prognostic factor of DFS. Additionally, a lack of chemotherapy showed a trend to be associated with worse DFS. There was no independent variable contributing to DDFS and OS. CONCLUSIONS: Chemotherapy may have reduced the recurrence rate of RIAS in Japanese patients but did not improve OS. Further data are needed to confirm the efficacy and proper regimen of chemotherapy.

Clinicopathological and ultrasound characteristics of breast cancer in BRCA1 and BRCA2 mutation carriers.

PURPOSE: BRCA1 and BRCA2 tumors exhibit different characteristics. This study aimed to assess and compare the ultrasound findings and pathologic features of BRCA1 and BRCA2 breast cancers. To our knowledge, this is the first study to examine the mass formation, vascularity, and elasticity in breast cancers of BRCA-positive Japanese women. METHODS: We identified patients with breast cancer harboring BRCA1 or BRCA2 mutations. After excluding patients who underwent chemotherapy or surgery before the ultrasound, we evaluated 89 cancers in BRCA1-positive and 83 in BRCA2-positive patients. The ultrasound images were reviewed by three radiologists in consensus. Imaging features, including vascularity and elasticity, were assessed. Pathological data, including tumor subtypes, were reviewed. RESULTS: Significant differences in tumor morphology, peripheral features, posterior echoes, echogenic foci, and vascularity were observed between BRCA1 and BRCA2 tumors. BRCA1 breast cancers tended to be posteriorly accentuating and hypervascular. In contrast, BRCA2 tumors were less likely to form masses. In cases where a tumor formed a mass, it tended to show posterior attenuation, indistinct margins, and echogenic foci. In pathological comparisons, BRCA1 cancers tended to be triple-negative subtypes. In contrast, BRCA2 cancers tended to be luminal or luminal-human epidermal growth factor receptor 2 subtypes. CONCLUSION: In the surveillance of BRCA mutation carriers, radiologists should be aware that the morphological differences between tumors are quite different between BRCA1 and BRCA2 patients.

Clinicopathological and imaging features of ductal carcinoma in situ in BRCA1/2 mutation carriers.

BACKGROUND: BRCA1/2-associated invasive breast cancer has been extensively studied. However, there are few reports of ductal carcinoma in situ (DCIS). OBJECTIVE: This study aimed to investigate the clinicopathological and imaging findings of DCIS in patients with BRCA1/2 mutations. METHODS: This was a single-institution, retrospective study. We identified patients diagnosed with DCIS with BRCA mutations between September 2003 and December 2020. Clinicopathological data and mammography (MG), magnetic resonance imaging (MRI), and ultrasound (US) findings were reviewed. RESULTS: We identified 30 cancers in 28 patients; 7 (25.0%) patients had BRCA1 mutations, and 21 (75.0%) had BRCA2 mutations. The median patient age was 42 years. Screening was the most common reason for the detection of DCIS (50.0%), followed by occult cancer diagnosed by pathological examination after risk-reducing mastectomy (26.7%). The nuclear grade was most often 1 (46.7%), and 93.3% were estrogen and/or progesterone receptor positive. The detection rates of MG, MRI, and US were 64.3%, 72.0%, and 64.0%, respectively. The most common imaging findings were calcification (100%) on MG, non-mass enhancement (88.9%) on MRI, and hypoechoic area (75.0%) on US. CONCLUSION: BRCA-associated DCIS was more strongly associated with BRCA2, and imaging features were similar to those of sporadic DCIS. Our results are helpful in informing surveillance strategies based on genotypes in women with BRCA mutations.

The location of unilateral axillary lymphadenopathy after COVID-19 vaccination compared with that of metastasis from breast cancer without vaccination.

PURPOSE: Unilateral axillary lymphadenopathy is known to occur after coronavirus disease (COVID-19) vaccination. Post-vaccination lymphadenopathy may mimic the metastatic lymph nodes in breast cancer, and it is challenging to distinguish between them. This study investigated whether the localization of axillary lymphadenopathy on magnetic resonance imaging (MRI) could be used to distinguish reactive lymphadenopathy after COVID-19 vaccines from metastatic nodes. MATERIALS AND METHODS: We retrospectively examined preoperative MRI images of 684 axillae in 342 patients who underwent breast cancer surgery from June to October 2021. Lymphadenopathy was defined as cortical thickening or short axis ≥ 5 mm. The axilla was divided into ventral and dorsal parts on the axial plane using a perpendicular line extending from the most anterior margin of the muscle group, including the deltoid, latissimus dorsi, or teres major muscles, relative to a line along the lateral chest wall. We recorded the presence or absence of axillary lymphadenopathy in each area and the number of visible lymph nodes. RESULTS: Of 80 axillae, 41 and 39 were included in the vaccine and metastasis groups, respectively. The median time from the last vaccination to MRI was 19 days in the vaccine group. The number of visible axillary lymph nodes was significantly higher in the vaccine group (median, 15 nodes) than in the metastasis group (7 nodes) (P < 0.001). Dorsal lymphadenopathy was observed in 16 (39.0%) and two (5.1%) axillae in the vaccine and metastasis groups, respectively (P < 0.001). If the presence of both ventral and dorsal lymphadenopathy is considered indicative of vaccine-induced reaction, this finding has a sensitivity of 34.1%, specificity of 97.4%, and positive and negative predictive values of 93.3％ and 58.5%, respectively. CONCLUSION: The presence of deep axillary lymphadenopathy may be an important factor for distinguishing post-vaccination lymphadenopathy from metastasis. The number of axillary lymph nodes may also help.

Deep Learning for Differentiation of Breast Masses Detected by Screening Ultrasound Elastography.

Recently, deep learning using convolutional neural networks (CNNs) has yielded consistent results in image-pattern recognition. This study was aimed at investigating the effectiveness of deep learning using CNNs to differentiate benign and malignant breast masses identified by elastography on ultrasound screening. A data set of the elastography images of 245 breast masses (146 benign, 99 malignant) in 239 consecutive patients was retrospectively obtained. The data set was randomly split into training (55%), validation (25%) and test (20%) cohorts. A deep learning model predicting the probability of malignancy was constructed using GoogLeNet architectures (pre-trained by ImageNet) with 50 epochs. The model was then applied to the test data, and the results were compared with those obtained by evaluating the fat-to-lesion ratio (FLR) and by a 5-point visual color assessment (elasticity score). The receiver operating characteristic (ROC) curve was calculated to evaluate the performance of the model. The DeLong test was used to compare the areas under the ROC curve (AUCs). The CNN, FLR and elasticity score had a sensitivity of 0.800, 0.800 and 0.350; specificity of 0.966, 0.586 and 0.931; accuracy of 0.898, 0.673 and 0.694; positive predictive value of 0.941, 0.571 and 0.778; negative predictive value of 0.875, 0.810 and 0.675; and AUC of 0.895, 0.693 and 0.641, respectively. The AUC of the CNN was significantly higher than that of the FLR or elasticity score (p < 0.001). A CNN-based deep learning model for predicting benign or malignant breast masses revealed better diagnostic performance than did FLR or elasticity score-based estimations on ultrasound elastography. The CNN-based model also increased the positive predictive value from 57%-78% to 94%. Therefore, this model may reduce unnecessary biopsy recommendations for masses detected on breast ultrasound screening.

Why is the depth/width ratio of a typical fibroadenoma small in breast ultrasonography?

PURPOSE: Typical myxomatous fibroadenomas have a small depth/width (D/W) ratio on ultrasonography. The small D/W ratio of fibroadenomas is speculated to be caused by the softness of the mass and its orientation along the longitudinal aspect of the ductal elements without adhesion to the surrounding tissue; however, this has not been clearly proven. This study aimed to confirm the reason why fibroadenomas present with a small D/W ratio on ultrasonography. METHODS: We retrospectively analyzed imaging data from 17 patients who were diagnosed with typical fibroadenomas on ultrasonography and who underwent magnetic resonance imaging (MRI) at our hospital. RESULTS: The median D/W ratio obtained from ultrasonography images was 0.48 (0.32-0.67), while that obtained from MRI was 1.38 (0.62-1.68). The D/W ratios calculated from MRI were significantly greater than those calculated from ultrasonography images (p < 0.001). The D/W ratio obtained using ultrasonography was not greater than the D/W ratio obtained using MRI in any of the cases. CONCLUSION: This study revealed that the small D/W ratio of fibroadenomas on ultrasonography may be attributable to the horizontal force acting on the breast against the chest wall in the supine position, the elasticity of the fibroadenoma, and the lack of adhesion between the mass and surrounding tissue.

Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images.

The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review board. A total of 115 patients who underwent breast surgery and had pathologically confirmed breast cancer were enrolled in this study. Two image pairs [230 pairs of bilateral breast digital breast tomosynthesis (DBT) images with 115 malignant tumors and contralateral tissue (M/N), and 115 bilateral normal areas (N/N)] were generated from each patient enrolled in this study. The proposed deep learning model is called bilateral asymmetrical detection (BilAD), which is a modified convolutional neural network (CNN) model of Xception with two-dimensional tensors for bilateral breast images. BilAD was trained to classify the differences between pairs of M/N and N/N datasets. The results of the BilAD model were compared to those of the unilateral control CNN model (uCNN). The results of BilAD and the uCNN were as follows: accuracy, 0.84 and 0.75; sensitivity, 0.73 and 0.58; and specificity, 0.93 and 0.92, respectively. The mean area under the receiver operating characteristic curve of BilAD was significantly higher than that of the uCNN (p = 0.02): 0.90 and 0.84, respectively. The proposed deep learning model trained by embedding a diagnostic algorithm to examine the asymmetry of bilateral breast tissue improves the diagnostic accuracy for breast cancer.

An association between mammographic breast density and fine particulate matter among postmenopausal women.

Increasing breast density is a risk factor for breast cancer. Geographic variations in breast density may be due to differences in lifestyle and diet, as well as environmental factors such as air pollution exposure. However, these environmental contributors have not been established. In this study, we evaluated an association between air pollution and mammographic breast density. The study population for this study was postmenopausal women who had undergone screening mammography at the Center for Preventive Medicine, St. Luke's International Hospital, from April 2004 to September 2018. Individual mammography results were obtained from electronic charts. The ambient air pollution (PM2.5) density of the locations of interest, namely, the patients' residential areas during the study period, was obtained. The mean PM2.5 exposure levels for 1, 3, 5, and 7 years were determined. A generalized estimating equations model was used to examine the association between air pollution density and dense breast. A total of 44,280 mammography results were included in this study, and 29,135 were classified in the non-dense breast group and 15,145 in the dense breast group. There was a 3% increase in the odds of having dense breasts after 1 year (OR = 1.027, 95% confidence interval (CI) 1.019-1.034) and 3 years of PM2.5 exposure (OR = 1.029, 95% CI 1.022-1.036). This further increased to 4% at 5-year exposure (OR = 1.044, 95% CI 1.037-1.052) and 5% at 7-year exposure (OR = 1.053, 95% CI 1.044-1.063). The risk for dense breasts increased if the factors of smoking, family history of breast and/or ovarian cancer, and history of childbirth were present.

Association between mammographic breast composition and breast cancer risk among Japanese women: a retrospective cohort study.

BACKGROUND: Mammographic breast composition is associated with breast cancer risk. However, evidence in a Japanese cohort investigating this association is scarce. Thus, we aimed to compare breast cancer risk between women with and without dense breasts. METHODS: All Japanese women who underwent breast cancer screening at a tertiary care academic hospital-affiliated preventive center at least twice with known baseline mammographic breast composition were included in this study. A single-center retrospective cohort study was conducted among 24,863 women who had 125,566 screening opportunities between April 1, 2005, and March 31, 2015. All women were categorized into two groups based on their baseline breast composition: women with dense breasts (13,815) and women with non-dense breasts (11,048). We compared the demographic characteristics between the two groups. After calculating person-years, Cox proportional hazards analyses were performed to estimate the hazard ratio (HR) of developing breast cancer according to breast composition status. RESULTS: During the study period, 358 breast cancer cases were identified. The dense and non-dense groups differed significantly by age, body mass index, family history of breast cancer, physical activity, history of smoking and alcohol consumption, number of pregnancies, and number of deliveries. After adjusting for these factors, Cox proportional hazards analyses showed that women with dense breasts had a significantly higher HR for developing breast cancer than women without dense breasts. The association was even stronger in younger women (≤ 50 years old), but it did not achieve statistical significance in older women. CONCLUSION: Dense breasts at baseline are a risk factor for developing breast cancer in Japanese women. However, this association was only observed in women aged 50 years or younger at the time of entry into the screening program.

Impact of childbirth history on dense breast in mammographic screening: a cross-sectional study.

BACKGROUND: The evaluation of breast density is important, because dense breast has been shown to be associated with increased risk of breast cancer and a greater risk of a false-negative diagnostic performance due to masking a tumor. Although the relationship between parity and dense breast is under investigation, conclusive evidence is lacking. We aimed to investigate whether parity affects breast density. METHODS: The study design is a cross-sectional study. The subjects are healthy Japanese women who underwent opportunistic mammographic screening at the center for preventive medicine at a single institution from January 2016 to December 2018. Clinical characteristics and lifestyle factors were obtained from questionnaires. Breast density was categorized into 4 groups, namely, almost entirely fatty dense, scattered fibroglandular dense, heterogeneously dense, and extremely dense, according to the Breast Imaging Reporting and Data System. Heterogeneously and extremely dense were considered collectively as dense breast. Multivariate logistic regression analysis was conducted to investigate the relationship between parity and dense breast among premenopausal and postmenopausal women separately. RESULTS: 7612 premenopausal and 9252 postmenopausal women were investigated. Dense breast was shown in 62.6% of nulliparity, 57.3% of single parity, 47.3% of two parity, 37.6% of more than two parity among premenopausal women, and in 41.6% of nulliparity, 31.1% of single parity, 19.3% of two parity, 10.1% of more than two parity among postmenopausal women. For premenopausal women, two parity, single parity and nulliparity showed a higher risk for dense breast with statistically significance (Odds Ratio (OR) adjusted for potential confounding factors: 1.458 (95% Confidence interval (CI); 1.123-1.894), 2.349 (95%CI; 1.801-3.064), 3.222 (95%CI; 2.500-4.151), respectively), compared with more than two parity. For postmenopausal women, two parity, single parity and nulliparity had a higher risk (OR: 1.849 (95%CI; 1.479-2.312), 3.023 (95%CI; 2.385-3.830), 4.954 (95%CI; 3.975-6.174), respectively) with statistically significance, compared with more than two parity. CONCLUSIONS: Parity showed an inverse trend of having dense breast among both premenopausal and postmenopausal women. In particular, nulliparous women need to recognize their higher risk of dense breast. In the future, the declining fertility rate may affect the prevalence of dense breast in the world.

Relationship Between Breast Density, Breast Cancer Subtypes, and Prognosis.

BACKGROUND: In the United States, Europe, and Asia, a consensus has been reached that there is a higher risk of breast cancer in high density breasts. However, there are some contrary reports that suggest the absence of an association between breast composition and breast cancer subtype; thus, there is conflicting evidence. The purpose of this study was to investigate trends in the incidence of breast cancer subtypes according to breast composition and analyze the survival rates in Japanese women. PATIENTS AND METHODS: Between 2007 and 2008, 1258 Japanese patients with invasive breast cancer who underwent mammography and obtained a pathological diagnosis in our institution were included in the study. We compared cancer subtypes with breast composition types (dense and non-dense breast), and classified them based on initial mammography findings. Information on 5- and 10-year survival rates was collected by chart review for patients with dense and nondense breasts. Statistical analysis was performed using the Pearson's chi-square test for breast composition and cancer subtype. The effect of breast composition on mortality was examined using a multivariate Cox proportional hazards model, and adjusted hazard ratios were calculated. RESULTS: No significant difference was found between breast cancer subtype and breast composition (P = .08). Five-year (log-rank test, P = .09) and 10-year (log-rank test, P = .31) survival rates were not significantly different between breast composition types. CONCLUSION: There was no significant association between breast composition and cancer subtypes. There was also no significant difference in the prognosis between patients with and without dense breasts.

Comparative study of the usefulness of adjunctive tomosynthesis in breast cancer screening by mammography and ultrasound in Japan.

INTRODUCTION: There are few studies have conducted digital breast tomosynthesis (DBT) in addition to digital mammography (2DDM) and ultrasound (US) for screening. The purpose of this study is to determine the possibility of synergistic effects of DBT and US screening for Japanese. METHODS: 5023 examinations of the opportunistic screening using 2DDM and US (2D group: 2581) or 2DDM and US plus DBT (3 group: 2442) were performed at our facility from May 1, 2017 to March 31, 2019. This study was not RCT, and the backgrounds of the two groups were different. RESULTS: The recall rate was 3.1% in the 2D group and 2.6% for the 3D group (p = 0.27). The number of detected cancer cases was 6 (0.23%) in the 2D group and 12 (0.49%) in the 3D group (p = 0.16). The positive predictive value (PPV) was 7.4% for the 2D group and 19.0% for the 3D group (p = 0.045). There was one invasive ductal carcinoma case which had no findings in 2DDM and US, but had a slight distortion in the images of DBT. CONCLUSION: We examined and reported whether DBT was useful for breast cancer screening combined with mammography and US. Compared to the 2D group, the 3D group showed better results of PPV with significant difference. However, due to the non-randomized design and difference between the two groups, the results should be interpreted in caution. Adding DBT in 2DDM and US screening would be acceptable only if the benefits and disadvantages are explained to the women undergoing the screening.

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.