Mammography Data Research Articles

Young Black Women May be More Likely to Have First Mammogram Cancers: A New Perspective in Breast Cancer Disparities.

Black women are diagnosed with breast cancer at earlier ages and are 42% more likely to die from the disease than White women. Recommendations for commencement of screening mammography remain discordant. This study sought to determine the frequency of first mammogram cancers among Black women versus other self-reported racial groups. In this retrospective cohort study, clinical and mammographic data were obtained from 738 women aged 40-45years who underwent treatment for breast cancer between 2010 and 2019 within a single hospital system. First mammogram cancers were defined as those with tissue diagnoses within 3months of baseline mammogram. Multivariate logistic regression was applied to assess variables associated with first mammogram cancer detection. Black women were significantly more likely to have first mammogram cancer diagnoses (39/82, 47.6%) compared with White women (162/610, 26.6%) and other groups (16/46, 34.8%) [p < 0.001]. Black women were also more likely to have a body mass index > 30 (p < 0.001), higher clinical T categories (p = 0.02), and present with more advanced clinical stages (p = 0.03). Every month delay in mammographic screening beyond age 40years (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.05-1.07; p < 0.0001), Black race (OR 2.24, 95% CI 1.10-4.53; p = 0.03), and lack of private insurance (OR 2.41, 95% CI 1.22-4.73; p = 0.01) were associated with an increased likelihood of cancer detection on first mammogram. Our findings suggests that Black women aged 40-45years may be more likely to have cancer detected on their first mammogram and would benefit from starting screening mammography no later than age 40years, and for those with elevated lifetime risk, even sooner.

Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.

To assess the significance of peritumoral features based on deep learning in classifying non-spiculated and noncalcified masses (NSNCM) on mammography. We retrospectively screened the digital mammography data of 2254 patients who underwent surgery for breast lesions in Harbin Medical University Cancer Hospital from January to December 2018. Deep learning and radiomics models were constructed. The classification efficacy in ROI and patient levels of AUC, accuracy, sensitivity, and specificity were compared. Stratified analysis was conducted to analyze the influence of primary factors on the AUC of the deep learning model. The image filter and CAM were used to visualize the radiomics and depth features. For 1298 included patients, 771 (59.4%) were benign, and 527 (40.6%) were malignant. The best model was the deep learning combined model (2mm), in which the AUC was 0.884 (P < 0.05); especially the AUC of breast composition B reached 0.941. All the deep learning models were superior to the radiomics models (P < 0.05), and the class activation map (CAM) showed a high expression of signals around the tumor of the deep learning model. The deep learning model achieved higher AUC for large size, age >60 years, and breast composition type B (P < 0.05). Combining the tumoral and peritumoral features resulted in better identification of malignant NSNCM on mammography, and the performance of the deep learning model exceeded the radiomics model. Age, tumor size, and the breast composition type are essential for diagnosis.

Аналіз ступенів ризику наявності новоутворів у молочних залозах жінок зрілого і літнього віку за даними скринінгової мамографії

The purpose of the study was to find out the frequency of manifestation of pathological changes in the mammary glands during screening mammographic examination in a randomized sample of mature and elderly women. Materials and methods. According to the results of the processing of a randomized sample of archival anonymized mammograms of 100 women (50 – mature and 50 – elderly) who underwent a screening or preventive examination, the state of breast condition was assessed according to the BI-RADS scale in accordance with the degree of risk of the presence of mammary gland neoplasms, using the following assessment categories: 0 – incomplete study (additional examination is required); 1 – negative (no neoplasm detected); 2 – benign changes. Results and discussion. It was established that among mature and elderly women undergoing screening or preventive mammographic examinations, in 19% only age changes in the mammary gland were verified. Benign changes in the form of calcifications, calcified vessels, axillary or intramammary lymph nodes without metastatic signs of degeneration, which correspond to category 2 on the BI-RADS scale, were found in one mammary gland or bilaterally in 72% of the examined individuals. 28% of examined mature and elderly women need additional examination to verify the diagnosis. It was established that among mature and elderly women, according to mammography data, categories 1 and 2 on the BI-RADS scale are more often manifested on the right, category 0 – on the left, and the frequency of manifestation of changes corresponding to category 0 on the BI-RADS scale among the elderly increases comparing to mature. Studying the frequency of detection of pathological changes in mammary gland during screening examinations in randomized samples can become a theoretical basis for forecasting the dynamics of mammary gland incidence in women of different age groups, identifying risk groups and developing preventive measures for mammary gland pathology of various genesis and methods of its early diagnosis. Conclusion. Among mature and elderly women undergoing screening or preventive mammographic examinations, only age-related changes were verified in the mammary glands in only 19%, and changes which are verified as benign or require additional examination were detected in 81%. According to the results of mammography, 28% of examined mature and elderly women need additional examination to verify the diagnosis. Among mature and elderly women in a randomized sample, according to mammography data, categories 1 and 2 on the BI-RADS scale are more often manifested on the right, category 0 – on the left. The frequency of manifestation of changes corresponding to category 0 on the BI-RADS scale among the elderly increases compared to the mature

Analysis of the manifestation frequency of different variants of calcifications in the mammary glands in women of mature and old age according to screening mammography data

The relevance of the problem of early diagnosis of pathological changes in the mammary glands (MG) of women of different ages is due to the fact that in the world rankings of neoplasms localization, both benign and malignant, the MG occupies a leading position today. According to multiple information sources, more than 80% of women of reproductive age are diagnosed with various MG diseases. For the purpose of early detection of pathological changes developing in MG today, mammography is widely used, which allows easy visualization of calcifications, MG and obtaining their complete characteristics.The purpose of our study was to determine the frequency of detection and to study the age-related dynamics of different types of calcifications in the mammary glands of mature and elderly women according to the mammography results.Material and methods. In the course of the research, a randomized sample of archival anonymized mammograms of 100 women (50 mature and 50 elderly) who underwent screening or preventive examination was processed. Examinations were performed on the Fujifilm Amulet Innovality device (Japan) in direct (SS) and medial-lateral oblique (MLO) projections. Calcifications were assessed by shape, number, and size.Research results. It was determined that in a randomized sample of examined women, only 34% of adults and 28% of elderly people did not have calcifications in the mammary glands. In 24% of adults and 48% of elderly people, calcifications were detected in the mammary glands bilaterally, in another 42% of adults and 24% of elderly people - in only one of the the mammary glands. Among women of mature age, the largest specific share in the examined sample belongs to persons without calcifications in the mammary glands; among the elderly, the largest share is women with calcifications in both mammary glands. According to the results of the conducted research in a randomized sample of mature and elderly women, the following was found: isolated scattered punctate calcifications (35% of the examined); single punctate/round calcifications (23% of the examined); annular calcifications (11% of the examined); multiple calcifications (19% of the examined); calcifications larger than 1 mm (14% of the examined); calcified vessels (4% of the examined).Conclusions. Digital mammography is a highly informative method of examining the mammary glands, which allows us to visualize calcifications and differentiate them by size, shape and number. According to the results of the analysis of a randomized examination sample of 100 mature and elderly women, various variants of calcifications were found as single, diffuse, punctate; single punctate/round; annular; multiple; greater than 1 mm; as well as calcified vessels. With age, the frequency of mammary gland calcifications increases.

Results of Two-Years Mammographic Screening in the Kaluga Region

Objective: approbation of the project of mass breast cancer mammographic screening in the Kaluga Region.Material and methods. From April 2018 to December 2019, screening mammography was performed in 35,439 women aged from 38 to 93 years (mean age 57.33 ± 8.07 years). The study was carried out on mobile mammography complexes assembled on Russian KamAZ vehicles with Russian-made equipment from Amiko company. Mammography was performed in frontal and oblique projections. BI-RADS ACR international classification was used to standardize the identified changes and assess the sensitivity and specificity of the study method.Results. Out of the total number of women examined, pathological changes (BI-RADS IV–V) were assumed according to mammography data in 778 (2.2%) cases, of which 700 (1.98%) patients appealed to an oncological institution. Benign processes were found in 394 (1.11%) patients, breast cancer in 164 (0.46%). Without verification of the diagnosis, 137 (0.39%) patients remained under the supervision of oncologists. Distribution of detected tumors by stages for 2 years of observation: stage 0 – 7, stage 1 – 73, stage 2 – 56, stage 3 – 13, stage 4 – 3. The stage was not set in 4 cases. The total number of those examined with verification of the diagnosis in the oncological institution was 596. The proportion of correct diagnoses was 94%. The total number of diagnostic errors that would occur in the absence of a true double scan accounted for 36 (6%) cases.Conclusion. The proposed screening method makes it possible to increase the breast cancer detection in the early preclinical stages of the disease, to provide the available diagnostics for the population due to mobile mammography complexes, and to speed up the process of diagnostics and further hospitalization for examination and necessary treatment according to the disease stage in a specialized oncological institution.

Virtual Biopsy by Using Artificial Intelligence-based Multimodal Modeling of Binational Mammography Data.

Background Computational models based on artificial intelligence (AI) are increasingly used to diagnose malignant breast lesions. However, assessment from radiologic images of the specific pathologic lesion subtypes, as detailed in the results of biopsy procedures, remains a challenge. Purpose To develop an AI-based model to identify breast lesion subtypes with mammograms and linked electronic health records labeled with histopathologic information. Materials and Methods In this retrospective study, 26 569 images were collected in 9234 women who underwent digital mammography to pretrain the algorithms. The training data included individuals who had at least 1 year of clinical and imaging history followed by biopsy-based histopathologic diagnosis from March 2013 to November 2018. A model that combined convolutional neural networks with supervised learning algorithms was independently trained to make breast lesion predictions with data from 2120 women in Israel and 1642 women in the United States. Results were reported using the area under the receiver operating characteristic curve (AUC) with the 95% DeLong approach to estimate CIs. Significance was tested with bootstrapping. Results The Israeli model was validated in 456 women and tested in 441 women (mean age, 51 years ± 11 [SD]). The U.S. model was validated in 350 women and tested in 344 women (mean age, 60 years ± 12). For predicting malignancy in the test sets (consisting of 220 Israeli patient examinations and 126 U.S. patient examinations with ductal carcinoma in situ or invasive cancer), the algorithms obtained an AUC of 0.88 (95% CI: 0.85, 0.91) and 0.80 (95% CI: 0.74, 0.85) for Israeli and U.S. patients, respectively (P = .006). These results may not hold for other cohorts of patients, and generalizability across populations should be further investigated. Conclusion The results offer supporting evidence that artificial intelligence applied to clinical and mammographic images can identify breast lesion subtypes when the data are sufficiently large, which may help assess diagnostic workflow and reduce biopsy sampling errors. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Tumor volume doubling time estimated from digital breast tomosynthesis mammograms distinguishes invasive breast cancers from benign lesions.

The aim of this study was to determine whether lesion size metrics on consecutive screening mammograms could predict malignant invasive carcinoma versus benign lesion outcome. We retrospectively reviewed suspicious screen-detected lesions confirmed by biopsy to be invasive breast cancers or benign that were visible on current and in-retrospect prior screening mammograms performed with digital breast tomosynthesis from 2017 to 2020. Four experienced radiologists recorded mammogram dates, breast density, lesion type, lesion diameter, and morphology on current and prior exams. We used logistic regression models to evaluate the association of invasive breast cancer outcome with lesion size metrics such as maximum dimension, average dimension, volume, and tumor volume doubling time (TVDT). Twenty-eight patients with invasive ductal carcinoma or invasive lobular carcinoma and 40 patients with benign lesions were identified. The mean TVDT was significantly shorter for invasive breast cancers compared to benign lesions (0.84 vs. 2.5 years; p = 0.0025). Patients with a TVDT of less than 1 year were shown to have an odds ratio of invasive cancer of 6.33 (95% confidence interval, 2.18-18.43). Logistic regression adjusted for age, lesion maximum dimension, and lesion volume demonstrated that shorter TVDT was the size variable significantly associated with invasive cancer outcome. Invasive breast cancers detected on current and in-retrospect prior screening mammograms are associated with shorter TVDT compared to benign lesions. If confirmed to be sufficiently predictive of benignity in larger studies, lesions visible on mammograms which in comparison to prior exams have longer TVDTs could potentially avoid additional imaging and/or biopsy. • We propose tumor volume doubling time as a measure to distinguish benign from invasive breast cancer lesions. • Logistic regression results summarized the utility of the odds ratio in retrospective clinical mammography data.

Anatomical breast imaging-derived parameters do not provide incremental information in prediction of nonvisualization of sentinel lymph nodes on lymphoscintigraphy.

ObjectiveAccurate sentinel lymph node (SLN) staging is essential for both prognosis and treatment in patients with breast cancer. However, the preoperative lymphoscintigraphy may fail to visualize the SLN. The aim of this retrospective study was to investigate whether parameters derived from anatomical breast imaging can predict SLN nonvisualization on lymphoscintigraphy.MethodsFor this retrospective study, all data of mammography, breast MRI, and lymphoscintigraphy of SLN procedures from January 2016 to April 2021 were collected and reviewed from the Amsterdam UMC database.ResultsA total of 758 breast cancer patients were included in this study. SLN nonvisualization on planar lymphoscintigraphy at 2-h postinjection (pi) was 29.7% and was reduced after a second injection to 7.5% at late lymphoscintigraphy 4-h pi. Multivariable analysis showed that age ≥ 70 years (P = 0.019; OR, 1.82; 95% CI, 1.10–3.01), BMI ≥ 30 kg/m2 (P = 0.031; OR, 1.59; 95% CI, 1.04–2.43), and nonpalpable tumors (P = 0.034; OR, 1.54; 95% CI, 1.03–2.04) were independent predictors of SLN nonvisualization. Differences in tumor size, Breast Imaging-Reporting and Data System classification, or breast density were not significantly associated with SLN nonvisualization.ConclusionThis study shows that, by using a multivariable analysis, risk factors for SLN nonvisualization in breast cancer patients during preoperative lymphoscintigraphy at 2-h pi are age ≥ 70 years, BMI ≥ 30 kg/m2, and nonpalpable tumors. Parameters derived from mammography or breast MRI, however, are not useful to predict SLN nonvisualization on lymphoscintigraphy.

Multi-Task Fusion for Improving Mammography Screening Data Classification.

Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy. We fuse model predictions and high-level features from deep learning models with hybrid patient models to build stronger predictors on patient level. To this end, we propose a multi-branch deep learning model which efficiently fuses features across different tasks and mammograms to obtain a comprehensive patient-level prediction. We train and evaluate our full pipeline on public mammography data, i.e., DDSM and its curated version CBIS-DDSM, and report an AUC score of 0.962 for predicting the presence of any lesion and 0.791 for predicting the presence of malignant lesions on patient level. Overall, our fusion approaches improve AUC scores significantly by up to 0.04 compared to standard model ensembling. Moreover, by providing not only global patient-level predictions but also task-specific model results that are related to radiological features, our pipeline aims to closely support the reading workflow of radiologists.

Abstract P1-01-09: Prediction of node negative breast cancer and high disease burden through image analysis software on mammographic images and clinicopathological data

Abstract Background/Aim: Sentinel lymph node biopsy is the standard staging procedure performed on all clinically node negative breast cancer patients, although more than 70% have no lymph node metastases and would not benefit from the procedure. Thus, a preoperative method of predicting lymph node status is warranted. Recently, artificial intelligence-based breast malignancy detection systems for mammograms have been developed, detecting both lesions and microcalcification. Previous models based on clinicopathological data, proposed by Dihge et al.1, 2, received an area under the curve (AUC) of 0.74 for prediction of negative lymph node status and an AUC of 0.75 for prediction of high-burden disease. In this study we aim to predict node negativity (N0) and high-burden disease (N2) in breast cancer patients through mammographic features captured by image analysis software on mammography, added to previous prediction models. Method: This is a retrospective cohort study including 770 women with unilateral breast cancer operated at Lund University Hospital 2009-2012. Mammographic images were identified for 755 women and analyzed by two image analysis software applications, Transpara and Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). Transpara findings were cross-checked with Picture Archiving and Communication System for tumor localization. Clinicopathological variables, soft tissue lesion scores, calc cluster scores, breast density, malignancy score and radiologic tumor size were collected. Prediction models were created using multivariable logistic regression. AUC assessed the performance of the models to predict N0 and N2. Results: Univariable logistic regression showed an association between axillary lymph node status and radiologic size, highest score of soft tissue lesion, highest score of calc cluster and malignancy score. Addition of highest score of soft tissue lesion and highest score of calc cluster to the previously published model1 for prediction of N0 resulted in an AUC of 0.75 (confidence interval (CI) 0.70-0.79). Addition of highest score of calc cluster to a modified version of the previously published model2 for prediction of high-burden disease (N2 versus N0 and N1) resulted in an AUC of 0.83 (CI 0.75-0.90). A comparison of pathologic and radiologic tumor size showed a strong correlation between the variables and associations with lymph node status. Mammographic density was not associated to nodal status. Conclusion: The prediction models proposed in this abstract, including radiomic features, did not significantly improve the previous clinicopathological models1,2. Nevertheless, point estimates of the AUCs were improved by 0.01 and 0.08, respectively, indicating that radiomic features could be of added value in the prediction of node-negative disease and high nodal disease burden and should be further investigated. The strong correlation between measurements of tumor size suggests that radiologic tumor size could replace pathologic size to enable preoperative prediction of nodal status. 1 Dihge L, Bendahl PO, Ryden L. Nomograms for preoperative prediction of axillary nodal status in breast cancer. Br J Surg 2017;104:1494-5. 2 Dihge L, Ohlsson M, Edén P, Bendahl, PO, Rydén L. Artificial neural network models to predict nodal status in clinically node-negative breast cancer. BMC Cancer 2019 Jun 21;19(1):610. doi: 10.1186/s12885-019-5827-6 Table: Multivariable logistic regression of prediction models.Predictions modelsPrediction of negative lymph node status. (n=560)Prediction of high axillary disease burden. (n=576)OR (95% CI)POR (95% CI)PTumor size mm (continuous)0.950 (0.926-0.974)&amp;lt;0.001Tumor size mm (continuous)1.062 (1.019-1.107)0.005Multifocality0.006Multifocality0.655Yes1.00 (ref)Yes1.00 (ref)No1.89 (1.20-2.98)No0.82 (0.34-1.98)Lymphovascular invasion&amp;lt;0.001Lymphovascular invasion&amp;lt;0.001Yes1.00 (ref)Yes1.00 (ref)No3.97 (2.25-7.02)No0.20 (0.08-0.48)Age years (continuous)1.022 (1.004-1.040)0.015PR status0.122Mode of detection0.019Positive1.00 (ref)Symptomatic1.00 (ref)Negative0.35 (0.09-1.33)Mammographic screening1.70 (1.09-2.66)Histological type0.857Molecular subtypes0.027aLobular1.00 (ref)Luminal A1.00 (ref)Other0.89 (0.25-3.21)Luminal B HER2 negative1.20 (0.76-1.91)0.452Tumor localization0.716aLuminal B HER2 positive1.00 (0.48-2.09)0.995Upper outer1.00 (ref)HER2 positive2.00 (0.47-8.59)0.352Central1.29 (1.13-13.0)0.830Triple negative5.68 (1.95-16.5)0.001Upper inner1.40 (0.32-6.12)0.656Highest score of soft tissue lesion0.283aLower inner1.92 (0.35-10.7)0.4561-791.00 (ref)Lower outer2.79 (0.77-10.2)0.12080-911.40 (0.65-3.03)0.393Overlapping1.96 (0.69-5.60)0.20992-951.13 (0.53-2.42)0.745Highest score of calc cluster0.456a≥960.83 (0.37-1.88)0.6611-921.00 (ref)Absence1.69 (0.74-3.87)0.216≥932.86 (0.55-14.9)0.212Highest score of calc cluster0.510aAbsence2.20 (0.46-10.4)0.3231-921.00 (ref)≥930.64 (0.31-1.36)0.246Absence0.78 (0.42-1.44)0.425Constant0.1700.026Constant0.0230.003AUC0.75 (0.70-0.79)AUC0.83 (0.75-0.90)P of ≤0.05 was considered significant. Abbreviations: OR =odds ratio, CI = confidence interval, HER2 = human epidermal growth factor receptor 2. a Test of overall effect Citation Format: Cornelia Rejmer, Looket Dihge, Pär-Ola Bendahl, Daniel Förnvik, Magnus Dustler, Lisa Rydén. Prediction of node negative breast cancer and high disease burden through image analysis software on mammographic images and clinicopathological data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-01-09.

Abstract P2-10-06: Validation of abridged breast cancer risk assessment model for the general population

Abstract The ability to efficiently and accurately risk stratify women can lead to improved risk-reduction measures. Actionable risk thresholds for both screening and risk-reduction guidance exist; however, despite several clinical risk models currently in existence, the utilization of such models is underwhelming. We have developed and validated an abridged clinical risk model, COSMO (Clinical pOlygenic denSity faMily mOdel), incorporating three of the most relevant epidemiological risk factors associated with sporadic and familial breast cancer in a format that is designed for “ease of use”. Polygenic risk is recognized as a promising epidemiological risk factor however, its value is greater when integrated with other phenotypic risk factors including 1st and 2nd family history, breast density menopausal status, BMI and age. We used 200,009 active UK Biobank (UKB) members who were female, genetically Caucasian, had SNP data for 313 breast cancer associated variants and aged 40 to 69 years at baseline assessment date. We used age and date of baseline assessment to calculate censoring age for those who had died before completing 5 years of follow-up and to determine the date at which 5 years of follow-up was completed for the others. We used both self-reported and linked cancer registry data (ICD9 code 174 or ICD10 code C50) to determine the earliest diagnosis of invasive breast cancer for each affected woman. In terms of discrimination, COSMO (AUC=0.651; 95% CI 0.642, 0.661) performed better than the Gail model (AUC=0.567; 95% CI=0.557, 0.567); P value for difference &amp;lt;0.0001. Overall calibration showed the number of breast cancers expected using COSMO was slightly higher than the number observed (SIR=0.94; 95% CI=0.91, 0.97), while for the Gail model, the number of breast cancers was lower than the number observed (SIR=1.07; 95% CI=1.04, 1.11). The modest over estimation may be explained by the “healthy volunteer” selection bias of the UKB. Importantly, COSMO significantly improved predicted risk classification of women compared to the Gail when stratified according to actionable thresholds. (Table) Limitations to the validation include the lack of mammographic breast density data in the UKB—a critical factor in COSMO. To this end, we have an ongoing validation in a Nurses’ Health Study population (1,269 cases and 1,812 controls) that includes breast density data. Ongoing model comparison studies will confirm the equivalence of COSMO to standard clinical models. If performance is equivalent, COSMO has an advantage in clinical implementation because the abridged model can be less time consuming to implement in a general practice or screening clinic setting due to the truncated pedigree. Furthermore, as multiethnic cohort data access continues to improve, the epidemiological components can be recalibrated for application across any ethnicity. COSMO integrates the most impactful epidemiological risk factors, is well calibrated for the general population and provides improved discrimination over a standard risk model. General population risk assessment like COSMO can lead to the identification of more women who would benefit from clinical intervention based on current risk-reduction guidelines. Beyond that, incidental by products of a general population risk assessment include promoting breast cancer awareness, empowering women to understand their own risk, and encouraging all women to consider their lifestyle habits. Citation Format: Erika Spaeth, Gillian Dite, Richard Allman. Validation of abridged breast cancer risk assessment model for the general population [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-06.

Abstract P3-14-09: Breast cancer disparities through an imaging lens: Are black women more likely to have cancer detected on their first mammogram?

Abstract Introduction Black women are more likely to be diagnosed with early-onset breast cancer, have triple negative disease, carry pathogenic variants in BRCA2, and are 41% more likely to die from the disease. The American College of Radiology recommends risk assessment for all Black women by age 30. Still, it remains unclear whether this population may benefit from earlier screening mammography. We evaluated women treated for breast cancer between ages 40 and 45 to determine the frequency of cancer detection on first mammogram for Black patients versus other racial/ethnic groups. We also analyzed risk factors associated with first mammogram-detected cancers. Methods Demographic, mammographic, and clinico-pathologic data were obtained from 724 women ages 40-45 who underwent oncologic surgery for breast cancer at our institution between 2010 and 2019. We defined first mammogram cancers as those with tissue diagnoses within three months of first mammogram. A logistic regression model was applied to assess the association of collected variables with cancer detection on baseline mammogram by racial/ethnic group. Results In the overall cohort of 724 patients, the mean age at breast cancer diagnosis was similar across ethnic groups (42.8 years, p=0.624). Black women were more likely to have a BMI greater than 30 (p=&amp;lt;0.001), to be current smokers (p=0.033), to have a variant of unknown significance on germline genetic testing (p=0.049), and present with Stage 2 or Stage 3 disease. Black women were also significantly more likely to have breast cancer detected on their first mammogram (38/80, 47.5%) compared to White women (153/611, 25.0%) and Asian women (7/21, 33.0%) (p = &amp;lt;0.001). One hundred ninety eight patients (27.3% of overall cohort) were diagnosed with breast cancer on their first mammogram. The mean age at diagnosis was similar across ethnicities within this subgroup (42.2 years, p=0.136). The only risk factor of statistical significance detected differentially in Black women among both the overall cohort and the subgroup of first mammogram cancer diagnoses was a BMI greater than 30. Black women with cancers detected on first mammogram presented more often with later stage disease, though this trend did not reach statistical significance (p=0.07). Conclusion Breast cancer screening guidelines for women age 40-45 vary. Unless a woman is evaluated based on family history and pedigree suggestive of hereditary cancer or familial clustering, there are no screening guidelines for women under age 40. Women identified with BRCA mutations, for example, would initiate high-risk screening at the age of 25, and even those with family history of early onset disease in the absence of a genetic mutation would initiate high risk screening to include contrast-enhanced MRI ten years earlier than the first affected relative. Our data suggests that Black women between age 40 and 45 are more likely to have cancer detected on their first mammogram. Early risk stratification and assessment, education and counseling on risk mitigation, and possibly initiation of earlier screening (even in the absence of family history) may be warranted in this group. First Mammogram Cancers by RaceAllAsianBlackWhitep-valueN=712N=21N=80N=611Cancer on first mammogram, N (%):198 (27.8%) 7 (33.3%) 38 (47.5%) 153 (25.0%) &amp;lt;0.001 Citation Format: Avia Wilkerson, Megan Obi, Camila Ortega Estrella, Chao Tu, Holly Pederson, Zahraa Al-Hilli. Breast cancer disparities through an imaging lens: Are black women more likely to have cancer detected on their first mammogram? [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-14-09.

Abstract P3-01-11: A clinical study indicates that 3D mammography shows no improvement over 2D mammography in cancer detection rates and biopsy-derived positive predictive value among BI-RADS 4 populations

Abstract The Breast Imaging Reporting and Data System (BI-RADS) was developed to standardize breast imaging reporting and facilitate cancer-probability communication and biopsy decision-making. However, the probability of malignancy for BI-RADS 4 designated breast lesions ranges from 2 - 95% and contributes to a high unnecessary biopsy rate. Over the decades, BI-RADS 4 tissue biopsy-proven positive predictive value (PPV3) rate have not improved (currently 21.1%) and translates to high false-positive rates of mammography. Recent advancements in breast cancer evaluation have resulted in digital breast tomosynthesis (DBT) to generate 3D images. Objectives. 1. To investigate the clinical performance of digital mammography (2D) versus DBT (3D) among Houston Methodist's BI-RADS 4 population. 2. To compare both modalities and determine if 3D mammography performs better than 2D mammography especially per cancer detection rates (CDR) and biopsy-derived positive predictive value (PPV3) among BI-RADS 4 population in a health system in Texas. Methods. We extracted retrospective clinical and mammography data for patients who underwent screening or diagnostic examination using 2D (DM) and 3D (DBT), performed between February 1, 2015, to September 30, 2020, from our clinical data warehouse at Houston Methodist. Data elements extracted include patient demographics including age, gender, race, and marital status; mammography modality used (2D vs. 3D); personal history of breast cancer; history of prior mammogram; final BI-RADS category; biopsy type: core needle or surgical; pathology results (malignant or benign) performed within three months after the mammogram; tumor staging; and hormone receptor and growth-promoting protein expression i.e. ER, PR, and HER2 status. Using student t, Fisher's exact, and Chi-squared tests, we evaluated the collected data to determine statistical significance of the difference between modalities in BI-RADS 4 cases including malignancy rate. We calculated the adjusted odds ratio between modalities for cancer detection rate (CDR) and biopsy-derived positive predictive value (PPV3). A p-value of &amp;lt; 0.05 was considered statistically significant. Results. A total of 158,630 encounters (83,905 unique patients) and 185,213 encounters (106,169 unique patients) had 2D and 3D mammography respectively performed across our hospital system. Out of these, 6,887 encounters (6,462 unique patients) in 2D and 6,483 encounters (6151 unique patients) in 3D were assessed as BI-RADS 4 within the period. Using Fisher’s exact test, the results show that the BI-RADS 4 assessed cases in 2D mammography are significantly more than those in 3D mammography by 24%, p-value &amp;lt; 10e-16 (1.24; 95% CI: 1.198 - 1.284). Among the parameters, only racial distribution (P=0.0018), history of breast cancer (P=0.0030), and prior mammogram performed (p&amp;lt;2e-16) were found to be significantly different between the modalities. The CDR among BI-RADS 4 cases were 117.47 for 2D and 122.32 for 3D with an adjusted odds ratio of 0.99 (0.89, 1.10) P=0.8725 adjusted by logistic regression. The PPV3 among BI-RADS 4 cohort were 15.09% (2D) and 16.27% (3D) and again, this difference was not significant with an adjusted odds ratio of 1.03 (0.92, 1.15), P=0.5796 adjusted by logistic regression. Conclusions. While DBT showed statistically significant improvement in performance and sensitivity in assigning BI-RADS 4 cases compared with DM i.e. 24% less assignment, there was no improvement in PPV3 and CDR in BI-RADS 4. Thus, DBT does not provide a reduction to unnecessary biopsies in BI-RADS 4 assessed patients. These findings are based on patient populations of our 9-hospital health system. Further investigation in different health systems is necessary to confirm these findings. Citation Format: Chika Frank Ezeana, Mamta Puppala, Lin Wang, Jenny C. Chang, Stephen T.C. Wong. A clinical study indicates that 3D mammography shows no improvement over 2D mammography in cancer detection rates and biopsy-derived positive predictive value among BI-RADS 4 populations [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-01-11.

Breast Imaging for Transgender Individuals: Assessment of Current Practice and Needs

The aim of this study was to investigate breast radiologists' practices related to recording sex and gender in the electronic medical record, knowledge and attitudes about breast cancer screening recommendations for transgender individuals, and experience and willingness to enter screening mammography data from transgender patients into databases that document service provision and outcomes of cancer detection protocols.A 19-question anonymous survey was distributed by e-mail to all active physician members of the Society of Breast Imaging. Response characteristics were assessed as frequencies and percentages and compared between groups using the Fisher exact test or χ2 test. The degree of agreement between questions was assessed using the McNemar test.Four hundred one radiologists across the United States and Canada responded (response rate 18%). Recording birth-assigned sex distinct from gender identity was reported by 44 of 352 respondents (13%). Depending on geographic region, 38% to 62% of breast radiologists followed screening guidelines for transgender women, and 226 of 349 (65%) did not provide screening recommendations for transgender men. Of 400, 324 (81%) believed that the evidence base for screening transgender individuals is incomplete, and 247 of 352 (70%) were either unsure of or had no Lesbian, Gay, Bisexual, Transgender, Queer competency training. A majority (247 of 401 [62%]) of respondents reported that they would enroll transgender patients in existing or novel national databases.In the practice of breast imaging, there is a substantial need to record transgender and other gender-nonconforming information. Breast radiologists differ in their practice and knowledge regarding screening of transgender women and men but expressed interest in contributing data to facilitate longitudinal databases needed to inform cancer screening guidelines.

The local diagnostic reference levels for breast screening using digital mammography at Songklanagarind Hospital

Introduction: This work aimed to determine local diagnostic reference levels (local DRLs) for screening mammography at Songklanagarind Hospital on digital mammography. Materials and methods: Retrospective data of screening mammography were collected from 400 patients from Songklanagarind Hospital, 200 patients from Tanyawej Breast Center and 200 patients from Premium Diagnostic Imaging Center. The patients were women aged between 40-75 years old with compressed breast thickness between 41-65 mm. and undertaken screening mammogram during 1st January 2019 – 31st October 2020. Patient data and exposure parameters collected were as follows: average glandular dose (AGD), entrance surface air kerma (ESAK), compressed breast thickness (CBT), compression force (CF), peak kilovoltage (kVp), tube current-time (mAs), target and filter (W/Rh, W/Al), and patient age. Results: The result showed that the average glandular dose of FFDM (2D mode) and DBT (3D mode) for compressed breast thickness 41-65 mm were 1.41±0.43 and 1.68±0.39 mGy, respectively and the 75th percentile of FFDM (2D mode) and DBT (3D mode) were 1.65 mGy and 1.89 mGy, respectively. The average ESAK were 4.93±1.96 mGy in FFDM (2D mode) and 5.31±1.55 mGy in DBT (3D mode), respectively. The 75th percentile of ESAK in FFDM (2D mode) and DBT (3D mode) were 6.03 and 6.17 mGy, respectively. There were 24.88 % and 24.63% received the average glandular dose over the 75th percentile in FFDM (2D mode) and DBT (3D mode). Conclusion: Local Diagnostic Reference Levels for FFDM (2D mode) and DBT (3D mode) at Songklanagarind Hospital were 1.65 mGy and 1.89 mGy. The Local DRLs in our study was safe and lower than the standard reference levels reported by the International Atomic Energy Agency at 3 mGy/view.

Current research opportunities for potential phytotherapeutic agents for the treatment of pathologies of the female reproductive system

Introduction and aim. Herbal medicine is prescribed for various disorders of the menstrual cycle (uterine bleeding, dysmenorrhea), for the treatment of premenstrual and climacteric syndromes, inflammatory diseases of the genital organs, mastopathy and mastalgia and other pathological conditions. The aim of the study is to analysis and generalization of data from professional literature and own experience in the treatment of patients with pathologies of the female reproductive system by phytotherapeutic methods, taking into account the influence of medicinal plants on various links in the pathogenesis of the disease, as well as making recommendations for improvement and prospects for the use of phytotherapy in the treatment of this pathology. Material and methods. To make an analysis of literary sources of domestic and foreign authors about usage of medicinal plants for the treatment of pathologies of the female reproductive system. Analysis of the literature. In the treatment of primary (spasmodic) dysmenorrhea, herbal remedies with an antispasmodic, analgesic, hormone-mimetic effects are prescribed. It can be Chamomile (Matricaria chamomilla L.), Achillea millefolium, commonly known as yarrow, Viburnum, Shepherd’s purse (Capsella bursa-pastoris), Greater celandine, Atropa belladonna, Hyoscyamus niger, commonly known as henbane, black henbane, or stinking nightshade and Abraham’s tree. Many plants have bactericidal activity, and this property is used in the treatment of inflammatory diseases of the mucous membranes and skin. Such properties are possessed by flowers of Chamomile (Matricaria chamomilla L.), Calendula officinalis, infusion of Medicinal sage (Salvia officinalis L.). For the treatment of functional hyperprolactinemia phytopreparations are also successfully used. It is known that the medicinal plant Vitex agnus castus has dopaminergic properties, selectively blocking prolactin synthesis, and reduces follicle stimulating hormone levels. Strychnos ignatia, Caulophyllum thalictroides, European cyclamen, Lilium tigrinum, Iris versicolor provide a complex effect on the female body, effectively reduces the level of prolactin and the severity of mastalgia, which is confirmed not only by clinical data, but also by mammography data in fibrocystic breast disease. For the treatment of climacteric syndrome, a large number of medicinal plants are used, in particular, the most popular is the Cimicifuga racemose. Conclusion. The effectiveness of phytotherapeutic drugs has been verified by many clinical trials. Modern phytotherapy is becoming more widespread in clinical practice, as an alternative to drug treatment.

Comparative study of the digital mammography data analysis system based on artificial intelligence “Celsus” and radiologists

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Breast Density Awareness and Knowledge in a Mammography Screening Cohort of Predominantly Hispanic Women: Does Breast Density Notification Matter?

New York State law mandates that women with dense breasts receive a written notification of their breast density (BD) and its implications, but data on the impact of dense breast notification (DBN) on BD awareness and knowledge in diverse populations remain limited. Between 2016 and 2018, we collected survey and mammographic data from 666 women undergoing screening mammography in New York City (ages 40-60, 80% Hispanic, 69% Spanish-speaking) to examine the impact of prior DBN on BD awareness by sociodemographic and breast cancer risk factors, and describe BD knowledge by sources of information. Only 24.8% of the overall sample and 34.9% of women receiving DBN had BD awareness. In multivariable models adjusting for DBN, awareness was significantly lower in women who were Spanish-speaking [OR, 0.16; 95% confidence interval (CI), 0.09-0.30 vs. English speakers], were foreign-born (OR, 0.31; 95% CI, 0.16-0.58 vs. U.S.-born), and had lower educational attainment (e.g., high school degree or less; OR, 0.14; 95% CI, 0.08-0.26 vs. college or higher degree). Women receiving DBN were more likely to be aware of BD (OR, 2.61; 95% CI, 1.59-4.27) but not more knowledgeable about the impact of BD on breast cancer risk and detection. However, women reporting additional communication about their BD showed greater knowledge in these areas. DBN increases BD awareness disproportionately across sociodemographic groups. Efforts to improve communication of DBN must focus on addressing barriers in lower socioeconomic and racially and ethnically diverse women, including educational and language barriers.

Transition to mammography in the regular computed tomography simulation and reconstruction software

This paper presents a modification of the previously developed and maintained computed tomography simulation and reconstruction software for the mammography case. New additional modules are designed to process the mammography data. Mammography provides incomplete information about the subject taken from the limited number points of view but as a result has potentially minimized exposure to radiation for the biological tissue under study. We implement this method by providing new standalone independent mammography modules in our software package. These modules are responsible for creating the projections’ set according to the operator’s input of exposure angles, phantom’s structure, and other multiple recording parameters. These additional modules reconstruct the data from these generated sets of projections or take the real medical data as input. Contrast and features’ recognition are particularly important elements of the study due to the limited number of projections in set. Our software could be used in combination with any real commercial mammography scanner as well as for research purposes to train medical and physics personnel and study for novel methods of contrast and image enhancement.

Digital Mammography and Breast Tomosynthesis Performance in Women with a Personal History of Breast Cancer, 2007-2016.

Background Since 2007, digital mammography and digital breast tomosynthesis (DBT) replaced screen-film mammography. Whether these technologic advances have improved diagnostic performance has, to the knowledge of the authors, not yet been established. Purpose To evaluate the performance and outcomes of surveillance mammography (digital mammography and DBT) performed from 2007 to 2016 in women with a personal history of breast cancer and compare with data from 1996 to 2007 and the performance of digital mammography screening benchmarks. Materials and Methods In this observational cohort study, five Breast Cancer Surveillance Consortium registries provided prospectively collected mammography data linked with tumor registry and pathologic outcomes. This study identified asymptomatic women with American Joint Committee on Cancer anatomic stages 0-III primary breast cancer who underwent surveillance mammography from 2007 to 2016. The primary outcome was a second breast cancer diagnosis within 1 year of mammography. Performance measures included the recall rate, cancer detection rate, interval cancer rate, positive predictive value of biopsy recommendation, sensitivity, and specificity. Results Among 32 331 women who underwent 117 971 surveillance mammographic examinations (112 269 digital mammographic examinations and 5702 DBT examinations), the mean age at initial diagnosis was 59 years ± 12 (standard deviation). Of 1418 second breast cancers diagnosed, 998 were surveillance-detected cancers and 420 were interval cancers. The recall rate was 8.8% (10 365 of 117 971; 95% CI: 8.6%, 9.0%), the cancer detection rate was 8.5 per 1000 examinations (998 of 117 971; 95% CI: 8.0, 9.0), the interval cancer rate was 3.6 per 1000 examinations (420 of 117 971; 95% CI: 3.2, 3.9), the positive predictive value of biopsy recommendation was 31.0% (998 of 3220; 95% CI: 29.4%, 32.7%), the sensitivity was 70.4% (998 of 1418; 95% CI: 67.9%, 72.7%), and the specificity was 98.1% (114 331 of 116 553; 95% CI: 98.0%, 98.2%). Compared with previously published studies, interval cancer rate was comparable with rates from 1996 to 2007 in women with a personal history of breast cancer and was higher than the published digital mammography screening benchmarks. Conclusion In transitioning from screen-film to digital mammography and digital breast tomosynthesis, surveillance mammography performance demonstrated minimal improvement over time and remained inferior to the performance of screening mammography benchmarks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moy and Gao in this issue.