Screening Mammography Examinations Research Articles

Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.

Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (n = 89 285), validation (n = 2106), and test (n = 39 351) datasets were produced for model development and evaluation. The AI model was trained to predict future cancer occurrence based on screening mammograms and patient age. Performance was evaluated on the test dataset using the area under the receiver operating characteristic curve (AUC) and compared across subpopulations to assess potential biases. Interpretability of the model was explored, including with saliency maps. Results On the hold-out test set, the AI model achieved an overall AUC of 0.70 (95% CI: 0.69, 0.72). There was no evidence of a difference in performance across the three sites, between patient ethnicities, or across age groups. Visualization of saliency maps and sample images provided insights into the mammographic features associated with AI-predicted cancer risk. Conclusion The developed AI tool showed good performance on a multisite, United Kingdom-specific dataset. Keywords: Deep Learning, Artificial Intelligence, Breast Cancer, Screening, Risk Prediction Supplemental material is available for this article. ©RSNA, 2024.

Abstract PO4-20-01: A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA

Abstract Background There have been no sustained efforts at stemming the rising incidence and mortality from breast cancer in sub-Saharan African countries like Nigeria. Mammography is the gold standard for breast cancer screening and is associated with improved survival particularly among women 50 to 69. Reports from various parts of Nigeria show very low mammography screening uptake. Causes include lack of geographical access, financial issues (less than 5% of the population is covered by the National Health Insurance Scheme), lack of awareness and the increasing expertise gap from the continued brain drain, despite the already low number of radiologists in the country. Given these obstacles, our long-term goal is to devise a rational basis for identifying mammographic risk categories so that this resource can be deployed cost-effectively. We propose to carry out a 5-year prospective mammographic study in Nigerian women and to use machine learning to develop a risk prediction algorithm, so that following a single screening mammogram, the appropriate frequency of subsequent mammograms can be determined. Our immediate goal is to examine the feasibility of performing such a study. To this end, we have initiated a cross-sectional pilot study that will recruit up to 2000 women in multiple urban centers in Nigeria. Objective: To explore feasibility of creating a digital mammography image database representative of the radiological features of the breast of Nigerian women. Objective: To explore feasibility of creating a database that can be used to inform a predictive mammographic risk model for breast cancer and identify the appropriate population that will benefit from breast cancer screening in Nigeria. Design This is a prospective, open-label, cross-sectional pilot study in 2000 eligible women in Nigeria and will include a single screening mammography examination. The mammography images will be read by the study radiologist. The Pilot phase will determine the feasibility and aid in the design of the of the Extension phase in which up to 20,000 participants will undergo yearly mammography examination for 5 years. Incident cases of breast cancer during the study period will permit the development of deep learning algorithms for predicting the risk of breast cancer in this population. Main Eligibility Criteria: Inclusion Criteria All consecutive women aged ≥40 years and < 65 years presenting for screening mammography at the participating institutions will be eligible. Signed study-specific written informed consent. Exclusion Criteria Complaint of a focal dominant lump or a bloody or clear nipple discharge. History of breast implants. Any woman who is pregnant or has reason to believe that she might be pregnant. Statistical Methods Analyses of continuous data will be summarized using descriptive statistics where the following parameters will be reported: Number of observations, Number of missing observations, Mean, Median, Standard deviation (SD), Minimum (Min), Maximum (Max) Categorical data will be presented with absolute and relative frequency (n and %). Present Accrual and Target accrual Two hundred ninety-six women have been enrolled and screening digital mammographic images using GE Digital mammography equipment have been obtained on all participants at 2 study sites in 2 cities in Southern Nigeria. The target accrual is 2000 participants over 12 months. Conclusion Our preliminary experience to date suggests the feasibility of proceeding with a larger prospective study to define mammographic risk groups in Nigerian women which will permit the rational allocation of resources for breast cancer prevention activities in this underserved population. Contact Information: Dr Toyin Shonukan at tshonukan@yemanjacancercare.org Citation Format: Oluwatoyin Shonukan, Hyelakumi Ibrahim, Esther Udoh, Wahab Egbeolu, Kanyinsola Oyeyinka, Jonas Haggstrom. A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-20-01.

Breast Cancer Screening: Can We Justify Deescalation?

Novel breast cancer screening methods that detect greater numbers of occult (nonpalpable) tumors have been rapidly incorporated into clinical practice, with the aim of reducing mortality. Yet, tumor detection has never been validated as a proper surrogate outcome measure for breast cancer mortality. Moreover, the detection of greater numbers of occult cancers increases the risk of overdiagnosis, which refers to detection of tumors that pose no threat to life and would never have been detected in the absence of screening. With recent advances in breast cancer therapy, many cancers that were previously curable only if detected as occult tumors with mammography screening are perhaps now curable even when detected as small palpable tumors, thereby giving us an opportunity to deescalate screening and mitigate the risk of overdiagnosis. Thus, a randomized trial comparing screening mammography versus screening clinical breast examination (CBE), with breast cancer mortality as the endpoint, is now warranted. In such a trial, hand-held ultrasound might aid in the interpretation of screening CBE findings. In conclusion, recent improvements in breast cancer therapy provide the justification to assess the deescalation of breast cancer screening. See related article by Farber et al., p. 671.

A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and P values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (noninferiority margin of 0.25 cancers per 1000 examinations: U.S. dataset 1, P = .02; U.S. dataset 2, P < .001; U.K. dataset, P < .001). On U.S. dataset 1 (11 592 mammograms; 101 cancers; 3810 female patients; mean age, 57.3 years ± 10.0 [SD]), the device reduced screening examinations requiring radiologist interpretation by 41.6% (95% CI: 40.6%, 42.4%; P < .001), diagnostic examinations callbacks by 31.1% (95% CI: 28.7%, 33.4%; P < .001), and benign needle biopsies by 7.4% (95% CI: 4.1%, 12.4%; P < .001). U.S. dataset 2 (1362 mammograms; 330 cancers; 1293 female patients; mean age, 55.4 years ± 10.5) was reduced by 19.5% (95% CI: 16.9%, 22.1%; P < .001), 11.9% (95% CI: 8.6%, 15.7%; P < .001), and 6.5% (95% CI: 0.0%, 19.0%; P = .08), respectively. The U.K. dataset (1877 mammograms; 595 cancers; 1491 female patients; mean age, 63.5 years ± 7.1) was reduced by 36.8% (95% CI: 34.4%, 39.7%; P < .001), 17.1% (95% CI: 5.9%, 30.1%: P < .001), and 5.9% (95% CI: 2.9%, 11.5%; P < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. Keywords: Artificial Intelligence, Semiautonomous Deep Learning, Breast Cancer, Screening Mammography Supplemental material is available for this article. Published under a CC BY 4.0 license.

Interval breast cancer: radiological surveillance in screening Egyptian population

BackgroundInterval cancers might be divided into true negative interval cancer where a new lesion is detected that no sign of disease could be detected on previous screening mammogram. For false-negative interval cancers, those missed for overt symptoms and those missed for mild or undetectable ones, this includes interpretive error as benign interval cancer (benign mimics), subtle changes, masked carcinoma or slowly growing or patient factors, such as the dense breast parenchyma. Technical failure interval cancer hampered the reader to discover the abnormality. The aim of this study was to relate the risk factors for the development of the interval breast cancer such as breast density, positive family history of breast cancer in Egyptian population. Highlight the causes of missed breast cancer in order to overcome it in the future radiological interpretation.MethodsA total of 74,546 screening mammographic examinations were performed in the radiology department at specialized breast cancer center in the period between 2015 and 2021 with about 844 pathologically proved malignant cases. Out of the 844 pathologically proven breast cancer cases, 185 breast cancer patients were interval breast cancer having reported normal examination in the previous year, 88 were true interval breast cancer and 97 were missed on mammography and detected later on. The cases were subjected to full-field digital mammogram (FFDM), complementary ultrasound, contrast-enhanced digital mammography (CEDM) and magnetic resonance imaging (MRI) in some cases, and all cases were histopathologically proven by either fine needle aspiration, core needle biopsy (CNB) or open biopsy.ResultsThe mean age of the patients was 53.5 years (range 36–83 years). The overall breast cancer detection rate was 11 per 1000 women. The 185 interval cancers were present at a rate of 3.8 per 1000 women. True negative interval breast cancers where 88 cancers were not present in retrospect on screening mammograms, 17 cases present with benign findings (benign mimics mass or calcifications) and 80 cancers were missed cancers. Analysis of the cause of missed carcinoma revealed patient-related, tumor-related, or provider-related factors. Tumor factors were the most commonly encountered, accounting for 49.5% (48 cases), then provider factors in 25.8% (25 cases) while the patient factors were the least commonly encountered in 24.7% (24 cases). Recorded positive family history found in about 35% (31cases) of the true interval breast cancers.ConclusionsAlthough mammography is the standard for detecting early breast cancer, some cancers can be missed due to various causes. Mammographic interpretation must meet high standards to reduce missed cancers. Radiologists should carefully assess screening views and order additional imaging if needed. Palpable lesions and clinical data should be further examined with ultrasound and contrast imaging if necessary. Always compare current images with previous examinations to check for any changes in lesion size. When one pathology is found, search for additional lesions.

Patient Preferences of Breast Radiologists’ Attire and Appearance

To assess patient preferences for breast radiologists' attire and appearance. A multi-institutional anonymous, voluntary 19-question survey was administered to patients undergoing screening and diagnostic mammography examinations over a 5-week period. Using a 5-point Likert scale, respondents were asked about their preferences for gender-neutral attire (white coat), male-presenting attire (scrubs, dress shirt with tie, or dress shirt without tie), and female-presenting attire (scrubs, dress, blouse with pants, and blouse with skirt). Patient responses were compared todemographic data using bivariable analysis and multivariable regression. Response rate was 84.7% (957/1130). Mean respondent age was 57.2 years±11.9. Most respondents agreed/strongly agreed that the breast radiologist's appearance mattered (52.5%, 502/956) followed by being indifferent (28.1%, 269/956). Respondents with greater education levels felt less strongly (p=0.001) about the radiologist's appearance: 63.3% (70/110) less than college cared about appearance compared to 53.5% (266/497) college/vocational and 47.4% (165/348) graduate. Most respondents felt indifferent about a breast radiologist wearing a white coat (68.9%, 657/954) or about male-presenting breast radiologists wearing atie (77.1%, 734/952) without significant demographic differences. Almost all respondents either prefer/strongly prefer (60.1%, 572/951) or were indifferent (39.6%, 377/951) to all breast radiologists wearing scrubs when performing procedures. While respondents approved of all attire choices overall, most respondents preferred scrubs for both male- and female-presenting breast radiologists (64.0%, 612/957 and 64.9%, 621/957, respectively). A variety of breast radiologists' attire can be worn while maintaining provider professionalism and without compromising patient expectations.

Characteristics of breast cancers detected by screening mammography in Taiwan: a single institute’s experience

Background/aimBreast cancer is the most common female malignancy in the world. Nearly ninety percent of screening-detected breast cancers were diagnosed with earlier stages of 0 to II in Taiwan. It’s widely acknowledged that mammography screening of breast cancer can achieve the goal of early diagnosis and treatment in terms of preventive task while neglected interval cancers are associated with unfavorable tumor characteristics and worse outcomes. The purpose of this study was to explore the characteristics of screening-detected breast cancers in Taiwan.Materials and methodsBoth screening and diagnostic mammography examinations with accompanied BI-RADS (breast imaging-reporting and data system) classification were extracted from the health information system and linked to cancer registry in Taiwan. Enrolled population included those attending their first mammography between 2012 and 2016, excluding subjects with previous breast cancer, or with missing or incomplete data. We compared treatment outcomes between breast cancers with either initial screening or diagnostic mammography (control group), and investigated the compositions of breast cancers detected by screening mammography through direct chart reviews.ResultsA total of 84,246 screening and 61,230 diagnostic mammography sessions were performed from 2010 to 2020. More positive results (BI-RADS 0, 3, 4 and 5) were observed for those attending the first diagnostic than the first screening mammography (43.58% versus 16.12%, p < 0.001). Earlier stages (0 and I) distribution (92% versus 81%, p < 0.0001), better survivorship (overall survival: 96.91% versus 92.17%, p = 0.007) and a lower HER2 (human epidermal growth factor receptor II) positive status and lower tumor grade were noted in breast cancers with initial screening rather than diagnostic mammography. Among 26,103 mammography screening invitees between 2012 and 2016, 325 breast cancers were ascertained from cancer registry. Of these, 234 had one, 72 had two and 19 had three episodes of mammography before cancer diagnosis. Extensive chart reviews revealed that women with and without breast symptoms constituted 29.9 and 70.1% of the 325 screening-detected breast cancers, with the latter further divided into false negative results (interval cancer), diagnosed at the first mammography, diagnostic at the secondary or subsequent mammography and those with a delayed biopsy or confirmatory imaging constituted (5.2, 47, 10.5 and 7.4%).ConclusionScreening-detected breast cancers were a mixture of women with and without symptoms, those with a false negative result, true negative results with cancer detected at subsequent mammography and non-adherers. Despite this, efficacy of mammography screening was ascertained in Taiwan from this study. To further enhance earlier detection and decrease false negativity, the impact of repeated mammography, and additional sonography for symptomatic women, compliance following a positive screening mammography should not be overemphasized.

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

Comparison of Outcomes for One-View Asymmetries Recalled From Digital Breast Tomosynthesis Versus Full-Field Digital Mammography Screening Examinations.

BACKGROUND. Recall rates are lower for digital breast tomosynthesis (DBT) than for full-field digital mammography (FFDM). This difference could have important implications with respect to one-view asymmetries given that missed cancers are often visible on one view. OBJECTIVE. The purpose of this study is to compare the outcomes of one-view asymmetries recalled from DBT versus FFDM screening examinations and to determine predictors of malignancy among recalled asymmetries. METHODS. This retrospective study first determined recall rates associated with one-view asymmetries for screening mammography performed using DBT and FFDM from July 14, 2016, through July 14, 2020. Further analyses included patients recalled for a one-view asymmetry who completed subsequent diagnostic workup and all recommended follow-up. Patient and cancer characteristics were extracted from the electronic health record. RESULTS. The recall rate associated with asymmetries was lower for DBT screening (2.5% [3169/128,755]) than for FFDM screening (3.4% [815/23,898]) (p < .001). Further analyses of patients who completed diagnostic workup and subsequent follow-up included 3119 patients (mean age, 57 years) for DBT screening and 811 patients (mean age, 56 years) for FFDM screening. Distribution of final BI-RADS categories from subsequent diagnostic workup was not different between the two modalities (p > .99). The frequency of malignancy was not different between asymmetries recalled from DBT (1.7% [54/3119]) and FFDM (1.7% [14/811]) (p > .99). Malignant asymmetries identified on FFDM versus DBT were more frequently associated with architectural distortion on diagnostic workup (35.7% [5/14] vs 9.3% [5/54]) (p < .001) and were more commonly invasive ductal carcinoma (92.9% vs 57.4%) and less commonly invasive lobular carcinoma (0.0% vs 24.1%) (p = .05). In multivariable analysis, independent predictors of malignancy among recalled asymmetries from DBT were age (for 55-69 years, odds ratio [OR] = 2.40 [p = .04]; for ≥ 70 years, OR = 7.93 [p < .001]; reference, < 55 years) and breast density (not dense, OR = 2.47 [p = .001]; reference, dense breasts). CONCLUSION. Recalled asymmetries were less frequent for DBT than for FFDM. The malignancy rate was low for both modalities (1.7%). Age 55 years old and older and lower breast density predicted malignancy for DBT-recalled asymmetries. CLINICAL IMPACT. Our results support the use of DBT to reduce unnecessary recalls without altering PPV for asymmetry-associated malignancies. Patient factors should be considered when assessing whether a potential asymmetry on DBT screening represents overlapping fibroglandular tissue or a suspicious finding that requires diagnostic workup.

Manual or auto-mode: Does this affect radiation dose in digital mammography without compromising image quality?

IntroductionIn current practice, auto-filter exposure mode is used for most screening mammography examinations. However, with better understanding of the side effects of radiation, it is important to examine exposure parameters and practises to minimise radiation dose to patients. The purpose of this phantom study is to investigate the impact that different exposure modes (manual, auto-time and auto-filter) have on radiation dose, while maintaining images of diagnostic quality. MethodsThis study was conducted in three stages. In the first stage, 125 images were taken using a Gammex 156 phantom with polymethyl methacrylate blocks to reflect varying thicknesses (4.5, 5.0, 5.5, 6.0 and 6.5 cm). In the second stage, three mammographers independently assessed image quality and assigned scores based on the number of distinct fibers, masses and speck groups visible. Images with acceptable quality were further investigated in the third stage by comparing their average glandular dose (AGD) using the Kruskal–Wallis H test and Dunn's post-hoc pairwise analysis. ResultsSignificant differences in AGD were shown between the auto-filter mode and manual mode techniques for 6.0 cm, and between auto-time mode and manual mode techniques for 6.5 cm (p < 0.05). ConclusionFor 4.5, 5.0 and 5.5 cm phantoms, as AGD was not significantly different among the different modes, the auto-filter may remain the most practical option. However, significant reductions in AGD were obtained for the 6.0 and 6.5 cm phantoms when manual mode techniques were used. Implications for practiceManual mode techniques can potentially provide dose-saving opportunity in 6.0 and 6.5 cm breast thickness though future work on human breast should be done to confirm this. Results from this study will support future research based on patient data.

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.

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

Reducing False-Positive Biopsies using Deep Neural Networks that Utilize both Local and Global Image Context of Screening Mammograms.

Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false-positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography examinations from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.

Artificial Intelligence Support for Mammography: In-Practice Clinical Experience.

Mammographic results can be delayed for many reasons, including physician shortages. In our practice, most delays occur when patients fail to bring their prior outside studies when they present for mammography. In those instances, screening mammographic examinations were held for several weeks until prior studies were received or were ultimately interpreted without comparison to satisfy Mammography Quality Standards Act requirements that reports and lay letters to patients be provided within 30 days of the acquisition of a mammogram [ 1 US Food and Drug AdministrationThe Mammography Quality Standards Act final regulations: modifications and additions to policy guidance help system #13. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/mammography-quality-standards-act-final-regulations-modifications-and-additions-policy-guidance-help-0Date accessed: October 3, 2021 Google Scholar ]. Many women experience anxiety waiting for their mammographic results, with 97% of women in one study reporting that immediate results would lower anxiety [ 2 Lee J. Hardest L.A. Kunzler N.M. et al. Direct interactive public education by breast radiologists about screening mammography: impact on anxiety and empowerment. J Am Coll Radiol. 2016; 13: 12-20 Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar ]. We improved our mammographic interpretation process through use of an artificial intelligence (AI)–based computer-aided detection (CAD) and triage software suite and share here our 2-year experience.

Adherence to guidance for prioritizing higher risk groups for breast cancer screening during the COVID-19 pandemic in the Ontario Breast Screening Program: a descriptive study.

Background:Breast cancer screening in Ontario, Canada, was deferred during the first wave of the COVID-19 pandemic, and a prioritization framework to resume services according to breast cancer risk was developed. The purpose of this study was to assess the impact of the pandemic within the Ontario Breast Screening Program (OBSP) by comparing total volumes of screening mammographic examinations and volumes of screening mammographic examinations with abnormal results before and during the pandemic, and to assess backlogs on the basis of adherence to the prioritization framework.Methods:A descriptive study was conducted among women aged 50 to 74 years at average risk and women aged 30 to 69 years at high risk, who participated in the OBSP. Percentage change was calculated by comparing observed monthly volumes of mammographic examinations from March 2020 to March 2021 with 2019 volumes and proportions by risk group. We plotted estimates of backlog volumes of mammographic examinations by risk group, comparing pandemic with prepandemic screening practices. Volumes of mammographic examinations with abnormal results were plotted by risk group.Results:Volumes of mammographic examinations in the OBSP showed the largest declines in April and May 2020 (> 99% decrease) and returned to prepandemic levels as of March 2021, with an accumulated backlog of 340 876 examinations. As of March 2021, prioritization had reduced the backlog volumes of screens for participants at high risk for breast cancer by 96.5% (186 v. 5469 expected) and annual rescreens for participants at average risk for breast cancer by 13.5% (62 432 v. 72 202 expected); there was a minimal decline for initial screens. Conversely, the backlog increased by 7.6% for biennial rescreens (221 674 v. 206 079 expected). More than half (59.4%) of mammographic examinations with abnormal results were for participants in the higher risk groups.Interpretation:Prioritizing screening for those at higher risk for breast cancer may increase diagnostic yield and redirect resources to minimize potential long-term harms caused by the pandemic. This further supports the clinical utility of risk-stratified cancer screening.

Disparities in Same-Day Diagnostic Imaging in Breast Cancer Screening: Impact of an Immediate-Read Screening Mammography Program Implemented During the COVID-19 Pandemic.

BACKGROUND. The need for second visits between screening mammography and diagnostic imaging contributes to disparities in the time to breast cancer diagnosis. During the COVID-19 pandemic, an immediate-read screening mammography program was implemented to reduce patient visits and decrease time to diagnostic imaging. OBJECTIVE. The purpose of this study was to measure the impact of an immediate-read screening program with focus on disparities in same-day diagnostic imaging after abnormal findings are made at screening mammography. METHODS. In May 2020, an immediate-read screening program was implemented whereby a dedicated breast imaging radiologist interpreted all screening mammograms in real time; patients received results before discharge; and efforts were made to perform any recommended diagnostic imaging during the visit (performed by different radiologists). Screening mammographic examinations performed from June 1, 2019, through October 31, 2019 (preimplementation period), and from June 1, 2020, through October 31, 2020 (postimplementation period), were retrospectively identified. Patient characteristics were recorded from the electronic medical record. Multivariable logistic regression models incorporating patient age, race and ethnicity, language, and insurance type were estimated to identify factors associated with same-day diagnostic imaging. Screening metrics were compared between periods. RESULTS. A total of 8222 preimplementation and 7235 postimplementation screening examinations were included; 521 patients had abnormal screening findings before implementation, and 359 after implementation. Before implementation, 14.8% of patients underwent same-day diagnostic imaging after abnormal screening mammograms. This percentage increased to 60.7% after implementation. Before implementation, patients who identified their race as other than White had significantly lower odds than patients who identified their race as White of undergoing same-day diagnostic imaging after receiving abnormal screening results (adjusted odds ratio, 0.30; 95% CI, 0.10-0.86; p = .03). After implementation, the odds of same-day diagnostic imaging were not significantly different between patients of other races and White patients (adjusted odds ratio, 0.92; 95% CI, 0.50-1.71; p = .80). After implementation, there was no significant difference in race and ethnicity between patients who underwent and those who did not undergo same-day diagnostic imaging after receiving abnormal results of screening mammography (p > .05). The rate of abnormal interpretation was significantly lower after than it was before implementation (5.0% vs 6.3%; p < .001). Cancer detection rate and PPV1 (PPV based on positive findings at screening examination) were not significantly different before and after implementation (p > .05). CONCLUSION. Implementation of the immediate-read screening mammography program reduced prior racial and ethnic disparities in same-day diagnostic imaging after abnormal screening mammograms. CLINICAL IMPACT. An immediate-read screening program provides a new paradigm for improved screening mammography workflow that allows more rapid diagnostic workup with reduced disparities in care.

Mammographic Breast Densities among 200 Nigerian Females in Sokoto, North Western Nigeria

Background: Breast density has been reported to be an independent predictor of breast cancer risk. Women with highest mammographic breast density may have four to six fold increased risk of developing cancer. Aim and Objective: This study is aimed at evaluating the various forms of breast densities following screening and diagnostic mammographic examination. Materials and Methods: From December 2010 and November 2012, 200 females had mammographic breast examination. Seventy-seven (77) females had routine screening mammography while 123 had diagnostic mammographic examination in the department with the General Electric (GE) Alpha-RT machine with model number MGF-101(manufactured 2010). All the subjects had to fill a mammographic form consisting of variables like age, sex, occupation, family history of breast cancer, tribe, contraception, parity, and caffeine consumption, history of surgical intervention (lumpectomy, biopsy and/or mastectomy), previous mammography and last child birth. MLO and CC views were done for the breast examination though additional views were occasionally employed. Results: Breast density decreases with increase in age of the female subjects and breast lesions are better detected following mammography on fatty or less dense breast.

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

ObjectivesTo evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance.MethodsA total of 2257 full-field digital mammography screening examinations, obtained 2011–2013, of women aged 50–69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0–95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers.ResultsUsing a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1–28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1–8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5–4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8–25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0–3.5%).ConclusionThe application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice.Key Points• Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss.• Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives.• About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.

Digital Mammography Device for Mobile Mammography Complex

The results of the development of a mammographic unit with a digital flat-panel detector designed for for installation in a mobile medical X-ray room are presented.The mammographic unit with a digital flat-panel detector is designed for screening and other specialized mammographic examinations, provides the ability to perform full-format raster (using a screening raster) mammography, X-ray of the armpits.

Evaluation of Radiation Doses and Estimation of the Risk of Radiation-Induced carcinogenesis in Women Undergoing Screening Mammography Examinations

Digital Mammography is used as a screening tool to discover breast cancer at an early stage, the benefits and harms of this techniques is under scrutiny hence and Moroccan regulations governing radiation protection of patients have been strengthened, the need to investigate the dose received during screening mammography and the risk associated. This study is consisted of examining 126 mammography projections, for 63 women. All examinations were performed with a full digital mammography machine, technical and exposure parameters were recorded, statistical analysis was carried out using Microsoft Excel in order to calculate local DRLs and compare them with international standards. Cancer risk has been estimated using BEIR VII report methods. The mean glandular dose MGD was 1,09±0, 45 mGy and 1,26±0,74 mGy for craniocaudal (CC) and mediolateral oblique (MLO) respectively, DRLs were 1,34 for CC view and 1,36 for MLO view. Of the 100,000 women exposed, Lifetime Attributable Risk of cancer incidence has been found to be 0,76 for CC examination, 0.88 for MLO, and 1,64 for the full mammography protocol. Established local DRLs in this study are lower compared to that of United Kingdom and France and higher compared to that of Nigeria and Australia. A potential risk of radiation-induced carcinogenesis exists, and there is a need for optimization of screening mammography practices.