Breast Cancer Screening Performance Research Articles

Breast density effect on the sensitivity of digital screening mammography in a UK cohort.

To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort. Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold. Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5. The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations. In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category. Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories 'a' to 'd'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.

Association of breast cancer with quantitative mammographic density measures for women receiving contrast-enhanced mammography.

Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer. Sixty-six patients with newly diagnosed unilateral breast cancer were frequency matched on the basis of age to 133 cancer-free control individuals. On low-energy craniocaudal contrast-enhanced mammograms (equivalent to standard mammograms), we measured quantitative mammographic density using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus," "Cirrocumulus"). The measures were standardized to enable estimation of odds ratio per adjusted standard deviation (OPERA). In multivariable logistic regression of case-control status, only the highest-intensity measure (Cirrocumulus) was statistically significantly associated with breast cancer (OPERA = 1.40, 95% confidence interval = 1.04 to 1.89). Conventional Cumulus did not contribute to model fit. For women receiving contrast-enhanced mammography, Cirrocumulus mammographic density may better predict breast cancer than conventional quantitative mammographic density.

Multi-Pronged Solutions Addressing Barriers to Breast Screening Access in Appalachia

&lt;i&gt;Annual mammography screening is recommended by the US Preventive Services Task Force (USPSTF) and American Cancer Society (ACS) to reduce mortality through early detection of breast cancer. In rural Appalachia, rates of later-stage breast cancer incidence and mortality are higher than national averages. We explored the ways that providers and staff at breast cancer screening facilities employed novel approaches to overcome patient- and facility-level barriers to access to breast cancer screening in the Appalachian region. We conducted 23 semi-structured interviews with 28 clinical providers and staff of breast health facilities in Appalachia. Themes reflect how limiting features of breast screening facilities influenced access to care; the way patient-level barriers presented challenges to access to breast screening; and that external and regulatory forces presented obstacles to access to care. In addition, the unique geographical and geographical attributes of the Appalachian region shaped access and adherence to mammography screening recommendations. Thematic findings highlight that facilities implemented patient-centered strategies to overcome access-related barriers. Results may inform the ways breast cancer screening facilities address suboptimal access to breast health screening. They may also inform future resource allocations to enable facilities to reach breast cancer screening performance goals.&lt;/i&gt;

Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening.

Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55 916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21 929 of 45 444), which reduced to 13.0% (5896 of 45 444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55 916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency. Keywords: Breast, Screening, Mammography, Computer Applications-Detection/Diagnosis, Neoplasms-Primary, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

Convolutional Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening for Vietnamese patients

ABSTRACT Nowadays, breast cancer is one of the leading cancers in Vietnam, and it causes approximately 6000 deaths every year. The rate of breast cancer patients was calculated as 26.4/100000 persons in 2018. There are 21,555 new cases reported in 2020. However, these figures can be reduced with early detection and diagnosis of breast cancer disease in women through mammographic imaging. In many hospitals in Vietnam, there is a lack of experienced breast cancer radiologists. Therefore, it is helpful to develop an intelligent system to improve radiologists’ performance in breast cancer screening for Vietnamese patients. Our research aims to develop a convolutional neural network-based system for classifying breast cancer X-Ray images into three classes of BI-RADS categories as BI-RADS 1 (“normal”), BI-RADS 23 (“benign”) and BI-RADS 045 (“incomplete and malignance”). This classification system is developed based on the convolutional neural network with ResNet 50. The system is trained and tested on a breast cancer image dataset of Vietnamese patients containing 7912 images provided by Hanoi Medical University Hospital radiologists. The system accuracy uses the testing set achieved a macAUC (a macro average of the three AUCs) of 0.754. To validate our model, we performed a reader study with the breast cancer radiologists of the Hanoi Medical University Hospital, reading about 500 random images of the test set. We confirmed the efficacy of our model, which achieved performance comparable to a committee of two radiologists when presented with the same data. Additionally, the system takes only 6 seconds to interpret a breast cancer X-Ray image instead of 450 seconds interpreted by a Vietnamese radiologist. Therefore, our system can be considered as a “second radiologist,” which can improve radiologists’ performance in breast cancer screening for Vietnamese patients.

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.

ObjectiveIn recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis.MethodologyTherefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work.ResultsThe present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified.ConclusionThe present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.

The impact of the COVID-19 pandemic on breast cancer screening and diagnosis in a Brazilian metropolitan area.

To evaluate the performance of breast cancer screening and early diagnosis during the pandemic, compared to the pre-pandemic period.Setting: The public referral centre for screening in Campinas, São Paulo State, Brazil. This is an audit study of performance screening and diagnostic indicators. Two periods were analysed: 2019, the pre-COVID period, and 2020, the COVID period. All women who underwent mammography in these periods were included. Indicators were compared between periods, and the US Breast Cancer Surveillance Consortium benchmarks were used as a reference. A comparison between the periods shows a reduction of 57.4% in screening and 4.4% in diagnosis using mammography. Cancer detection rate per 1000 screening mammograms dropped from 4.62 to 2.83 (p = 0.031), while it increased from 84.43 to 89.36 in diagnosis mammograms (p = 0.701), higher than the reference (34.4, p < 0.001). With regard to diagnosis, the proportion of minimal cancers was reduced (p = 0.005) and was lower than the reference (40.0%, p < 0.001), along with the proportion of node-negative invasive cancers (p < 0.001). The mean size of invasive tumours was similar in the two periods (32.50 mm and 33.40 mm, p = 0.808) but larger than the reference value (16.50 mm, p < 0.001). Recall rate was lower in the COVID period (22.55% vs. 27.37%, p = 0.015). The COVID pandemic caused an overall decrease in breast screening and detection of breast cancer cases, although the reduction in number of diagnosis mammograms performed was minimal. Tumour mean size was large in both periods, the pandemic highlighting a previous profile of detection at an advanced stage.

Clinical Application of Automated Breast Ultrasound

Automatic breast ultrasound (ABUS) has been developed to compensate for the shortcomings of hand-held ultrasound (HHUS) and is mainly used for breast cancer screening purposes in women with dense breasts. Since 2021, ABUS has been covered by the Korean National Health Insurance System. It is important to scan the entire breast on ABUS and to identify the poor-quality images requiring re-scanning. In addition, a general understanding of the unique ABUS display mode, distinguishing benign from malignant lesions, the diagnostic performance of breast cancer screening, and the application of computer-aided detection/diagnosis systems is necessary to use ABUS efficiently. This review explores the acquisition method, image quality, and image characteristics of ABUS to improve general understanding of this procedure and its advantages over HHUS, so that ABUS can be applied efficiently in clinical practice.

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.

Deep adversarial domain adaptation for breast cancer screening from mammograms

The early detection of breast cancer greatly increases the chances that the right decision for a successful treatment plan will be made. Deep learning approaches are used in breast cancer screening and have achieved promising results when a large-scale labeled dataset is available for training. However, they may suffer from a dramatic decrease in performance when annotated data are limited. In this paper, we propose a method called deep adversarial domain adaptation (DADA) to improve the performance of breast cancer screening using mammography. Specifically, our aim is to extract the knowledge from a public dataset (source domain) and transfer the learned knowledge to improve the detection performance on the target dataset (target domain). Because of the different distributions of the source and target domains, the proposed method adopts an adversarial learning technique to perform domain adaptation using the two domains. Specifically, the adversarial procedure is trained by taking advantage of the disagreement of two classifiers. To evaluate the proposed method, the public well-labeled image-level dataset Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) is employed as the source domain. Mammography samples from the West China Hospital were collected to construct our target domain dataset, and the samples are annotated at case-level based on the corresponding pathological reports. The experimental results demonstrate the effectiveness of the proposed method compared with several other state-of-the-art automatic breast cancer screening approaches.

The impact of adjunctive tomosynthesis on screening mammography outcomes in two widely diverse radiology practices.

To determine the effect of adjunctive digital breast tomosynthesis screening on dissimilar mammography practices. We compared the outcomes of breast cancer screening with digital mammography versus digital mammography combined with tomosynthesis in two independent breast imaging practices from June 1, 2015, to May 31, 2016. Institution one was a hospital-based academic practice of breast imaging specialists and institution two was a community-based practice with academic affiliation served by general radiologists. Screening mammography was linked to subsequent diagnostic imaging and pathology. Subject characteristics and performance metrics were compared via t test for continuous variables and the chi-square test for categorical variables. A two-sided z test was performed to test modality differences for assessment and pathology subtype. Of the 54638 women, 54% (n=29295) were from institution one and 55% (n=30013) underwent digital mammography alone. Women undergoing mammography with tomosynthesis were older (60.8years vs 56.9years, P<.001) and had slightly less dense breast composition (P=.001). Performance metrics varied substantially between institutions. At both institutions the biopsy rate, positive predictive value of screening (PPV1 ), and invasive cancer detection rate increased significantly with adjunctive tomosynthesis. At institution one, the biopsy rate increased from 1.4% to 1.9%, the PPV1 from 6.0% to 8.2%, and the invasive cancer detection rate from 3.4 to 4.9/1000 women screened. At institution two, the respective increases were from 0.7% to 1.0%, 5.5% to 11.0%, and 2.3% to 4.1/1000. Tomosynthesis recalled asymmetry less and mass more and resulted in fewer BI-RADS 1 and 2 assessments than screening with mammography alone. Adjunctive tomosynthesis appears to have a consistent impact on breast cancer screening performance metrics despite marked variation in breast imaging practice. Combined tomosynthesis screening has a significantly higher PPV1 , leads to a greater number of biopsies, and detects more invasive cancer than screening with digital mammography.

Assessment of Radiologist Performance in Breast Cancer Screening Using Digital Breast Tomosynthesis vs Digital Mammography

Many US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain. To evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations. This cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251 384 DBT and 2 000 681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019. Digital breast tomosynthesis and DM screening examinations. Recall rate and cancer detection rate. A total of 198 radiologists interpreted 2 252 065 DM and DBT examinations (2 000 681 [88.8%] DM examinations; 251 384 [11.2%] DBT examinations; 710 934 patients [31.6%] aged 50-59 years; 1 448 981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33). In this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200 000 exams (over 1 000 000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network’s novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network’s performance on different subpopulations of the screening population, the model’s design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.

Utility of Supplemental Training to Improve Radiologist Performance in Breast Cancer Screening: A Literature Review

PurposeThe authors evaluate whether supplemental training for radiologists improves their breast screening performance and how this is measured. MethodsA systematic search was conducted in PubMed on August 3, 2017. Articles were included if they described supplemental training for radiologists reading mammograms to improve their breast screening performance and at least one outcome measure was reported. Study quality was assessed using the Medical Education Research Study Quality Instrument. ResultsOf 2,199 identified articles, 18 were included, of which 17 showed improvement on at least one of the outcome measures, for at least one training activity or subgroup. Two measurement approaches were found. For the first approach, measuring performance on test sets, sensitivity, and specificity were the most reported outcomes (8 of 11 studies). Recall rate is the most reported outcome (6 of 7 studies) for the second approach, which measures performance in actual screening practice. The studies were mainly of moderate quality (Medical Education Research Study Quality Instrument score 11.7 ± 1.7), caused by small sample sizes and the lack of a control group. ConclusionsSupplemental training helps radiologists improve their screening performance, despite the mainly moderate quality of the studies. There is a need for better designed studies. Future studies should focus on performance in actual screening practice and should look for methods to isolate the training effect. If test sets are used, focus should be on knowledge about correlation between performance on test sets and actual screening practice.

Performance of breast cancer screening using digital breast tomosynthesis: results from the prospective population-based Oslo Tomosynthesis Screening Trial.

Digital breast tomosynthesis (DBT) has the potential to overcome limitations of conventional mammography. This study investigated the effects of addition of DBT on interval and detected cancers in population-based screening. Oslo Tomosynthesis Screening Trial (OTST) was a prospective, independent double-reading trial inviting women 50-69years biennially, comparing full-field digital mammography (FFDM) plus DBT with FFDM alone. Performance indicators and characteristics of screen-detected and interval cancers were compared with two previous FFDM rounds. 24,301 consenting women underwent FFDM+DBT screening over a 2-year period. Results were compared with 59,877 FFDM examinations during prior rounds. Addition of DBT resulted in a non-significant increase in sensitivity (76.2%, 378/496, vs. 80.8%, 227/281, p=0.151) and a significant increase in specificity (96.4%, 57229/59381 vs. 97.5%, 23427/24020, p<.001). Number of recalls per screen-detected cancer decreased from 6.7 (2530/378) to 3.6 (820/227) with DBT (p<.001). Cancer detection per 1000 women screened increased (6.3, 378/59877, vs. 9.3, 227/24301, p<.001). Interval cancer rate per 1000 screens for FFDM+DBT remained similar to previous FFDM rounds (2.1, 51/24301 vs. 2.0, 118/59877, p=0.734). Interval cancers post-DBT were comparable to prior rounds but significantly different in size, grade, and node status from cancers detected only using DBT. 39.6% (19/48) of interval cancers had positive nodes compared with only 3.9% (2/51) of additional DBT-only-detected cancers. DBT-supplemented screening resulted in significant increases in screen-detected cancers and specificity. However, no significant change was observed in the rate, size, node status, or grade of interval cancers. ClinicalTrials.gov: NCT01248546.

Women’s Performance of Breast Cancer Screening (Breast Self-Examination, Clinical Breast Exam and Mammography)

Background: Breast cancer is the most common malignancy among women in Saudi Arabia. Despite the availability of early detection methods to diagnose breast cancer, a huge number of women are still unaware about these methods. This study was conducted to identify the attitude of women in Medina toward breast cancer screening methods, including breast self-examination (BSE), clinical breast examination (CBE) and mammography. Methods and Materials: A cross-sectional survey has been conducted on 124 women aged from 39 and older, who attended Taibah Medical Center for cancer screening either by doctor’s recommendation, family or friends advice, or by herself. Face to face questionnaire was used to collect data. All data were analyzed by statistical analysis system software. Results: The results showed only 35.5%, 27.4% and 37.8% of participants reported that they practiced BSE, CBE and annual mammography, respectively. Only 27.3% of women practiced BSE once per month, and 8.8% visited doctor annually for CBE. Both educational level (p = 0.004) and family history (p = 0.01) were significantly related to BSE. 57.7% of the participants who had positive family history practiced BSE, and 56.0% of the participants who practiced BSE were highly educated. Lacking awareness about BSE is the most important barrier in not practicing BSE, while not having a breast lump was the reason for not undergoing either clinical breast examination (38.7%) or periodic mammography (54.9%). Conclusion: This study emphasized the need for massive health education program to increase awareness, and improve the attitude of women toward breast cancer screening methods.

Factors associated with breast screening radiologists' annual mammogram reading volume in Italy.

Screening mammogram reading volume (SMRV) and total (screening and clinical) mammogram reading volume (TMRV) per year are strongly associated with the radiologist's diagnostic performance in breast cancer screening. The current article reports the prevalence and correlates of a SMRV and a TMRV ≥5000 among Italian breast screening radiologists. A questionnaire survey was carried out in 2013-2014 by the Italian Group for Mammography Screening (GISMa). The questionnaire included items of information for radiologist's experience-related characteristics and for facility-level factors supposedly associated with SMRV and TMRV. Multivariate analysis was performed using backward stepwise multiple logistic regression models. Data for 235 radiologists from 51 local screening programmes were received. Of the 222 radiologists who were eligible, 133 (59.9%) reported a SMRV ≥5000 and 163 (73.4%) a TMRV ≥5000. Multivariate factors positively associated with both characteristics included: the number of years of experience reading mammograms; the percentage of total working time dedicated to breast imaging and breast care; the participation in diagnostic assessment; and the availability of digital tomosynthesis at facility. Full-time dedication to breast imaging and breast care was associated with the highest odds ratio for a SMRV and a TMRV ≥5000, i.e. 11.80 and 46.74, respectively, versus a percentage of time ≤50%. An early (<2000) year of implementation of the screening programme and the availability of vacuum-assisted biopsy at facility were associated with a SMRV and, respectively, a TMRV ≥5000. Increasing the proportion of radiologists with full-time dedication to breast imaging and breast care qualified as the most effective approach to improve SMRV and TMRV.

Women’s Performance of Breast Cancer Screening (Breast Self- Examination, Clinical Breast Exam and Mammography)

Background: Breast cancer is the most common malignancy among women in Saudi Arabia. Despite the availability of early detection methods to diagnose breast cancer, a huge number of women are still unaware about these methods. This study was conducted to identify the attitude of women in Medina toward breast cancer screening methods, including breast self-examination (BSE), clinical breast examination (CBE) and mammography. Materials and methods: A cross sectional survey has been conducted on 124 women aged from 39 and older, who attended Taibah Medical Center for cancer screening either by doctor’s recommendation, family or friends advice, or by herself. Face to face questionnaire was used to collect data. All data were analyzed by statistical analysis system software. Results: The results showed only 35.5%, 27.4% and 37.8% of participants reported that they practiced BSE, CBE and annual mammography, respectively. Only 27.3% of women practiced BSE once per month, and 8.8% visited doctor annually for CBE. Both educational level and family history were significantly related to BSE. 57.7% of the participants who had positive family history practiced BSE, and 56.0% of the participants who practiced BSE were highly educated. Lacking awareness about BSE is the most important barrier in not practicing BSE, while not having a breast lump was the reason for not undergoing either clinical breast examination (38.7%) or periodic mammography (54.9%). Conclusion: This study emphasized the need for massive health education program to increase awareness, and improve the attitude of women toward breast cancer screening methods.

Predictors of Breast Cancer Screening Performance UsingMammography in Korean Women

Predictors of Breast Cancer Screening Performance UsingMammography in Korean Women

Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms.

Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.