Effect of observing change from comparison mammograms on performance of screening mammography in a large community-based population

Does the availability of prior mammograms improve radiologists' observer performance?-a scoping review

Objective

The objective of this review was to examine the impact of previous mammogram availability on radiologists' performance from screening populations and experimental studies.

Materials and Methods

A search of the literature was conducted using five databases: MEDLINE, PubMed, Web of Science, ScienceDirect, and CINAHL as well as Google and reference lists of articles. Keywords were combined with "AND" or "OR" or "WITH" and included "prior mammograms, diagnostic performance, initial images, diagnostic efficacy, subsequent images, previous imaging, and radiologist's performance". Studies that assessed the impact of previous mammogram availability on radiologists' performance were reviewed. The Standard for Reporting Diagnostic Accuracy guidelines was used to critically appraise individual sources of evidence.

Results

A total of 15 articles were reviewed. The sample of mammogram cases used across these studies varied from 36 to 1,208,051. Prior mammograms did not affect sensitivity [with priors: 62-86% (mean = 73.3%); without priors: 69.4-87.4% (mean = 75.8%)] and cancer detection rate, but increased specificity [with priors: 72-96% (mean = 87.5%); without priors: 63-87% (mean = 80.5%)] and reduced false-positive rates [with priors: 3.7 to 36% (mean = 19.9%); without priors 13.3-49% (mean = 31.4%)], recall rates [with priors: 3.8-57% (mean = 26.6%); without priors: [4.9%-67.5% (mean = 37.9%)], and abnormal interpretation rate decreased by 4% with priors. Evidence for the associations between the availability of prior mammograms and positive-predictive value, area under the curve (AUC) from the receiver operating characteristic curve (ROC) and localisation ROC AUC, and positive-predictive value of recall is limited and unclear.

Conclusion

Availability of prior mammograms reduces recall rates, false-positive rates, abnormal interpretation rates, and increases specificity without affecting sensitivity and cancer detection rate.

Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography

Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI's potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists' workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care.

Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction

The identification of women at risk for sporadic breast cancer remains a clinical challenge. We hypothesize that the temporal analysis of annual screening mammograms, using a long short-term memory (LSTM) network, could accurately identify women at risk of future breast cancer. Women with an imaging abnormality, which had been biopsy-confirmed to be cancer or benign, who also had antecedent imaging available were included in this case-control study. Sequences of antecedent mammograms were retrospectively collected under HIPAA-approved guidelines. Radiomic and deep-learning-based features were extracted on regions of interest placed posterior to the nipple in antecedent images. These features were input to LSTM recurrent networks to classify whether the future lesion would be malignant or benign. Classification performance was assessed using all available antecedent time-points and using a single antecedent time-point in the task of lesion classification. Classifiers incorporating multiple time-points with LSTM, based either on deep-learning-extracted features or on radiomic features, tended to perform statistically better than chance, whereas those using only a single time-point failed to show improved performance compared to chance, as judged by area under the receiver operating characteristic curves (AUC: 0.63 ± 0.05, 0.65 ± 0.05, 0.52 ± 0.06 and 0.54 ± 0.06, respectively). Lastly, similar classification performance was observed when using features extracted from the affected versus the contralateral breast in predicting future unilateral malignancy (AUC: 0.63 ± 0.05 vs. 0.59 ± 0.06 for deep-learning-extracted features; 0.65 ± 0.05 vs. 0.62 ± 0.06 for radiomic features). The results of this study suggest that the incorporation of temporal information into radiomic analyses may improve the overall classification performance through LSTM, as demonstrated by the improved discrimination of future lesions as malignant or benign. Further, our data suggest that a potential field effect, changes in the breast extending beyond the lesion itself, is present in both the affected and contralateral breasts in antecedent imaging, and, thus, the evaluation of either breast might inform on the future risk of breast cancer.

Identification of factors associated with diagnostic performance variation in reporting of mammograms: A review

OBJECTIVES

This narrative review aims to identify what factors are linked to diagnostic performance variation for those who interpret mammograms. Identification of influential factors has potential to contribute to the optimisation of breast cancer diagnosis. PubMed, ScienceDirect and Google Scholar databases were searched using the following terms: 'Radiology', 'Radiologist', 'Radiographer', 'Radiography', 'Mammography', 'Interpret', 'read', 'observe' 'report', 'screen', 'image', 'performance' and 'characteristics.' Exclusion criteria included articles published prior to 2000 as digital mammography was introduced at this time. Non-English articles language were also excluded. 38 of 2542 studies identified were analysed.

KEY FINDINGS

Influencing factors included, new technology, volume of reads, experience and training, availability of prior images, social networking, fatigue and time-of-day of interpretation. Advancements in breast imaging such as digital breast tomosynthesis and volume of mammograms are primary factors that affect performance as well as tiredness, time-of-day when images are interpreted, stages of training and years of experience. Recent studies emphasised the importance of social networking and knowledge sharing if breast cancer diagnosis is to be optimised.

CONCLUSION

It was demonstrated that data on radiologist performance variability is widely available but there is a paucity of data on radiographers who interpret mammographic images.

IMPLICATIONS FOR PRACTICE

This scarcity of research needs to be addressed in order to optimise radiography-led reporting and set baseline values for diagnostic efficacy.

Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame

BACKGROUND

A concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework.

OBJECTIVE

This study proposes and evaluates an NLP-based algorithm capable of extracting the temporal referrals in written radiology reports, applies it to all the radiology reports generated for 10 years, introduces a graphical representation of imaging reports, and investigates its benefits for clinical and research purposes.

METHODS

In this single-center, university hospital, retrospective study, we developed a convolutional neural network capable of extracting the date of referrals from imaging reports. The model's performance was assessed by calculating precision, recall, and F1-score using an independent test set of 149 reports. Next, the algorithm was applied to our department's radiology reports generated from 2011 to 2021. Finally, the reports and their metadata were represented in a modulable graph.

RESULTS

For extracting the date of referrals, the named-entity recognition (NER) model had a high precision of 0.93, a recall of 0.95, and an F1-score of 0.94. A total of 1,684,635 reports were included in the analysis. Temporal reference was mentioned in 53.3% (656,852/1,684,635), explicitly stated as not available in 21.0% (258,386/1,684,635), and omitted in 25.7% (317,059/1,684,635) of the reports. Imaging records can be visualized in a directed and modulable graph, in which the referring links represent the connecting arrows.

CONCLUSIONS

Automatically extracting the date of referrals from unstructured radiology reports using deep learning NLP algorithms is feasible. Graphs refined the selection of distinct pathology pathways, facilitated the revelation of missing comparisons, and enabled the query of specific referring exam sequences. Further work is needed to evaluate its benefits in clinics, research, and resource planning.

The Need for Medical Artificial Intelligence That Incorporates Prior Images

The use of artificial intelligence (AI) has grown dramatically in the past few years in the United States and worldwide, with more than 300 AI-enabled devices approved by the U.S. Food and Drug Administration (FDA). Most of these AI-enabled applications focus on helping radiologists with detection, triage, and prioritization of tasks by using data from a single point, but clinical practice often encompasses a dynamic scenario wherein physicians make decisions on the basis of longitudinal information. Unfortunately, benchmark data sets incorporating clinical and radiologic data from several points are scarce, and, therefore, the machine learning community has not focused on developing methods and architectures suitable for these tasks. Current AI algorithms are not suited to tackle key image interpretation tasks that require comparisons to previous examinations. Focusing on the curation of data sets and algorithm development that allow for comparisons at different points will be required to advance the range of relevant tasks covered by future AI-enabled FDA-cleared devices.

Outcomes of screening mammography performed prior to fertility treatment in women ages 40-49

OBJECTIVE

There are currently various conflicting recommendations for breast cancer screening with mammography in women between ages 40-49. There are no specific guidelines for breast cancer screening in women of this age group prior to assisted reproductive technology (ART) for the treatment of infertility. The purpose of our study was to evaluate outcomes of screening mammography, specifically ordered for the purpose of pre-fertility treatment clearance in women aged 40-49 years old.

MATERIALS AND METHODS

This was an IRB approved retrospective study of women aged 40-49 presenting for screening mammography prior to ART between January 2010 and October 2018. Clinical history, imaging, and pathology results were gathered from the electronic medical record. Descriptive statistics were performed.

RESULTS

Our study cohort consisted of 118 women with a mean age of 42 years (range 40-49). Sixteen of 118 (14%) women were recalled from screening for additional diagnostic work-up. Five of the 16 (31%) were recommended for biopsy (BI-RADS 4 or 5). One of 5 biopsies yielded a malignant result (PPV 20%). Overall cancer detection rate was 0.85% or 8.5 women per 1000 women screened. The single cancer in this cohort was an ER+ PR+ HER2- invasive ductal carcinoma.

CONCLUSION

Screening mammography in women 40-49 performed prior to initiation of ART may identify asymptomatic breast malignancy. In accordance with ACR and SBI guidelines to screen women of this age group, women of this age group should undergo screening mammography prior to ART.

A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.

Rates of positive lung cancer screening examinations in academic versus community practice

The benefits and harms of lung cancer screening reported in the National Lung Screening Trial (NLST) likely differ from those observed in academic and community settings. High rates of positive screening findings in the NLST led to the development of the Lung CT Screening Reporting and Data System (Lung-RADS) to standardize interpretation and reporting. We conducted a prospective observational study of lung cancer screening data from four lung cancer screening sites in North Carolina to compare prospective use of Lung-RADS in a real-world screened population versus Lung-RADS retrospectively applied to the NLST, and to determine if Lung-RADS assessment use differs in academic versus community settings. We included 4,037 screening examinations from 11/2014 to 12/2018 in academic and community sites and 75,126 NLST LDCT screening exams. On baseline screening exams, the proportion of positive LDCT exams was higher in community versus academic sites or the NLST (17.7% vs. 11.4% and 13.6%, P value <0.01). On subsequent screens, the proportion of positive exams was lowest in the NLST and higher in community and academic sites (5.9% vs. 12.7% and 11.6%, P value <0.01). After adjusting for age, race, sex, and smoking status, patients screened at academic versus community sites were 34% less likely to have a positive screen at baseline [adjusted odds ratio (aOR) =0.66; 95% confidence interval (95% CI): 0.51-0.86] but on subsequent examinations, there was no difference in academic versus community sites (aOR =0.91; 95% CI: 0.58-1.43). Our findings may be due to differences in radiologists' training or experiences or the availability of prior images for comparison.

Risk-Based Screening Mammography for Women Aged <40: Outcomes From the National Mammography Database

OBJECTIVE

There is insufficient large-scale evidence for screening mammography in women <40 years at elevated risk. This study compares risk-based screening of women aged 30 to 39 with risk factors versus women aged 40 to 49 without risk factors in the National Mammography Database (NMD).

METHODS

This retrospective, HIPAA-compliant, institutional review board-exempt study analyzed data from 150 NMD mammography facilities in 31 states. Patients were stratified by 5-year age intervals, availability of prior mammograms, and specific risk factors for breast cancer: family history of breast cancer, personal history of breast cancer, and dense breasts. Four screening performance metrics were calculated for each age and risk group: recall rate (RR), cancer detection rate (CDR), and positive predictive values for biopsy recommended (PPV₂) and biopsy performed (PPV₃).

RESULTS

Data from 5,986,131 screening mammograms performed between January 2008 and December 2015 in 2,647,315 women were evaluated. Overall, mean CDR was 3.69 of 1,000 (95% confidence interval: 3.64-3.74), RR was 9.89% (9.87%-9.92%), PPV₂ was 20.1% (19.9%-20.4%), and PPV₃ was 28.2% (27.0%-28.5%). Women aged 30 to 34 and 35 to 39 had similar CDR, RR, and PPVs, with the presence of the three evaluated risk factors associated with significantly higher CDR. Moreover, compared with a population currently recommended for screening mammography in the United States (aged 40-49 at average risk), incidence screening (at least one prior screening examination) of women aged 30 to 39 with the three evaluated risk factors has similar cancer detection rates and recall rates.

DISCUSSION

Women with one or more of these three specific risk factors likely benefit from screening commencing at age 30 instead of age 40.

Breast microcalcifications: the UK RCR 5-point breast imaging system or BI-RADS; which is the better predictor of malignancy?

OBJECTIVE

In the UK RCR 5-point breast imaging system (UKS), radiologists grade mammograms from 1 to 5 according to suspicion for malignancy, however unlike BI-RADS, no lexicon of descriptors is published. The aim of this study was to determine whether strict categorisation of microcalcifications (MCC) according to BI-RADS was a better predictor of malignancy than the UKS and whether these descriptors could be used within the UKS.

METHODS

A retrospective review of 241 cases, with MCC on mammography, who underwent biopsy was performed. Morphology, distribution, extent, UKS score, BI-RADS category and pathology were recorded. The positive predictive value (PPV) of each classification system for malignancy was calculated.

RESULTS

28.6% were diagnosed with DCIS/IDC. The PPV for malignancy using the UKS was 18.9%, 69.4%, 100% for M3-5 respectively (p < 0.001) and using ΒI-RADS morphology was amorphous: 7.1%, coarse heterogeneous: 33.3%, fine pleomorphic: 48.1% and fine linear/fine linear branching: 85.2% (p < 0.001). The PPV based on distribution was grouped: 14.2%, regional: 32.3%, diffuse: 33.3% and linear/segmental: 77.8% (p < 0.001). Combining all cases of benign-appearing, amorphous and grouped coarse heterogenous and grouped fine pleomorphic MCC gave a PPV of 12.8%. Combining regional, linear or segmental coarse heterogenous and fine pleomorphic and all fine linear/branching MCC resulted in a PPV of 83.3% for malignancy.

CONCLUSION

Combining morphology and distribution of MCC is accurate in malignancy prediction. Use of BI-RADS descriptors could help standardise reporting within the UKS and an algorithm using these within the UKS is proposed. Better prediction would enable more appropriate counselling and help to identify discrepancies.

ADVANCES IN KNOWLEDGE

No guidance exists on scoring of suspicious MCC in the UK breast imaging system. Use of BI-RADS morphologic/distribution descriptors can aid malignancy prediction. Findings other than morphology of MCC are important in malignancy prediction. An algorithm for use by the UK radiologist when evaluating MCC is provided.

Baseline Mammography: What Is It and Why Is It Important? A Cross-Sectional Survey of Women Undergoing Screening Mammography

OBJECTIVES

Studies have shown that having a baseline mammogram, the first screening mammogram, available for comparison at the time of interpreting a subsequent mammogram significantly decreases the potential of a false-positive examination. Our aim was to evaluate knowledge of and perception about the significance of baseline mammograms in those women undergoing screening mammography.

MATERIALS AND METHODS

A cross-sectional prospective survey study was conducted in women without a history of breast cancer presenting for their screening mammogram. Respondents were surveyed anonymously between March and April 2017. The questionnaire was developed by primary care providers and radiologists and pretested for readability and clarity.

RESULTS

In all, 401 women (87% white, 93% educated beyond high school) completed surveys in which 77% of women reported having yearly mammograms, 31% reported having a history of an abnormal mammogram, and 45% had not heard the term baseline mammogram. Of those who had heard the term, the most commonly reported source was their primary care provider (31%). Although 74% chose the correct definition of a baseline mammogram, 67% did not think that a baseline mammogram was important for decreasing associated cost, time, and discomfort due to the number of mammograms incorrectly read as abnormal.

CONCLUSION

In a group of educated women who routinely get mammograms, almost one-half had not heard the term baseline mammogram. Furthermore, most women did not think baseline mammography was important for decreasing associated cost, time, and discomfort due to mammograms incorrectly read as abnormal. This study suggests that efforts to improve women's understanding of baseline mammograms and their importance are warranted, with greatest opportunity for health care providers and radiologists.

Availability of prior mammograms affects incomplete report rates in mobile screening mammography

PURPOSE

Mobile mammography can improve access to screening mammography in rural areas and underserved populations. We evaluated the frequency of incomplete reports in mobile mammography screening and the relationships between prior mammograms and recall rates.

METHODS

The frequency of incomplete mammogram reports, the subgroups of those needing prior comparison mammograms, recalls for additional imaging, and availability of prior mammograms of a mobile screening mammography unit were compared with fixed site mammography from January 1, 2007 through December 31, 2009. All mobile unit mammograms were full field digital mammography (FFDM). Differences between rates of recall, incomplete reports, and availability of prior mammograms were calculated using the Chi-Square statistic.

RESULTS

Of 2640 mobile mammography cases, 21.9% (578) reports were incomplete, versus 15.2% (7653) (p ≤ 0.001) of 50325 fixed site reports. Of incomplete cases, recall for additional imaging occurred among 8.3% (218) of mobile mammography reports versus 11.3% (5708) (p ≤ 0.001) of fixed site reports. Prior mammograms were needed among 13.6% (360) of mobile mammography versus 3.9% (1945) (p ≤ 0.001) of fixed site reports. Mobile mammography recall rate varied with availability of prior mammograms: 16.0% (54) when no prior mammograms, 7.6% (127) when prior mammograms were elsewhere but unavailable and 5.9% (37) when prior FFDM were immediately available (p ≤ 0.001).

CONCLUSIONS

Incomplete reports were more frequent in mobile mammography than the fixed site. The availability of prior comparison mammograms at time of interpretation decreased the rate of incomplete mammogram reports. Recall rates were higher without prior comparison mammograms and lowest when comparison FFDM mammograms were available.

Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks

We investigate the addition of symmetry and temporal context information to a deep convolutional neural network (CNN) with the purpose of detecting malignant soft tissue lesions in mammography. We employ a simple linear mapping that takes the location of a mass candidate and maps it to either the contralateral or prior mammogram, and regions of interest (ROIs) are extracted around each location. Two different architectures are subsequently explored: (1) a fusion model employing two datastreams where both ROIs are fed to the network during training and testing and (2) a stagewise approach where a single ROI CNN is trained on the primary image and subsequently used as a feature extractor for both primary and contralateral or prior ROIs. A "shallow" gradient boosted tree classifier is then trained on the concatenation of these features and used to classify the joint representation. The baseline yielded an AUC of 0.87 with confidence interval [0.853, 0.893]. For the analysis of symmetrical differences, the first architecture where both primary and contralateral patches are presented during training obtained an AUC of 0.895 with confidence interval [0.877, 0.913], and the second architecture where a new classifier is retrained on the concatenation an AUC of 0.88 with confidence interval [0.859, 0.9]. We found a significant difference between the first architecture and the baseline at high specificity with [Formula: see text]. When using the same architectures to analyze temporal change, we yielded an AUC of 0.884 with confidence interval [0.865, 0.902] for the first architecture and an AUC of 0.879 with confidence interval [0.858, 0.898] in the second setting. Although improvements for temporal analysis were consistent, they were not found to be significant. The results show our proposed method is promising and we suspect performance can greatly be improved when more temporal data become available.

The Economic and Social Value of an Image Exchange Network: A Case for the Cloud

As the health care environment continually changes, radiologists look to the ACR's Imaging 3.0^® initiative to guide the search for value. By leveraging new technology, a cloud-based image exchange network could provide secure universal access to prior images, which were previously siloed, to facilitate accurate interpretation, improved outcomes, and reduced costs. The breast imaging department represents a viable starting point given the robust data supporting the benefit of access to prior imaging studies, existing infrastructure for image sharing, and the current workflow reliance on prior images. This concept is scalable not only to the remainder of the radiology department but also to the broader medical record.

Breast cancer screening effect across breast density strata: A case-control study

Breast cancer screening is known to reduce breast cancer mortality. A high breast density may affect this reduction. We assessed the effect of screening on breast cancer mortality in women with dense and fatty breasts separately. Analyses were performed within the Nijmegen (Dutch) screening programme (1975-2008), which invites women (aged 50-74 years) biennially. Performance measures were determined. Furthermore, a case-control study was performed for women having dense and women having fatty breasts. Breast density was assessed visually with a dichotomized Wolfe scale. Breast density data were available for cases. The prevalence of dense breasts among controls was estimated with age-specific rates from the general population. Sensitivity analyses were performed on these estimates. Screening performance was better in the fatty than in the dense group (sensitivity 75.7% vs 57.8%). The mortality reduction appeared to be smaller for women with dense breasts, with an odds ratio (OR) of 0.87 (95% CI 0.52-1.45) in the dense and 0.59 (95% CI 0.44-0.79) in the fatty group. We can conclude that high density results in lower screening performance and appears to be associated with a smaller mortality reduction. Breast density is thus a likely candidate for risk-stratified screening. More research is needed on the association between density and screening harms.

Improving Screening Mammography Outcomes Through Comparison With Multiple Prior Mammograms

OBJECTIVE

The objective of the present study is to evaluate the effect of comparison with multiple prior mammograms on the outcomes of screening mammography relative to comparison with a single prior mammogram.

MATERIALS AND METHODS

We retrospectively analyzed 46,288 consecutive screening mammograms performed at our institution for 22,792 women. We divided these examinations into three groups: those interpreted without comparison with prior mammograms, those interpreted in comparison with one prior examination, and those interpreted in comparison with two or more prior examinations. For each group, we determined the rate of examination recall. We also calculated the positive predictive value of recall (i.e., positive predictive value level 1 [PPV1]) and the cancer detection rate (CDR) for both the group of examinations compared with a single prior mammogram and the group compared with multiple prior mammograms. Generalized estimating equations with the logistic link function were used to determine the relative odds ratio of recall as a function of the number of comparisons, with adjustment made for age as a confounding variable. The Fisher exact test was performed to compare the PPV1 and the CDR in the different cohorts.

RESULTS

The recall rate for mammograms interpreted without comparison with prior examinations was 16.6%, whereas that for mammograms compared with one prior examination was 7.8% and that for mammograms compared with two or more prior examinations was 6.3%. After adjustment was made for age, the odds ratio of recall for the group with multiple prior examinations relative to the group with a single prior examination was 0.864 (95% CI, 0.776-0.962; p = 0.0074). Statistically significant increases in the PPV1 of 0.05 (p = 0.0009) and in the CDR of 2.3 cases per 1000 examinations (p = 0.0481) were also noted for mammograms compared with multiple prior examinations relative to those compared with a single prior examination.

CONCLUSION

Comparison with two or more prior mammograms resulted in a statistically significant reduction in the screening mammography recall rate and increases in the CDR and PPV1 relative to comparison with a single prior mammogram.

Breast cancer screening in England and the United States: a comparison of provision and utilisation

OBJECTIVES

Comparing breast cancer screening across countries within the context of some of the benefits and harms offers the opportunity to improve effectiveness through mutual learning.

METHODS

This paper describes the provision of breast cancer screening in England and the United States. The various recommendations for accessing breast cancer screening in the two countries are set out and the organisation of services including quality assurance, incentives and performance mechanisms considered.

RESULTS

In the United States, younger women are routinely screened; they are less likely to benefit and more likely to be harmed. The utilisation of breast cancer screening amongst eligible women is broadly comparable in the two countries. However, there are differences in technical performance; the reasons for these including radiological reading procedures and cultural factors are explored.

CONCLUSIONS

Despite a well-functioning screening programme, breast cancer mortality and survival in England are poor relative to other countries. Emphasis for American improvement should be on reducing false-positive recall rates, while the English NHS could supplement existing efforts to understand and improve comparatively poor survival and mortality.

Racial differences in false-positive mammogram rates: results from the ACRIN Digital Mammographic Imaging Screening Trial (DMIST)

BACKGROUND

Mammography screening reduces breast cancer mortality, but false-positive tests are common. Few studies have assessed racial differences in false-positive rates.

OBJECTIVES

We compared false-positive mammography rates for black and white women, and the effect of patient and facility characteristics on false positives.

RESEARCH DESIGN AND SUBJECTS

A prospective cohort study. From a sample of the American College of Radiology Imaging Network (ACRIN) Digital Mammographic Imaging Screening Trial (DMIST), we identified black/African American (N=3176) or white (N=26,446) women with no prior breast surgery or breast cancer.

MEASURES

Race, demographics, and breast cancer risk factors were self-reported. Results of initial digital and film mammograms were assessed. False positives were defined as a positive mammogram (Breast Imaging Reporting and Data System category 0, 4, 5) with no cancer diagnosis within 15 months.

RESULTS

The false-positive rate for digital mammograms was 9.2% for black women compared with 7.8% for white women (P=0.009). After adjusting for age, black women had 17% increased odds of false-positive digital mammogram compared with whites (OR=1.17; 95% CI, 1.01-1.35; P=0.033). This association was attenuated after adjusting for patient factors, prior films, and study site (OR=1.04; 95% CI, 0.91-1.20; P=0.561). There was no difference in the occurrence of false positives by race for film mammography.

CONCLUSIONS

Black women had higher frequency of false-positive digital mammograms explained by lack of prior films and study site.The variation in the disparity between the established technique (film) and the new technology (digital) raises the possibility that racial differences in screening quality may be greatest for new technologies.