Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories.
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ABSTRACT:
SUBMITTER: Tagliafico AS
PROVIDER: S-EPMC3830430 | biostudies-other | 2013 Nov
REPOSITORIES: biostudies-other
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Project description:Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.
| S-EPMC6493215 | biostudies-literature
Project description:ImportanceMany 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.ObjectiveTo evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations.Design, setting, and participantsThis 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.ExposuresDigital breast tomosynthesis and DM screening examinations.Main outcomes and measuresRecall rate and cancer detection rate.ResultsA 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).Conclusions and relevanceIn 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.
| S-EPMC7292996 | biostudies-literature
Project description:The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.
| S-EPMC3820635 | biostudies-other
Project description:BackgroundOur objective was to perform a systematic review and meta-analysis comparing the breast cancer detection rate (CDR), invasive CDR, recall rate, and positive predictive value 1 (PPV1) of digital mammography (DM) alone, combined digital breast tomosynthesis (DBT) and DM, combined DBT and synthetic 2-dimensional mammography (S2D), and DBT alone.MethodsMEDLINE and Embase were searched until April 2020 to identify comparative design studies reporting on patients undergoing routine breast cancer screening. Random effects model proportional meta-analyses estimated CDR, invasive CDR, recall rate, and PPV1. Meta-regression modeling was used to compare imaging modalities. All statistical tests were 2-sided.ResultsForty-two studies reporting on 2 606 296 patients (13 003 breast cancer cases) were included. CDR was highest in combined DBT and DM (6.36 per 1000 screened, 95% confidence interval [CI] = 5.62 to 7.14, P < .001), and combined DBT and S2D (7.40 per 1000 screened, 95% CI = 6.49 to 8.37, P < .001) compared with DM alone (4.68 per 1000 screened, 95% CI = 4.28 to 5.11). Invasive CDR was highest in combined DBT and DM (4.53 per 1000 screened, 95% CI = 3.97 to 5.12, P = .003) and combined DBT and S2D (5.68 per 1000 screened, 95% CI = 4.43 to 7.09, P < .001) compared with DM alone (3.42 per 1000 screened, 95% CI = 3.02 to 3.83). Recall rate was lowest in combined DBT and S2D (42.3 per 1000 screened, 95% CI = 37.4 to 60.4, P<.001). PPV1 was highest in combined DBT and DM (10.0%, 95% CI = 8.0% to 12.0%, P = .004), and combined DBT and S2D (16.0%, 95% CI = 10.0% to 23.0%, P < .001), whereas no difference was detected for DBT alone (7.0%, 95% CI = 6.0% to 8.0%, P = .75) compared with DM alone (7.0%, 95.0% CI = 5.0% to 8.0%).ConclusionsOur findings provide evidence on key performance metrics for DM, DBT alone, combined DBT and DM, and combined DBT and S2D, which may inform optimal application of these modalities for breast cancer screening.
| S-EPMC8168096 | biostudies-literature
Project description:Purpose To evaluate Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment at diagnostic examination after recall from screening in a large urban population after implementation of digital breast tomosynthesis (DBT) by focusing both on overall use and use stratified by recalled finding type and outcome at 2 years. Materials and Methods This was an intuitional review board-approved and HIPAA-compliant retrospective review of 10 728 digital mammography (DM) examinations from September 1, 2010, to August 30, 2011, and 15 571 screening DBT examinations from October 1, 2011, to February 28, 2013. The recall populations for DM and DBT were 1112 of 10 728 (10.4% of women screened) and 1366 of 15 571 (8.8% of women screened), respectively. Recall examinations were classified according to finding type: calcifications, asymmetry or focal asymmetry, mass, and architectural distortion. Differences between groups were compared by using the χ2 test. Results Although there was no significant change in the utilization rate of BI-RADS category 3 in those patients screened with DM compared with DBT (168 of 10 728, 1.6% for DM vs 206 of 15 571, 1.3% for DBT; P = .102), there was a mean overall reduction of 2.4 women per 1000 (95% confidence interval [CI]: -0.5, 5.4) recommended for short-term follow-up. Lesion types given a BI-RADS category 3 assessment after diagnostic work-up did not change. The distribution of recalled finding types significantly changed with DBT, with increased recall examinations for architectural distortion and mass (P < .001) and decreased recall examinations for asymmetries (P ≤ .001). There was no change in recall examinations for calcifications (P = .977). Conclusion Screening with DBT did not significantly change the utilization rate of BI-RADS category 3 classification; however, the overall number of patients recommended for short-interval follow-up decreased by a mean of 2.4 women per 1000 (95% CI: -0.5, 5.4). © RSNA, 2017 Online supplemental material is available for this article.
| S-EPMC5708302 | biostudies-literature
Project description:BackgroundDigital breast tomosynthesis (DBT) has become a prevalent mode of breast cancer screening in recent years. Although older women are commonly screened for breast cancer, little is known about screening outcomes using DBT among older women.ObjectiveTo assess proximal screening outcomes with DBT compared to traditional two-dimensional(2-D) mammography among women 67-74 and women 75 and older.DesignCohort study.ParticipantsMedicare fee-for-service beneficiaries aged 67 years and older with no history of prior cancer who received a screening mammogram in 2015.Main measuresUse of subsequent imaging (ultrasound and diagnostic mammography) as an indication of recall, breast cancer detection, and characteristics of breast cancer at the time of diagnosis. Analyses used weighted logistic regression to adjust for potential confounders.Key resultsOur study included 26,406 women aged 67-74 and 17,001 women 75 and older who were screened for breast cancer. Among women 75 and older, the rate of subsequent imaging among women screened with DBT did not differ significantly from 2-D mammography (91.8 versus 97.0 per 1,000 screening mammograms, p=0.37). In this age group, DBT was associated with 2.1 additional cancers detected per 1,000 screening mammograms compared to 2D (11.5 versus 9.4, p=0.003), though these additional cancers were almost exclusively in situ and stage I invasive cancers. For women 67-74 years old, DBT was associated with a higher rate of subsequent imaging than 2-D mammography (113.9 versus 100.3, p=0.004) and a higher rate of stage I invasive cancer detection (4.7 versus 3.7, p=0.002), but not other stages.ConclusionsBreast cancer screening with DBT was not associated with lower rates of subsequent imaging among older women. Most additional cancers detected with DBT were early stage. Whether detecting these additional early-stage cancers among older women improves health outcomes remains uncertain.
| S-EPMC8483166 | biostudies-literature
Project description:BackgroundDigital breast tomosynthesis (DBT) combined with digital mammography (DM) is increasingly used in the United States instead of DM alone for breast cancer screening. Early screening outcomes incorporating synthetic mammography (SM) with DBT have suggested that SM is an acceptable non-radiation dose alternative to DM.PurposeTo compare multicenter outcomes from breast cancer screening with SM/DBT versus DM/DBT.Materials and MethodsThis was a retrospective study of consecutive screening mammograms obtained at two institutions. Eligible studies consisted of 34 106 DM/DBT examinations between October 3, 2011, and October 31, 2014, and 34 180 SM/DBT examinations between January 7, 2015, and February 2, 2018, at the University of Pennsylvania and 51 148 DM/DBT examinations between January 1, 2012, and May 31, 2016, and 31 929 SM/DBT examinations between June 1, 2016, and March 30, 2018, at the University of Vermont. Demographics of women who attended screening and results from screening were recorded. Recall rate, biopsy rate, false-negative rate, cancer detection rate, positive predictive value, sensitivity, and specificity were calculated according to modality and institution. Descriptive statistics, χ2 tests, and logistic regression were used in analysis.ResultsThe study included 151 363 screening examinations among 151 363 women (mean age, 58.1 years ± 10.9 [standard deviation]). The unadjusted recall rate was lower with SM/DBT than with DM/DBT (7.0% [4630 of 66 109 examinations] for SM/DBT vs 7.9% [6742 of 85 254 examinations] for DM/DBT; P < .01). However, after multivariable adjustment, SM/DBT was associated with a slightly higher recall rate compared with DM/DBT (adjusted odds ratio [OR], 1.06; adjusted 95% CI: 1.01, 1.11; P = .02). Similarly, after multivariable adjustment, SM/DBT was associated with slightly lower specificity compared with DM/DBT (adjusted OR, 0.95; adjusted 95% CI: 0.90, 0.99; P = .02). There was no statistically significant difference in biopsy rate (P = .54), false-negative rate (P = .38), cancer detection rate (P = .55), invasive or in situ cancer detection rate (P = .52 and P = .98, respectively), positive predictive value (P = .78), or sensitivity (P = .33) for SM/DBT versus DM/DBT overall or within either institution (P > .05 for all).ConclusionBreast cancer screening performance is maintained within benchmarks when synthetic mammography replaces digital mammography in digital breast tomosynthesis imaging.© RSNA, 2020Online supplemental material is available for this article.See also the editorial by Lång in this issue.
| S-EPMC7706877 | biostudies-literature
Project description:Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.
| S-EPMC6822772 | biostudies-literature
Project description:PurposeTo investigate how an artificial intelligence (AI) system performs at digital mammography (DM) from a screening population with ground truth defined by digital breast tomosynthesis (DBT), and whether AI could detect breast cancers at DM that had originally only been detected at DBT.Materials and methodsIn this secondary analysis of data from a prospective study, DM examinations from 14 768 women (mean age, 57 years), examined with both DM and DBT with independent double reading in the Malmӧ Breast Tomosynthesis Screening Trial (MBTST) (ClinicalTrials.gov: NCT01091545; data collection, 2010-2015), were analyzed with an AI system. Of 136 screening-detected cancers, 95 cancers were detected at DM and 41 cancers were detected only at DBT. The system identifies suspicious areas in the image, scored 1-100, and provides a risk score of 1 to 10 for the whole examination. A cancer was defined as AI detected if the cancer lesion was correctly localized and scored at least 62 (threshold determined by the AI system developers), therefore resulting in the highest examination risk score of 10. Data were analyzed with descriptive statistics, and detection performance was analyzed with receiver operating characteristics.ResultsThe highest examination risk score was assigned to 10% (1493 of 14 786) of the examinations. With 90.8% specificity, the AI system detected 75% (71 of 95) of the DM-detected cancers and 44% (18 of 41) of cancers at DM that had originally been detected only at DBT. The majority were invasive cancers (17 of 18).ConclusionAlmost half of the additional DBT-only screening-detected cancers in the MBTST were detected at DM with AI. AI did not reach double reading performance; however, if combined with double reading, AI has the potential to achieve a substantial portion of the benefit of DBT screening.Keywords: Computer-aided Diagnosis, Mammography, Breast, Diagnosis, Classification, Application DomainClinical trial registration no. NCT01091545© RSNA, 2021.
| S-EPMC8637235 | biostudies-literature
Project description:PurposeTo compare radiologists' diagnostic accuracy and recall rates for breast tomosynthesis combined with digital mammography versus digital mammography alone.Materials and methodsInstitutional review board approval was obtained at each accruing institution. Participating women gave written informed consent. Mediolateral oblique and craniocaudal digital mammographic and tomosynthesis images of both breasts were obtained from 1192 subjects. Two enriched reader studies were performed to compare digital mammography with tomosynthesis against digital mammography alone. Study 1 comprised 312 cases (48 cancer cases) with images read by 12 radiologists; study 2, 312 cases (51 cancer cases) with 15 radiologists. Study 1 readers recorded only that an abnormality requiring recall was present; study 2 readers had additional training and recorded both lesion type and location. Diagnostic accuracy was compared with receiver operating characteristic analysis. Recall rates of noncancer cases, sensitivity, specificity, and positive and negative predictive values determined by analyzing Breast Imaging Reporting and Data System scores were compared for the two methods.ResultsDiagnostic accuracy for combined tomosynthesis and digital mammography was superior to that of digital mammography alone. Average difference in area under the curve in study 1 was 7.2% (95% confidence interval [CI]: 3.7%, 10.8%; P < .001) and in study 2 was 6.8% (95% CI: 4.1%, 9.5%; P < .001). All 27 radiologists increased diagnostic accuracy with addition of tomosynthesis. Recall rates for noncancer cases for all readers significantly decreased with addition of tomosynthesis (range, 6%-67%; P < .001 for 25 readers, P < .03 for all readers). Increased sensitivity was largest for invasive cancers: 15% and 22% in studies 1 and 2 versus 3% for in situ cancers in both studies.ConclusionAddition of tomosynthesis to digital mammography offers the dual benefit of significantly increased diagnostic accuracy and significantly reduced recall rates for noncancer cases.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120674/-/DC1.
| S-EPMC5410947 | biostudies-literature