Digital Breast Tomosynthesis Examinations Research Articles

Screening the Implant-Augmented Breast with Digital Breast Tomosynthesis: Is Tomosynthesis Necessary for Non-implant-Displaced Views?

To determine cancer visualization utility and radiation dose for non-implant-displaced (ID) views using standard protocol with digital breast tomosynthesis (DBT) vs alternative protocol with 2D only when screening women with implant augmentation. This retrospective cohort study identified women with implants who underwent screening DBT examinations that had abnormal findings from July 28, 2014, to December 31, 2021. Three fellowship-trained breast radiologists independently reviewed examinations retrospectively to determine if the initially identified abnormalities could be visualized on standard protocol (DBT with synthesized 2D (S2D) for ID and non-ID views) and alternate protocol (DBT with S2D for ID and only the S2D images for non-ID views). Estimated exam average glandular dose (AGD) and associations between cancer visualization with patient and implant characteristics for both protocols were evaluated. The study included 195 patients (mean age 55 years ± 10) with 223 abnormal findings. Subsequent biopsy was performed for 86 abnormalities: 59 (69%) benign, 8 (9%) high risk, and 19 (22%) malignant. There was no significant difference in malignancy visualization rate between standard (19/223, 8.5%) and alternate (18/223, 8.1%) protocols (P = .92), but inclusion of the DBT for non-ID views found one additional malignancy. Total examination AGD using standard protocol (21.9 mGy ± 5.0) was significantly higher than it would be for estimated alternate protocol (12.6 mGy ± 5.0, P <.001). This remained true when stratified by breast thickness: 6.0-7.9 cm, 8.0-9.9 cm, >10.0 cm (all P <.001). The inclusion of DBT for non-ID views did not significantly increase the cancer visualization rate but did significantly increase overall examination AGD.

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and ethnicity, age, and breast density) on the performance of an AI algorithm interpreting negative screening digital breast tomosynthesis (DBT) examinations. Materials and Methods This retrospective cohort study identified negative screening DBT examinations from an academic institution from January 1, 2016, to December 31, 2019. All examinations had 2 years of follow-up without a diagnosis of atypia or breast malignancy and were therefore considered true negatives. A subset of unique patients was randomly selected to provide a broad distribution of race and ethnicity. DBT studies in this final cohort were interpreted by a U.S. Food and Drug Administration-approved AI algorithm, which generated case scores (malignancy certainty) and risk scores (1-year subsequent malignancy risk) for each mammogram. Positive examinations were classified based on vendor-provided thresholds for both scores. Multivariable logistic regression was used to understand relationships between the scores and patient characteristics. Results A total of 4855 patients (median age, 54 years [IQR, 46-63 years]) were included: 27% (1316 of 4855) White, 26% (1261 of 4855) Black, 28% (1351 of 4855) Asian, and 19% (927 of 4855) Hispanic patients. False-positive case scores were significantly more likely in Black patients (odds ratio [OR] = 1.5 [95% CI: 1.2, 1.8]) and less likely in Asian patients (OR = 0.7 [95% CI: 0.5, 0.9]) compared with White patients, and more likely in older patients (71-80 years; OR = 1.9 [95% CI: 1.5, 2.5]) and less likely in younger patients (41-50 years; OR = 0.6 [95% CI: 0.5, 0.7]) compared with patients aged 51-60 years. False-positive risk scores were more likely in Black patients (OR = 1.5 [95% CI: 1.0, 2.0]), patients aged 61-70 years (OR = 3.5 [95% CI: 2.4, 5.1]), and patients with extremely dense breasts (OR = 2.8 [95% CI: 1.3, 5.8]) compared with White patients, patients aged 51-60 years, and patients with fatty density breasts, respectively. Conclusion Patient characteristics influenced the case and risk scores of a Food and Drug Administration-approved AI algorithm analyzing negative screening DBT examinations. © RSNA, 2024.

Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.

Purpose To develop an artificial intelligence (AI) model for the diagnosis of breast cancer on digital breast tomosynthesis (DBT) images and to investigate whether it could improve diagnostic accuracy and reduce radiologist reading time. Materials and Methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter reader study was performed to compare the performance of 15 radiologists (seven breast specialists, eight general radiologists) in interpreting DBT examinations in 258 women (mean age, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for stand-alone AI performance was 0.93 (95% CI: 0.92, 0.94). With AI, radiologists' AUC improved from 0.90 (95% CI: 0.86, 0.93) to 0.92 (95% CI: 0.88, 0.96) (P = .003) in the reader study. AI showed higher specificity (89.64% [95% CI: 85.34%, 93.94%]) than radiologists (77.34% [95% CI: 75.82%, 78.87%]) (P < .001). When reading with AI, radiologists' sensitivity increased from 85.44% (95% CI: 83.22%, 87.65%) to 87.69% (95% CI: 85.63%, 89.75%) (P = .04), with no evidence of a difference in specificity. Reading time decreased from 54.41 seconds (95% CI: 52.56, 56.27) without AI to 48.52 seconds (95% CI: 46.79, 50.25) with AI (P < .001). Interreader agreement measured by Fleiss κ increased from 0.59 to 0.62. Conclusion The AI model showed better diagnostic accuracy than radiologists in breast cancer detection, as well as reduced reading times. The concurrent use of AI in DBT interpretation could improve both accuracy and efficiency. Keywords: Breast, Computer-Aided Diagnosis (CAD), Tomosynthesis, Artificial Intelligence, Digital Breast Tomosynthesis, Breast Cancer, Computer-Aided Detection, Screening Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Bae in this issue.

Performance Metrics of Screening Digital Breast Tomosynthesis Based on Years Since a Prior Breast Cancer Diagnosis.

BACKGROUND. Mammography surveillance protocols after breast cancer treatment vary widely. Some practices recommend performing diagnostic mammography for a certain number of years or indefinitely, whereas others recommend returning immediately to screening. OBJECTIVE. This study's objective was to determine performance metrics of screening digital breast tomosynthesis (DBT) in patients who resume screening mammography immediately after breast cancer treatment, based on the number of years since the breast cancer diagnosis. METHODS. This retrospective study included screening DBT examinations performed from January 2013 to June 2019 in patients who resumed screening mammography immediately after a prior breast cancer diagnosis. Multivariable logistic regression models with generalized estimating equations were used to evaluate associations between screening performance metrics and years since the prior breast cancer diagnosis, controlling for age, race and ethnicity, breast density, presence of a prior screening mammogram, and interpreting radiologist. RESULTS. The study included 8090 patients (mean age, 65 ± 11 [SD] years) with a prior breast cancer diagnosis who underwent 30,812 screening DBT examinations during the study period. The cancer detection rate (CDR) was 8.6 per 1000 examinations (265/30,812), abnormal interpretation rate (AIR) was 5.7% (1750/30,812), PPV1 was 15.1% (265/1750), sensitivity was 80.3% (265/330), specificity was 95.1% (28,997/30,482), and false-negative rate was 2.1 per 1000 examinations (65/30,812). CDR showed a significant independent positive association with years since breast cancer diagnosis (adjusted OR, 1.03; 95% CI, 1.01-1.05; p < .001), being lowest more than 2 to up to 3 years after diagnosis (4.9 per 1000 examinations) and highest more than 8 to up to 9 years after diagnosis (11.2 per 1000 examinations). AIR showed a significant independent negative association with years since breast cancer diagnosis (adjusted OR, 0.99; 95% CI, 0.98-1.00; p = .01), being highest 1 year or less after diagnosis (7.5%) and lowest more than 5 to up to 6 years after diagnosis (5.0%). CONCLUSION. Among 8090 patients with a prior breast cancer diagnosis, even though the AIR was higher during the year after diagnosis compared with subsequent years, the AIR remained acceptably low (< 10%) in all years. CLINICAL IMPACT. These results support the study institution's mammographic surveillance protocol for patients with a prior breast cancer diagnosis of returning immediately to DBT screening.

Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.

Breast cancer screening is predominantly performed using digital mammography (DM), but digital breast tomosynthesis (DBT) has higher sensitivity. DBT demands more resources than DM, and it might be more feasible to reserve DBT for women with a clear benefit from the technique. We explore if artificial intelligence (AI) can select women who would benefit from DBT imaging. We used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately double read DM and DBT. We retrospectively analyzed DM examinations () with a breast cancer detection system and used the provided risk score (1 to 10) for risk stratification. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives. If using a threshold of 9.0, 25 (26%) more cancers would be detected compared to using DM alone. Of the 41 cancers only detected on DBT, 61% would be detected, with only 1797 (12%) of the women examined with both DM and DBT. The detection rate for the added DBT would be 14/1000 women, whereas the false-positive recalls would be increased with 58 (21%). Using DBT only for selected high gain cases could be an alternative to complete DBT screening. AI can analyze initial DM images to identify high gain cases where DBT can be added during the same visit. There might be logistical challenges, and further studies in a prospective setting are necessary.

Digital Breast Tomosynthesis versus Digital Mammography Screening Performance on Successive Screening Rounds from the Breast Cancer Surveillance Consortium.

Background Prior cross-sectional studies have observed that breast cancer screening with digital breast tomosynthesis (DBT) has a lower recall rate and higher cancer detection rate compared with digital mammography (DM). Purpose To evaluate breast cancer screening outcomes with DBT versus DM on successive screening rounds. Materials and Methods In this retrospective cohort study, data from 58 breast imaging facilities in the Breast Cancer Surveillance Consortium were collected. Analysis included women aged 40-79 years undergoing DBT or DM screening from 2011 to 2020. Absolute differences in screening outcomes by modality and screening round were estimated during the study period by using generalized estimating equations with marginal standardization to adjust for differences in women's risk characteristics across modality and round. Results A total of 523 485 DBT examinations (mean age of women, 58.7 years ± 9.7 [SD]) and 1 008 123 DM examinations (mean age, 58.4 years ± 9.8) among 504 863 women were evaluated. DBT and DM recall rates decreased with successive screening round, but absolute recall rates in each round were significantly lower with DBT versus DM (round 1 difference, -3.3% [95% CI: -4.6, -2.1] [P < .001]; round 2 difference, -1.8% [95% CI: -2.9, -0.7] [P = .003]; round 3 or above difference, -1.2% [95% CI: -2.4, -0.1] [P = .03]). DBT had significantly higher cancer detection (difference, 0.6 per 1000 examinations [95% CI: 0.2, 1.1]; P = .009) compared with DM only for round 3 and above. There were no significant differences in interval cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.24, 0.30] [P = .96]; round 2 or above difference, 0.04 [95% CI: -0.19, 0.31] [P = .76]) or total advanced cancer rate (round 1 difference, 0.00 per 1000 examinations [95% CI: -0.15, 0.19] [P = .94]; round 2 or above difference, -0.06 [95% CI: -0.18, 0.11] [P = .43]). Conclusion DBT had lower recall rates and could help detect more cancers than DM across three screening rounds, with no difference in interval or advanced cancer rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Skaane in this issue.

National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium.

Background It is important to establish screening mammography performance benchmarks for quality improvement efforts. Purpose To establish performance benchmarks for digital breast tomosynthesis (DBT) screening and evaluate performance trends over time in U.S. community practice. Materials and Methods In this retrospective study, DBT screening examinations were collected from five Breast Cancer Surveillance Consortium (BCSC) registries between 2011 and 2018. Performance measures included abnormal interpretation rate (AIR), cancer detection rate (CDR), sensitivity, specificity, and false-negative rate (FNR) and were calculated based on the American College of Radiology Breast Imaging Reporting and Data System, fifth edition, and compared with concurrent BCSC DM screening examinations, previously published BCSC and National Mammography Database benchmarks, and expert opinion acceptable performance ranges. Benchmarks were derived from the distribution of performance measures across radiologists (n = 84 or n = 73 depending on metric) and were presented as percentiles. Results A total of 896 101 women undergoing 2 301 766 screening examinations (458 175 DBT examinations [median age, 58 years; age range, 18-111 years] and 1 843 591 DM examinations [median age, 58 years; age range, 18-109 years]) were included in this study. DBT screening performance measures were as follows: AIR, 8.3% (95% CI: 7.5, 9.3); CDR per 1000 screens, 5.8 (95% CI: 5.4, 6.1); sensitivity, 87.4% (95% CI: 85.2, 89.4); specificity, 92.2% (95% CI: 91.3, 93.0); and FNR per 1000 screens, 0.8 (95% CI: 0.7, 1.0). When compared with BCSC DM screening examinations from the same time period and previously published BCSC and National Mammography Database performance benchmarks, all performance measures were higher for DBT except sensitivity and FNR, which were similar to concurrent and prior DM performance measures. The following proportions of radiologists achieved acceptable performance ranges with DBT: 97.6% for CDR, 91.8% for sensitivity, 75.0% for AIR, and 74.0% for specificity. Conclusion In U.S. community practice, large proportions of radiologists met acceptable performance ranges for screening performance metrics with DBT. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Artificial-intelligence-enhanced synthetic thick slabs versus standard slices in digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) can provide additional information over mammography, albeit at the cost of prolonged reading time. This study retrospectively investigated the impact of reading enhanced synthetic 6 mm slabs instead of standard 1 mm slices on interpretation time and readers performance in a diagnostic assessment centre. Three radiologists (R1-3; 6/4/2 years of breast imaging experience) reviewed 111 diagnostic DBT examinations. Two datasets were interpreted independently for each patient, with one set containing artificial-intelligence-enhanced synthetic 6 mm slabs with 3 mm overlap, while the other set comprised standard 1 mm slices. Blinded to histology and follow-up, readers noted individual BIRADS categories and diagnostic confidence while reading time was recorded. Among the 111 examinations, 70 findings were histopathologically correlated including 56 malignancies. No significant difference was found between BIRADS categories assigned based on 6 mm vs 1 mm datasets (p ≥ 0.317). Diagnostic accuracy was comparable for 6 mm and 1 mm readings (R1: 87.0% vs 87.0%; R2: 86.1% vs 87.0%; R3: 80.0% vs 84.4%; p ≥ 0.125) with high interrater agreement (intraclass correlation coefficient 0.848 vs 0.865). One reader reported higher confidence with 1 mm slices (R1: p = 0.033). Reading time was substantially shorter when interpreting 6 mm slabs compared to 1 mm slices (R1: 33.5 vs 46.2; R2: 49.1 vs 64.8; R3: 39.5 vs 67.2 sec; all p < 0.001). Artificial-intelligence-enhanced synthetic 6 mm slabs allow for substantial interpretation time reduction in diagnostic DBT without a decrease in reader accuracy. A simplified slab-only protocol instead of 1 mm slices may offset the higher reading time without a loss of diagnosis-relevant image information in first and second readings. Further evaluations are required regarding workflow implications, particularly in screening settings.

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

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

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

Impact of Obtaining a Digital Breast Tomosynthesis (DBT) Spot Compression View on Assessment of Equivocal DBT Findings.

BACKGROUND. A recently introduced digital breast tomosynthesis (DBT) device allows acquisition of DBT spot compression views with a small paddle during DBT acquisition. OBJECTIVE. The purpose of this study was to evaluate the impact on diagnostic performance of obtaining a DBT spot compression view for assessment of equivocal DBT findings. METHODS. This retrospective study included 102 women (mean age, 60 years) in whom a DBT spot compression view was obtained to characterize an equivocal finding on DBT at the performing radiologist's discretion. The DBT examinations were performed from December 14, 2018, to December 18, 2019. Two fellowship-trained breast radiologists and one breast imaging fellow, who were aware of the location of the equivocal lesions, independently reviewed the examinations. Readers first assigned a BI-RADS category using standard DBT views and then immediately assigned a category using the DBT spot compression view. BI-RADS categories 2 and 3 were considered negative, and categories 4A and greater were considered positive. Histology and at least 1 year of imaging follow-up served as the reference standard. Intrareader agreement for one reader and interreader agreement among all readers were evaluated with kappa coefficients. Diagnostic performance was compared between DBT with and DBT without spot compression views by use of McNemar tests. RESULTS. Intrareader agreement increased from 0.43 to 0.72, and interreader agreement increased from 0.21 to 0.45 on the basis of kappa coefficients for DBT without and with spot compression views. Eighteen cancers were present. Compared with standard DBT views, DBT spot compression views yielded significantly increased accuracy for all three readers (75% vs 90%, 74% vs 94%, 72% vs 94%); significantly increased specificity for all three readers (69% vs 90%, 75% vs 94%, 68% vs 93%); and significantly increased sensitivity for one reader (67% vs 94%) without significant change in sensitivity for the two other readers (89% vs 100%, 100% vs 89%). Radiation dose was 1.97 mGy for the DBT spot compression view versus 1.78-1.81 mGy for standard DBT craniocaudal and medio-lateral oblique views. CONCLUSION. Use of the DBT spot compression view increased intrareader agreement, interreader agreement, and diagnostic accuracy (primarily owing to improved specificity); the supplemental dose for the spot compression view was slightly higher than that for a standard DBT view. CLINICAL IMPACT. DBT spot compression may help characterize equivocal DBT findings, reducing further workup for benign findings.

Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis.

Background Digital breast tomosynthesis (DBT) has higher diagnostic accuracy than digital mammography, but interpretation time is substantially longer. Artificial intelligence (AI) could improve reading efficiency. Purpose To evaluate the use of AI to reduce workload by filtering out normal DBT screens. Materials and Methods The retrospective study included 13 306 DBT examinations from 9919 women performed between June 2013 and November 2018 from two health care networks. The cohort was split into training, validation, and test sets (3948, 1661, and 4310 women, respectively). A workflow was simulated in which the AI model classified cancer-free examinations that could be dismissed from the screening worklist and used the original radiologists' interpretations on the rest of the worklist examinations. The AI system was also evaluated with a reader study of five breast radiologists reading the DBT mammograms of 205 women. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate were evaluated in both studies. Statistics were computed across 10 000 bootstrap samples to assess 95% CIs, noninferiority, and superiority tests. Results The model was tested on 4310 screened women (mean age, 60 years ± 11 [standard deviation]; 5182 DBT examinations). Compared with the radiologists' performance (417 of 459 detected cancers [90.8%], 477 recalls in 5182 examinations [9.2%]), the use of AI to automatically filter out cases would result in 39.6% less workload, noninferior sensitivity (413 of 459 detected cancers; 90.0%; P = .002), and 25% lower recall rate (358 recalls in 5182 examinations; 6.9%; P = .002). In the reader study, AUC was higher in the standalone AI compared with the mean reader (0.84 vs 0.81; P = .002). Conclusion The artificial intelligence model was able to identify normal digital breast tomosynthesis screening examinations, which decreased the number of examinations that required radiologist interpretation in a simulated clinical workflow. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection among Women with a History of Breast Cancer

To compare the diagnostic performance of abbreviated breast MRI (AB-MRI) and digital breast tomosynthesis (DBT) in women with a personal history (PH) of breast cancer as a postoperative screening tool. A total of 471 patients who completed both DBT and AB-MRI examinations were included in this study (median age, 54.5 years). The detected cancer characteristics were analyzed. The cancer detection rate (CDR), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) were calculated by receiver operating characteristic (ROC) curve analysis. Eleven malignancies were diagnosed, and most of the detected cancers were stage I (7 of 11, 63.6%). Eight were invasive ductal carcinomas (IDC), and 3 were ductal carcinoma in situ (DCIS). Of the 11 recurrences, 6 malignancies were detected by DBT, and 11 were detected by AB-MRI. AB-MRI detected all 8 IDC and 3 DCIS lesions, and DBT detected 6 of 8 IDC lesions. The CDRs for DBT and AB-MRI screenings were 12.7 and 23.4 per 1,000 women, respectively. The sensitivity, specificity, PPV, NPV, and accuracy of DBT versus AB-MRI were 54.6% versus 100%, 97.6% versus 96.5%, 35.3% versus 40.7%, 98.9% versus 100%, and 96.6% versus 96.6%, respectively. AB-MRI showed a higher AUC value (0.983) than DBT (0.761) (p=0.0049). AB-MRI showed an improved CDR, especially for invasive cancer. The diagnostic performance of AB-MRI was superior to that of DBT with high sensitivity and PPV without sacrificing specificity in women with a PH of breast cancer.

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation.

Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test. Results A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years ± 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: -2.4, -1.6). For DBT, AI achieved noninferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9). Conclusion Artificial intelligence could replace radiologists' readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography but a higher recall rate for digital breast tomosynthesis. Published under a CC BY 4.0 license. See also the editorial by Fuchsjäger and Adelsmayr in this issue.

Developing Asymmetries without Sonographic Correlate at Digital Breast Tomosynthesis.

Background Digital breast tomosynthesis (DBT) has improved the accuracy of mammography, including resolving many breast asymmetries as overlapping breast tissue. The pathologic outcomes of persistent developing asymmetries visualized at DBT are not well established. Purpose To characterize the outcomes and the predictors of malignancy for developing asymmetries visualized at DBT without a sonographic correlate. Materials and Methods This retrospective study included all tomosynthesis-guided biopsies of developing asymmetries performed at a single institution from May 2017 through January 2020. A reader study including three breast imaging radiologists determined interrater agreement and inclusion into the study. Electronic medical records were used to extract patient characteristics, imaging characteristics, and pathologic diagnoses. The Wilcoxon rank sum test, Fisher exact test, and χ2 test were used to analyze correlations of patient and imaging characteristics with likelihood of malignancy. Results The reader study included 95 DBT examinations with moderate interrater reliability (Fleiss κ = 0.45). There was majority reader agreement in 85 of the 95 DBT examinations (89%) of 83 women (median age, 56 years; interquartile range, 47-69 years), and this finalized the study data set. At pathologic examination, most asymmetries (68 of 85, 80%) were benign, with common diagnoses being fibrocystic change (n = 20), stromal fibrosis (n = 10), and fat necrosis (n = 10). The overall malignancy rate was 20% (17 of 85 asymmetries; 95% CI: 12, 29); 15 of the 17 malignancies (88%) were invasive cancers. Malignancies were more common in women with a personal history of breast cancer (35% vs 10%, P = .02). Conclusion In 85 developing asymmetries visualized at digital breast tomosynthesis without a sonographic correlate, there was a 20% (95% CI: 12, 29) malignancy rate, which was higher than the rates of malignancy for a developing asymmetry detected at digital mammography. © RSNA, 2021 See also the editorial by Skaane in this issue.

Benefit of digital breast tomosynthesis in symptomatic young women (≤30 years) diagnosed with BI-RADS category 4 or 5 on ultrasound

To evaluate the addition of digital breast tomosynthesis (DBT) in the diagnosis of breast lesions in symptomatic young Chinese women (≤30 years) diagnosed with Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5 on ultrasound, and demonstrate the potential use of combining DBT with ultrasound. This retrospective analysis included 5 years of digital mammography (DM) and DBT data (January 2015 to July 2020). In total, 768 DBT and DM examinations were performed in 713 young women. The results were determined by pathological assessment. Diagnostic performance was measured based on the sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic area under the curve (AUC). Compared with DM alone, DBT+DM increased the sensitivity from 82.5% to 93.2%, specificity from 70.8% to 84%, accuracy from 74% to 86.5%, NPV from 93.6% to 97.4% (all p<0.01). The AUC of DBT+DM (0.946, 95% confidence interval [CI]: 0.927-0.960) was greater than that of DM (0.884, 95% CI: 0.859-0.905; p<0.001). The differences in the BI-RADS category distributions of malignant and benign lesions were both statistically significant (p<0.001). DM alone led to 36 false-negative diagnoses, whereas the inclusion of DBT identified breast cancer in 22 of those cases. There were 4.9% (10/206) false-negative diagnoses in ultrasound. After adding DBT, four breast cancers were detected. An additional six breast cancers were diagnosed by biopsy based on an assessment of BI-RADS 4A by DBT/DM. DBT+DM significantly improves the diagnostic performance in this young population, especially in young people with higher breast density. Moreover, DBT is an effective supplementary examination to ultrasound.

Malignancy Upgrade Rates of Radial Sclerosing Lesions at Breast Cancer Screening

Purpose To determine the upgrade rate for biopsy-proven radial scars and radial sclerosing lesions (RS). Materials and Methods In this retrospective study, radiology and pathology databases from two tertiary breast centers were searched to identify patients with biopsy-confirmed RS between March 1, 2012, and December 31, 2017, during which all mammography was performed with digital breast tomosynthesis (DBT). Adjunct modalities such as MRI or US are performed at our centers to better characterize lesions identified at DBT. Patient demographics, imaging, needle and excisional biopsies, and follow-up data were collected at the patient level. Clopper-Pearson interval estimate for upgrade was calculated for 95% confidence using PropCIs package with R version 4.1.0 (R Foundation for Statistical Computing) (1). Results During the study period, a total of 155 885 DBT examinations were performed. From these examinations, 146 biopsy-proven RS were identified in 142 women (median age, 58 years; age range, 26-87 years). A total of 80.1% (117 of 146) of all RS did not have associated atypia or malignancy, and 19.9% (29 of 146) had associated atypia at initial biopsy. A total of 66.7% (78 of 117) of RS without atypia or malignancy were surgically excised, 25.6% (30 of 117) were followed (median, 3 years; range, 1-7 years) with benign findings on imaging, and 7.7% (nine of 117) were lost to follow-up. The rate of malignancy upgrade was 0.9% (one of 117 [95% CI: 0.02, 4.7]); one RS without concurrent atypia or malignancy demonstrated invasive carcinoma at surgical excision. Conclusion RS without atypia had a low upgrade rate. Keywords: Mammography, Breast © RSNA, 2021.

Assessment of the uterine dose in digital mammography and digital breast tomosynthesis

IntroductionDigital Mammography (DM-2D) and more recently Digital Breast Tomosynthesis (DBT), are two of the most effective imaging modalities for breast cancer detection, often used in screening programmes. It may happen that exams using these two imaging modalities are inadvertently performed to pregnant women. The objective of this study is to assess the dose in the uterus due to DM-2D and DBT exams, according to two main irradiation scenarios: in the 1st scenario the exposure parameters were pre-selected directly by the imaging system, while in the 2nd scenario, the maximum exposure parameters were chosen. MethodsThe mammography equipment used was a Siemens Mammomat Inspiration. A physical anthropomorphic phantom, PMMA plates (simulating a breast thickness of 6 cm) and thermoluminescent dosimeters (TLDs) were used to measure entrance air kerma values on the phantom's breast and abdomen in order to successively estimate the mean glandular dose (MGD) and the dose in the uterus. For the two irradiation scenarios chosen, two-breast imaging modalities were selected: 1) DBT in Cranio-Caudal (CC) view (with 28 kV and 160 mAs as exposure parameters), 2) DBT and DM in Medio Lateral-Oblique (MLO) and CC views (with 34 kV and 250 mAs as exposure parameters). ResultsIn the 1st scenario, the TLD measurements did not detect significant dose values in the abdomen whereas the MGD estimated using the D.R. Dance model was in close agreement with data available in the literature. In the 2nd scenario, there was no significant difference in MGD estimation between the different views, whereas the air kerma values in the abdomen (in DBT mode, CC and MLO) were 0.049 mGy and 0.004 mGy respectively. In CC DM-2D mode the abdomen air kerma value was 0.026 mGy, with no significant detected value in MLO view. ConclusionsFor the dose in the uterus, the obtained values seem to indicate that DM-2D and DBT examinations inadvertently performed during pregnancy do not pose a significant radiological risk, even considering the case of overexposure in both breasts. Implications for practiceThe accurate knowledge of the doses in DM-2D and DBT will contribute to raise the awareness among medical practitioners involved in breast imaging empowering them to provide accurate information about dose levels in the uterus, improving their radiation risk communication skills and consequently helping to reduce the anxiety of pregnant women undergoing this type of examinations.

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.

Estimated risk of radiation-induced cancer following breast screening employing tomosynthesis and digital mammography

The objective of this study was to estimate the risk of radiation-induced injury to the organs due to ionizing radiation following breast screening recommendations employing digital breast tomosynthesis (DBT) and digital mammography (DM). Using the Monte Carlo method, absorbed doses in the tissues and organs were calculated on an adult female phantom, considering two-view craniocaudal (CC) and mediolateral oblique (MLO) projections for each breast. The results showed differences in the total effective risk due to DM (CC + MLO) and DBT (CC + MLO) examinations in Brazil, ranging from 20.73 cases 10−5 (DM) to 27.19 cases 10−5 (DBT). Significant differences were also observed in the total effective risk of cancer incidence in the lungs due to DM (CC + MLO) and DBT (CC + MLO), ranging from 1.75×10−01 cases 10−5 (DM) to 1.76×10−01 cases 10−5 (DBT). The results indicate that the total effective risk of incidence should be considered as an additional parameter for the evaluation of DBT or DBT + DM program performance.