Diagnostic performance of tomosynthesis plus synthetic mammography versus full-field digital mammography with or without tomosynthesis in breast cancer screening: A systematic review and meta-analysis

Digital breast tomosynthesis (DBT) with full-field digital mammography (FFDM) exposes women to a higher radiation dose. A synthetic 2D mammogram (S2D) is a two-dimensional image constructed from DBT. We aim to evaluate the S2D performance when used alone or combined with DBT compared to FFDM alone or with DBT. Studies were included if they recruited screening participants and reported on S2D performance. Studies were excluded if they included symptomatic patients, imaging was for diagnostic purposes, or if participants had a breast cancer history. Meta-analyses for cancer detection rates (CDR) and Specificities were conducted where available. Differences in the performance of imaging modalities were calculated within individual studies, and these were pooled by meta-analysis. Out of 3241 records identified, 17 studies were included in the review and 13 in the meta-analysis. The estimated combined difference in CDRs per thousand among individual studies that reported on DBT plus S2D vs. FFDM and those reporting on DBT plus S2D versus DBT plus FFDM was 2.03 (95% CI 0.81-3.25) and - 0.15 (95% CI -1.17 to 0.86), respectively. The estimated difference in percent specificities was 1.13 (95% CI -0.06 to 2.31) in studies comparing DBT plus S2D and FFDM. In studies comparing DBT plus S2D and DBT plus FFDM, the estimated difference in specificities was 1.08 (95% CI 0.59-1.56). DBT plus S2D showed comparable accuracy to FFDM plus DPT and improved cancer detection to FFDM alone. Integrating S2D with DBT in breast cancer screening is safe and preserves performance.

Cancers not detected in one-view breast tomosynthesis screening-characteristics and reasons for non-detection

OBJECTIVES

Limited understanding exists regarding non-detected cancers in digital breast tomosynthesis (DBT) screening. This study aims to classify non-detected cancers into true or false negatives, compare them with true positives, and analyze reasons for non-detection.

MATERIALS AND METHODS

Conducted between 2010 and 2015, the prospective single-center Malmö Breast Tomosynthesis Screening Trial (MBTST) compared one-view DBT and two-view digital mammography (DM). Cancers not detected by DBT, i.e., interval cancers, those detected in the next screening round, and those only identified by DM, underwent a retrospective informed review by in total four breast radiologists. Reviewers classified cancers into true negative, false negative, or non-visible based on both DBT and DM findings and assessed radiographic appearances at screening and diagnosis, breast density, and reasons for non-detection. Statistics included the Pearson X2 test.

RESULTS

In total, 89 cancers were not detected with DBT in the MBTST; eight cancers were solely in the DM reading mode, 59 during subsequent DM screening rounds, and 22 interval cancers. The proportion of cancers classified as false negative was 25% (22/89) based on DBT, compared with 18% (14/81) based on DM screening. The primary reason for false negatives was normal-appearing density, 50% (11/22). False negatives exhibited lower rates of high breast density, 36% (8/22), compared with true positives, 61% (78/129), p = 0.04, and spiculated densities were less frequent in false negatives, 41% (9/22) compared with true positives, 68% (88/129), p = 0.01.

CONCLUSION

False negatives in one-view DBT screening commonly presented with spiculated features, but less frequently than true positives, and were missed or misinterpreted due to benign appearances.

KEY POINTS

Question Cancers not detected in digital breast tomosynthesis screening, including false negatives, remain partly unexplored. Findings The most common reason behind false-negative cancers in a large screening trial was a normal-appearing density. Clinical relevance Recognizing the factors contributing to false negative findings in digital breast tomosynthesis screening is essential to further improve cancer detection.

Digital Breast Tomosynthesis Screening Improves Early Breast Cancer Detection and Survival in Taiwan

OBJECTIVE

Our objective was to compare the efficacy of digital breast tomosynthesis (DBT) and digital mammography (DM) in breast cancer screening and their impact on long-term overall survival (OS).

METHODS

The study involved 48 549 consecutive mammography examinations between 2011 and 2015 at a medical center in Taiwan, identifying 545 women who were screened and diagnosed with breast cancer. Digital mammography and DBT examinations were alternated on different days. Patients were categorized based on mammographic modality, breast density, and American Joint Committee on Cancer (AJCC) stage. To determine the long-term outcome until August 2021, survival rates were analyzed using the Kaplan-Meier (K-M) survival analysis.

RESULTS

The mean age at breast cancer diagnosis was 53.2 years. Digital breast tomosynthesis examinations were significantly associated with early breast cancer (AJCC stage 0 to 2) (P = .022). The 5- and 9-year OS rates for all patients were 96.8% and 93.0%, respectively. The 5- and 9-year OS was significantly greater in the DBT group (98.4% and 96.8%) compared with the DM group (95.0% and 90.4%) (P = .030 for all). The K-M survival analysis demonstrated a significantly higher OS in the DBT group than the DM group (P = .037). Furthermore, DBT significantly improved OS in a cohort of women with stage II and III cancer (P = .032) and heterogeneously dense breasts (P = .045).

CONCLUSION

Screening with DBT is associated with early breast cancer diagnosis and higher survival rates compared with DM.

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer Database and the DDSM Curated Breast Imaging Subset (CBIS-DDSM). The Wisconsin Breast Cancer Database provides a detailed examination of the characteristics of cell nuclei, including radius, texture, and concavity, for 569 patients, of which 212 had malignant tumors. In addition, the CBIS-DDSM dataset-a revised variant of the Digital Database for Screening Mammography (DDSM)-offers a standardized collection of 2,620 scanned film mammography studies, including cases that are normal, benign, or malignant and that include verified pathology data. To identify complex patterns and trait diagnoses of breast cancer, this investigation used a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with the stochastic gradients method. The Wisconsin Breast Cancer Database is used for CNN training, while the CBIS-DDSM dataset is used for fine-tuning to maximize adaptability across a variety of mammography investigations. Data integration, feature extraction, model development, and thorough performance evaluation are the main objectives. The diagnostic effectiveness of the algorithm was evaluated by the area under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, specificity, and accuracy. The generalizability of the model will be validated by independent validation on additional datasets. This research provides an accurate, comprehensible, and therapeutically applicable breast cancer detection method that will advance the field. These predicted results might greatly increase early diagnosis, which could promote improvements in breast cancer research and eventually lead to improved patient outcomes.

Breast Cancers Detected during a Decade of Screening with Digital Breast Tomosynthesis: Comparison with Digital Mammography

Background Digital breast tomosynthesis (DBT) has been shown to help increase cancer detection compared with two-dimensional digital mammography (DM). However, it is unclear whether additional tumor detection will improve outcomes or lead to overdiagnosis of breast cancer. Purpose This study aimed to compare cancer types and stages over 3 years of DM screening and 10 years of DBT screening to determine the effect of DBT. Materials and Methods A retrospective search identified breast cancers detected by using screening mammography from August 2008 through July 2021. Data collected included demographic, imaging, and pathologic information. Invasive cancers 2 cm or larger, human epidermal growth factor 2-positive or triple-negative tumors greater than 10 mm, axillary nodes positive for cancer, and distant organ spread were considered advanced cancers. The DBT and DM cohorts were compared and further analyzed by prevalent versus incident examinations. False-negative findings were also assessed. Results A total of 1407 breast cancers were analyzed (142 with DM, 1265 with DBT). DBT showed a higher rate of cancer depiction than DM (5.3 vs four cancers per 1000, respectively; P = .001), with a similar ratio of invasive cancers to ductal carcinomas in situ (76.5%:23.5% [968 and 297 of 1265, respectively] vs 71.1%:28.9% [101 and 41 of 142, respectively]). Mean invasive cancer size did not differ between DM and DBT (1.44 cm ± 0.93 [SD] vs 1.36 cm ± 1.14, respectively; P = .49), but incident DBT cases were smaller than prevalent cases (1.2 cm ± 1.0 vs 1.6 cm ± 1.4, respectively; P < .001). DBT and DM had similar rates of invasive cancer subtypes: low grade (26.5% [243 of 912] vs 29% [28 of 96], respectively), moderate grade (57.2% [522 of 912] vs 51% [49 of 96], respectively), and high grade (16.1% [147 of 912] vs 20% [19 of 96], respectively) (P = .65). The proportion of advanced cancers was lower with DBT than DM (32.6% [316 of 968] vs 43.6% [44 of 101], respectively; P = .04) and between DBT prevalent and incident screening (39.1% [133 of 340] vs 29.1% [183 of 628], respectively; P = .003). There was no difference in interval cancer rates (0.14 per 1000 with DM and 0.2 per 1000 with DBT; P = .42) for both groups. Conclusion DBT helped to increase breast cancer detection rate and depicted invasive cancers with a lower rate of advanced cancers compared with DM, with further improvement observed at incident rounds of screening. © RSNA, 2024 See also the editorial by Kim and Woo in this issue.

How long does it take to read a mammogram? Investigating the reading time of digital breast tomosynthesis and digital mammography

PURPOSE

To analyse digital breast tomosynthesis (DBT) reading times in the screening setting, compared to 2D full-field digital mammography (FFDM), and investigate the impact of reader experience and professional group on interpretation times.

METHOD

Reading time data were recorded in the PROSPECTS Trial, a prospective randomised trial comparing DBT plus FFDM or synthetic 2D mammography (S2D) to FFDM alone, in the National Health Service (NHS) breast screening programme, from January 2019-February 2023. Time to read DBT+FFDM or DBT+S2D and FFDM alone was calculated per case and reading times were compared between modalities using dependent T-tests. Reading times were compared between readers from different professional groups (radiologists and radiographer readers) and experience levels using independent T-tests. The learning curve effect of using DBT in screening on reading time was investigated using a Kruskal-Wallis test.

RESULTS

Forty-eight readers interpreted 1,242 FFDM batches (34,210 FFDM cases) and 973 DBT batches (13,983 DBT cases). DBT reading time was doubled compared to FFDM (2.09 ± 0.64 min vs. 0.98 ± 0.30 min; p < 0.001), and DBT+S2D reading was longer than DBT + FFDM (2.24 ± 0.62 min vs. 2.04 ± 0.46 min; p = 0.006). No difference was identified in reading time between radiologists and radiographers (2.06 ± 0.71 min vs. 2.14 ± 0.46 min, respectively; p = 0.71). Readers with five or more years of experience reading DBT were quicker than those with less experience (1.86 ± 0.56 min vs. 2.37 ± 0.65 min; p = 0.008), and DBT reading time decreased after less than 9 months accrued screening experience (p = 0.01).

CONCLUSIONS

DBT reading times were double those of FFDM in the screening setting, but there was a short learning curve effect with readers showing significant improvements in reading times within the first nine months of DBT experience.

CLINICALTRIALS

gov Identifier: NCT03733106.

Limited impact of adding digital breast tomosynthesis to full field digital mammography in an elevated breast cancer risk population

PURPOSE

To investigate the impact of adding digital breast tomosynthesis (DBT) to full field digital mammography (FFDM) in screening asymptomatic women with an elevated breast cancer life time risk (BCLTR) but without known genetic mutation.

METHODS

This IRB-approved single-institution multi-reader study on prospectively acquired FFDM + DBT images included 429 asymptomatic women (39-69y) with an elevated BC risk on their request form. The BCLTR was calculated for each patient using the IBISrisk calculator v8.0b. The screening protocol and reader study consisted of 4-view FFDM + DBT, which were read by four independent radiologists using the BI-RADS lexicon. Standard of care (SOC) included ultrasound (US) and magnetic resonance imaging (MRI) for women with > 30 % BCLTR. Breast cancer detection rate (BCDR), sensitivity and positive predictive value were assessed for FFDM and FFDM + DBT and detection outcomes were compared with McNemar-test.

RESULTS

In total 7/429 women in this clinically elevated breast cancer risk group were diagnosed with BC using SOC (BCDR 16.3/1000) of which 4 were detected with FFDM. Supplemental DBT did not detect additional cancers and BCDR was the same for FFDM vs FFDM + DBT (9.3/1000, McNemar p = 1). Moderate inter-reader agreement for diagnostic BI-RADS score was found for both study arms (ICC for FFDM and FFDM + DBT was 0.43, resp. 0.46).

CONCLUSION

In this single institution study, supplemental screening with DBT in addition to standard FFDM did not increase BCDR in this higher-than-average BC risk group, objectively documented using the IBISrisk calculator.

Introduction of one-view tomosynthesis in population-based mammography screening: Impact on detection rate, interval cancer rate and false-positive rate

OBJECTIVE

To assess performance endpoints of a combination of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) compared with FFDM only in breast cancer screening.

MATERIALS AND METHODS

This was a prospective population-based screening study, including eligible (50-69 years) women attending the Capital Region Mammography Screening Program in Denmark. All attending women were offered FFDM. A subgroup was consecutively allocated to a screening room with DBT. All FFDM and DBT underwent independent double reading, and all women were followed up for 2 years after screening date or until next screening date, whichever came first.

RESULTS

6353 DBT + FFDM and 395 835 FFDM were included in the analysis and were undertaken in 196 267 women in the period from 1 November 2012 to 12 December 2018. Addition of DBT increased sensitivity: 89.9% (95% confidence interval (CI): 81.0-95.5) for DBT + FFDM and 70.1% (95% CI: 68.6-71.6) for FFDM only, p < 0.001. Specificity remained similar: 98.2% (95% CI: 97.9-98.5) for DBT + FFDM and 98.3% (95% CI: 98.2-98.3) for FFDM only, p = 0.9. Screen-detected cancer rate increased statistically significantly: 11.18/1000 for DBT + FFDM and 6.49/1000 for FFDM only, p < 0.001. False-positive rate was unchanged: 1.75% for DBT + FFDM and 1.73% for FFDM only, p = 0.9. Positive predictive value for recall was 39.0% (95% CI: 31.9-46.5) for DBT + FFDM and 27.3% (95% CI: 26.4-28.2), for FFDM only, p < 0.0005. The interval cancer rate decreased: 1.26/1000 for DBT + FFDM and 2.76/1000 for FFDM only, p = 0.02.

CONCLUSION

DBT + FFDM yielded a statistically significant increase in cancer detection and program sensitivity.

Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound

Purpose

To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.

Materials and methods

149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA.

Results

Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.

Conclusion

The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Assessing Digital Breast Tomosynthesis Impact on Early Cancer Detection: Insights from Consecutive Screening

Background Analysis of how digital breast tomosynthesis (DBT) screening affects consecutive screening performance is important to estimate its future value in screening. Purpose To evaluate whether DBT contributes to early detection of breast cancer by assessing cancer detection rates (CDRs), including the fraction of invasive cancers and cancer subtypes in consecutive routine digital mammography (DM). Materials and Methods The paired prospective Malmö Breast Tomosynthesis Screening Trial (MBTST) was performed between January 2010 and February 2015. Participating women underwent one-view DBT and two-view DM at one screening occasion. In this secondary analysis, women were followed up through their first (DM1) and second (DM2) consecutive two-view DM screening rounds after MBTST participation. Cancer diagnoses were identified by referencing records. A logistic regression model, adjusted for age, was used to calculate the odds of luminal A-like cancers with use of the MBTST as reference. Results There were 14 848 final participants in the MBTST (median age, 57 years [IQR, 49-65 years]). Of those, 12 876 women were screened in DM1 (median age, 58 years [IQR, 50-66 years]) and 10 883 were screened in DM2 (median age, 59 years [IQR, 51-67 years]). Compared with CDRs in the trial of 6.5 of 1000 women (95% CI: 5.2, 7.9) for DM and 8.7 of 1000 women (95% CI: 7.3, 10.3) for DBT, the CDR was lower in DM1 (4.6 of 1000 women [95% CI: 3.6, 5.9]) and DM2 (5.3 of 1000 women [95% CI: 4.1, 6.9]). The proportion of invasive cancers was 84.9% (118 of 139 cancers) in the MBTST; the corresponding numbers were 66% (39 of 59 cancers) for DM1 and 83% (50 of 60 cancers) for DM2. The odds of luminal A-like cancers were lower in DM1 at 0.28 (95% CI: 0.12, 0.66 [P = .004]) but not in DM2 at 0.80 (95% CI: 0.40, 1.58 [P = .52]) versus screening in the MBTST. Conclusion CDR and the fraction of invasive cancers were lower in DM1 and then increased in DM2 following the MBTST, indicating earlier cancer detection mainly due to increased detection of luminal A-like cancers in DBT screening. Clinical trials registration no. NCT01091545 © RSNA, 2024 See also the editorial by Hooley and Philpotts in this issue.

Interval Cancers in Understanding Screening Outcomes

Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.

ACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update

Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer. Numerous factors contribute to breast cancer risk, which is frequently divided into three major categories: average, intermediate, and high risk. For patients assigned female at birth with native breast tissue, mammography and digital breast tomosynthesis are the recommended method for breast cancer screening in all risk categories. In addition to the recommendation of mammography and digital breast tomosynthesis in high-risk patients, screening with breast MRI is recommended. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Discordant and false-negative interpretations at digital breast tomosynthesis in the prospective Oslo Tomosynthesis Screening Trial (OTST) using independent double reading

OBJECTIVES

To analyze discordant and false-negatives of double reading digital breast tomosynthesis (DBT) versus digital mammography (DM) including reading times in the Oslo Tomosynthesis Screening Trial (OTST), and reclassify these in a retrospective reader study as missed, minimal sign, or true-negatives.

METHODS

The prospective OTST comparing double reading DBT vs. DM had paired design with four parallel arms: DM, DM + computer aided detection, DBT + DM, and DBT + synthetic mammography. Eight radiologists interpreted images in batches using a 5-point scale. Reading time was automatically recorded. A retrospective reader study including four radiologists classified screen-detected cancers with at least one false-negative score and screening examinations of interval cancers as negative, non-specific minimal sign, significant minimal sign, and missed; the two latter groups are defined "actionable." Statistics included chi-square, Fisher's exact, McNemar's, and Mann-Whitney U tests.

RESULTS

Discordant rate (cancer missed by one reader) for screen-detected cancers was overall comparable (DBT (31% [71/227]) and DM (30% [52/175]), p = .81), significantly lower at DBT for spiculated cancers (DBT, 19% [20/106] vs. DM, 36% [38/106], p = .003), but high (28/49 = 57%, p = 0.001) for DBT-only detected spiculated cancers. Reading time and sensitivity varied among readers. False-negative DBT-only detected spiculated cancers had shorter reading time than true-negatives in 46% (13/28). Retrospective evaluation classified the following DBT exams "actionable": three missed by both readers, 95% (39/41) of discordant cancers detected by both modes, all 30 discordant DBT-only cancers, 25% (13/51) of interval cancers.

CONCLUSIONS

Discordant rate was overall comparable for DBT and DM, significantly lower at DBT for spiculated cancers, but high for DBT-only detected spiculated lesions. Most false-negative screen-detected DBT were classified as "actionable."

CLINICAL RELEVANCE STATEMENT

Retrospective evaluation of false-negative interpretations from the Oslo Tomosynthesis Screening Trial shows that most discordant and several interval cancers could have been detected at screening. This underlines the potential for modern AI-based reading aids and triage, as high-volume screening is a demanding task.

KEY POINTS

• Digital breast tomosynthesis (DBT) screening is more sensitive and has higher specificity compared to digital mammography screening, but high-volume DBT screening is a demanding task which can result in high discordance rate among readers. • Independent double reading DBT screening had overall comparable discordance rate as digital mammography, lower for spiculated masses seen on both modalities, and higher for small spiculated cancer seen only on DBT. • Almost all discordant digital breast tomosynthesis-detected cancers (72 of 74) and 25% (13 of 51) of the interval cancers in the Oslo Tomosynthesis Screening Trial were retrospectively classified as actionable and could have been detected by the readers.

A Review of Artificial Intelligence in Breast Imaging

With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

False-positive recalls in the prospective Malmö Breast Tomosynthesis Screening Trial

OBJECTIVES

To evaluate the total number of false-positive recalls, including radiographic appearances and false-positive biopsies, in the Malmö Breast Tomosynthesis Screening Trial (MBTST).

METHODS

The prospective, population-based MBTST, with 14,848 participating women, was designed to compare one-view digital breast tomosynthesis (DBT) to two-view digital mammography (DM) in breast cancer screening. False-positive recall rates, radiographic appearances, and biopsy rates were analyzed. Comparisons were made between DBT, DM, and DBT + DM, both in total and in trial year 1 compared to trial years 2 to 5, with numbers, percentages, and 95% confidence intervals (CI).

RESULTS

The false-positive recall rate was higher with DBT, 1.6% (95% CI 1.4; 1.8), compared to screening with DM, 0.8% (95% CI 0.7; 1.0). The proportion of the radiographic appearance of stellate distortion was 37.3% (91/244) with DBT, compared to 24.0% (29/121) with DM. The false-positive recall rate with DBT during trial year 1 was 2.6% (95% CI 1.8; 3.5), then stabilized at 1.5% (95% CI 1.3; 1.8) during trial years 2 to 5. The percentage of stellate distortion with DBT was 50% (19/38) trial year 1 compared to 35.0% (72/206) trial years 2 to 5.

CONCLUSIONS

The higher false-positive recall rate with DBT compared to DM was mainly due to an increased detection of stellate findings. The proportion of these findings, as well as the DBT false-positive recall rate, was reduced after the first trial year.

CLINICAL RELEVANCE STATEMENT

Assessment of false-positive recalls gives information on potential benefits and side effects in DBT screening.

KEY POINTS

• The false-positive recall rate in a prospective digital breast tomosynthesis screening trial was higher compared to digital mammography, but still low compared to other trials. • The higher false-positive recall rate with digital breast tomosynthesis was mainly due to an increased detection of stellate findings; the proportion of these findings was reduced after the first trial year.

Comparing the performance of full-field digital mammography and digital breast tomosynthesis in the post-treatment surveillance of patients with a history of breast cancer: A retrospective study

INTRODUCTION

The purpose of our study was to compare the performance of 2D (FFDM) against 3D (FFDM plus DBT) examinations in the post-treatment surveillance of asymptomatic breast cancer survivors.

METHODS

A list of women with a history of breast cancer who underwent screening mammography (2D or 3D) from 5/2017 to 5/2020 was retrieved. A total of 20,210 examinations were identified and performance metrics were compared.

RESULTS

There were no statistically significant difference in cancer detection rate (CDR) (p = 0.38), recall rate (RR) (p = 0.087), or positive predictive value (PPV) (p = 0.74) between 2D vs. 3D examinations. Stratification by breast tissue identified no statistically significant difference in CDR (p = 0.581 and p = 0.428), RR (p = 0.230 and p = 0.205), or PPV (p = 0.908 and p = 0.721) between fatty/scattered and heterogeneous/extremely dense breast tissue when comparing 2D vs 3D examinations. Stratification by age did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased with 2D vs. 3D examinations in the 60-69 years group (p = 0.021). Stratification by race did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased with 3D vs. 2D examinations in white women (p = 0.036). Stratification by laterality (bilateral vs. unilateral post mastectomy) did not identify a significant difference in RR or PPV between the two groups. CDR was statistically increased in 2D vs. 3D examinations in unilateral studies (p = 0.009).

CONCLUSION

For asymptomatic women with a history of breast cancer, there is no evidence that the addition of DBT to FFDM improves CDR, RR, or PPV.

IMPLICATIONS FOR PRACTICE

More studies are needed concerning screening methodologies supplementing FFDM in the screening regimens of breast cancer survivors.

Comparison of prone and upright, stereotactic, and tomosynthesis-guided biopsies with secondary analysis of ultrasound-occult architectural distortions

OBJECTIVES

Compare prone and upright, stereotactic, and tomosynthesis-guided vacuum-assisted breast biopsies (prone DM-VABB, prone DBT-VABB, upright DM-VABB, and upright DBT-VABB) in a community-practice setting and review outcomes of ultrasound-occult architectural distortions (AD).

METHODS

Consecutive biopsies performed at two community-based breast centers from 2016 to 2019 were retrospectively reviewed. Technical details of each procedure and patient outcomes were recorded. Separate analyses were performed for ultrasound-occult ADs. Two sample t-tests and Fisher's exact test facilitated comparisons.

RESULTS

A total of 1133 patients underwent 369 prone DM-VABB, 324 prone DBT-VABB, 437 upright DM-VABB, and 123 upright DBT-VABB with 99.2%, 100%, 99.3%, and 99.2% success, respectively (p-values > 0.25). Mean lesion targeting times were greater for prone biopsy (minutes: 6.94 prone DM-VABB, 8.54 prone DBT-VABB, 5.52 upright DM-VABB, and 5.51 upright DBT-VABB; p-values < 0.001), yielding longer total prone procedure times for prone biopsy (p < 0.001). Compared to DM-VABB, DBT-VABB used fewer exposures (p < 0.001) and more commonly targeted AD, asymmetries, or masses (p < 0.001). Malignancy rates were similar between procedures: prone DM-VABB 22.4%, prone DBT-VABB 21.9%, upright DM-VABB 22.8%, and upright DBT-VABB 17.2% (p-values > 0.19). One hundred forty of the 1133 patients underwent 145 biopsies for ultrasound-occult AD (143 DBT-VABB and 2 DM-VABB). Biopsy yielded 27 malignancies and 47 high-risk lesions (74 of 145, 51%). Malignancy rate was 20.7% after surgical upgrade of one benign-discordant and two high-risk lesions.

CONCLUSIONS

All biopsy procedure types were extremely successful. The 20.7% malignancy rate for ultrasound-occult AD confirms a management recommendation for tissue diagnosis. Upright biopsy was faster than prone biopsy, and DBT-VABB used fewer exposures than DM-VABB.

CLINICAL RELEVANCE

Our results highlight important differences between prone DM-VABB, prone DBT-VABB, upright DM-VABB, and upright DBT-VABB. Moreover, the high likelihood of malignancy for ultrasound-occult AD will provide confidence in recommending tissue diagnosis in lieu of observation or clinical follow-up.

KEY POINTS

• Upright and prone stereotactic and tomosynthesis-guided breast biopsies were safe and effective in the community-practice setting. • The malignancy rate for ultrasound-occult architectural distortion of 20.7% confirms the management recommendation for biopsy. • Upright procedures were faster than prone procedures, and tomosynthesis-guided biopsy used fewer exposures than stereotactic biopsy.

Comparative Performance of Contrast-enhanced Mammography, Abbreviated Breast MRI, and Standard Breast MRI for Breast Cancer Screening

Background Contrast-enhanced mammography (CEM) and abbreviated breast MRI (ABMRI) are emerging alternatives to standard MRI for supplemental breast cancer screening. Purpose To compare the diagnostic performance of CEM, ABMRI, and standard MRI. Materials and Methods This single-institution, prospective, blinded reader study included female participants referred for breast MRI from January 2018 to June 2021. CEM was performed within 14 days of standard MRI; ABMRI was produced from standard MRI images. Two readers independently interpreted each CEM and ABMRI after a washout period. Examination-level performance metrics calculated were recall rate, cancer detection, and false-positive biopsy recommendation rates per 1000 examinations and sensitivity, specificity, and positive predictive value of biopsy recommendation. Bootstrap and permutation tests were used to calculate 95% CIs and compare modalities. Results Evaluated were 492 paired CEM and ABMRI interpretations from 246 participants (median age, 51 years; IQR, 43-61 years). On 49 MRI scans with lesions recommended for biopsy, nine lesions showed malignant pathology. No differences in ABMRI and standard MRI performance were identified. Compared with standard MRI, CEM demonstrated significantly lower recall rate (14.0% vs 22.8%; difference, -8.7%; 95% CI: -14.0, -3.5), lower false-positive biopsy recommendation rate per 1000 examinations (65.0 vs 162.6; difference, -97.6; 95% CI: -146.3, -50.8), and higher specificity (87.8% vs 80.2%; difference, 7.6%; 95% CI: 2.3, 13.1). Compared with standard MRI, CEM had significantly lower cancer detection rate (22.4 vs 36.6; difference, -14.2; 95% CI: -28.5, -2.0) and sensitivity (61.1% vs 100%; difference, -38.9%; 95% CI: -66.7, -12.5). The performance differences between CEM and ABMRI were similar to those observed between CEM and standard MRI. Conclusion ABMRI had comparable performance to standard MRI and may support more efficient MRI screening. CEM had lower recall and higher specificity compared with standard MRI or ABMRI, offset by lower cancer detection rate and sensitivity compared with standard MRI. These trade-offs warrant further consideration of patient population characteristics before widespread screening with CEM. Clinical trial registration no. NCT03517813 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chang in this issue.

Screening outcome for interpretation by the first and second reader in a population-based mammographic screening program with independent double reading

BACKGROUND

Double reading of screening mammograms is associated with a higher rate of screen-detected cancer than single reading, but different strategies exist regarding reader pairing and blinding. Knowledge about these aspects is important when considering strategies for future use of artificial intelligence in mammographic screening.

PURPOSE

To investigate screening outcome, histopathological tumor characteristics, and mammographic features stratified by the first and the second reader in a population based screening program for breast cancer.

MATERIAL AND METHODS

The study sample consisted of data from 3,499,048 screening examinations from 834,691 women performed during 1996-2018 in BreastScreen Norway. All examinations were interpreted independently by two radiologists, 272 in total. We analyzed interpretation score, recall, and cancer detection, as well as histopathological tumor characteristics and mammographic features of the cancers, stratified by the first and second readers.

RESULTS

For Reader 1, the rate of positive interpretations was 4.8%, recall 2.3%, and cancer detection 0.5%. The corresponding percentages for Reader 2 were 4.9%, 2.5%, and 0.5% (P < 0.05 compared with Reader 1). No statistical difference was observed for histopathological tumor characteristics or mammographic features when stratified by Readers 1 and 2. Recall and cancer detection were statistically higher and histopathological tumor characteristics less favorable for cases detected after concordant positive compared with discordant interpretations.

CONCLUSION

Despite reaching statistical significance, mainly due to the large study sample, we consider the differences in interpretation scores, recall, and cancer detection between the first and second readers to be clinically negligible. For practical and clinical purposes, double reading in BreastScreen Norway is independent.

Stepwise Implementation of 2D Synthesized Screening Mammography and Its Effect on Stereotactic Biopsy of Microcalcifications

RATIONALE AND OBJECTIVES

Information evaluating the efficacy of 2D synthesized mammography (2Ds) reconstructions in microcalcification detection is limited. This study used stereotactic biopsy data for microcalcifications to evaluate the stepwise implementation of 2Ds in screening mammography. The study aim was to identify whether 2Ds + digital breast tomosynthesis (DBT) is non-inferior to 2D digital mammography (2DM) + 2Ds + DBT, 2DM + DBT, and 2DM in identifying microcalcifications undergoing further diagnostic imaging and stereotactic biopsy.

MATERIALS AND METHODS

Retrospective stereotactic biopsy data were extracted following 151,736 screening mammograms of healthy women (average age, 56.3 years; range, 30-89 years), performed between 2012 and 2019. The stereotactic biopsy data were separated into 2DM, 2DM + DBT, 2DM + 2Ds + DBT, and 2Ds + DBT arms and examined using Fisher's exact test to compare the detection rates of all cancers, invasive cancers, DCIS, and ADH between modalities for patients undergoing stereotactic biopsy of microcalcifications.

RESULTS

No statistical significance in cancer detection was seen for 2Ds + DBT among those calcifications that underwent stereotactic biopsy when comparing the 2Ds + DBT to 2DM, 2DM + DBT, and 2DM + 2Ds + DBT imaging combinations.

CONCLUSION

These data suggest that 2Ds + DBT is non-inferior to 2DM + DBT in detecting microcalcifications that will undergo stereotactic biopsy.

Comparing image quality of five breast tomosynthesis systems based on radiologists' reviews of phantom data

BACKGROUND

Previous studies have shown differences in technical image quality between digital breast tomosynthesis (DBT) systems. However, quantitative image quality measurements may not necessarily fully reflect the clinical performance of DBT.

PURPOSE

To study the subjective image quality of five DBT systems manufactured by Fujifilm, GE, Hologic, Planmed, and Siemens using phantom images.

MATERIAL AND METHODS

A TOR MAM test object with polymethyl methacrylate plates was imaged on five DBT systems from different vendors. Three DBT acquisitions were performed at mean glandular doses of 1.0 mGy, 2.0 mGy, and 3.5 mGy while maintaining a constant phantom set-up. Eight DBT acquisitions with different test plate positions and phantom set-up thicknesses were performed at clinically applied dose levels. Additionally, three conventional two-dimensional mammogram images were acquired with different phantom thicknesses. Six radiologists ranked the systems based on the visibilities of the targets seen in the phantom images.

RESULTS

In the DBT acquisitions performed at comparable dose levels, one system differed significantly from all other systems in microcalcification scores. When using site-specific DBT protocols, significant differences were found between the devices for microcalcification, filament, and low-contrast targets. A strong correlation was observed between the reviewer scores and radiation doses in DBT acquisitions, whereas no such correlation was observed in the 2D acquisitions.

CONCLUSION

In DBT acquisitions, dose level was found to be a major factor explaining image quality differences between the systems, regardless of other acquisition parameters. Most DBT systems performed equally well at similar dose levels.

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

OBJECTIVES

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

Artificial-intelligence-enhanced synthetic 6 mm slabs allow for substantial interpretation time reduction in diagnostic DBT without a decrease in reader accuracy.

ADVANCES IN KNOWLEDGE

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.

Supplemental Breast Cancer Screening in Women with Dense Breasts and Negative Mammography: A Systematic Review and Meta-Analysis

Background The best supplemental breast cancer screening modality in women at average risk or intermediate risk for breast cancer with dense breast and negative mammogram remains to be determined. Purpose To conduct systematic review and meta-analysis comparing clinical outcomes of the most common available supplemental screening modalities in women at average risk or intermediate risk for breast cancer in patients with dense breasts and mammography with negative findings. Materials and Methods A comprehensive search was conducted until March 12, 2020, in Medline, Epub Ahead of Print and In-Process and Other Non-Indexed Citations; Embase Classic and Embase; Cochrane Central Register of Controlled Trials; and Cochrane Database of Systematic Reviews, for Randomized Controlled Trials and Prospective Observational Studies. Incremental cancer detection rate (CDR); positive predictive value of recall (PPV1); positive predictive value of biopsies performed (PPV3); and interval CDRs of supplemental imaging modalities, digital breast tomosynthesis, handheld US, automated breast US, and MRI in non-high-risk patients with dense breasts and mammography negative for cancer were reviewed. Data metrics and risk of bias were assessed. Random-effects meta-analysis and two-sided metaregression analyses comparing each imaging modality metrics were performed (PROSPERO; CRD42018080402). Results Twenty-two studies reporting 261 233 screened patients were included. Of 132 166 screened patients with dense breast and mammography negative for cancer who met inclusion criteria, a total of 541 cancers missed at mammography were detected with these supplemental modalities. Metaregression models showed that MRI was superior to other supplemental modalities in CDR (incremental CDR, 1.52 per 1000 screenings; 95% CI: 0.74, 2.33; P < .001), including invasive CDR (invasive CDR, 1.31 per 1000 screenings; 95% CI: 0.57, 2.06; P < .001), and in situ disease (rate of ductal carcinoma in situ, 1.91 per 1000 screenings; 95% CI: 0.10, 3.72; P < .04). No differences in PPV1 and PPV3 were identified. The limited number of studies prevented assessment of interval cancer metrics. Excluding MRI, no statistically significant difference in any metrics were identified among the remaining imaging modalities. Conclusion The pooled data showed that MRI was the best supplemental imaging modality in women at average risk or intermediate risk for breast cancer with dense breasts and mammography negative for cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Hooley and Butler in this issue.

Performance of Wide-Angle Tomosynthesis with Synthetic Mammography in Comparison to Full Field Digital Mammography

RATIONALE AND OBJECTIVES

The purpose of this study was to test for superiority of wide-angle digital breast tomosynthesis plus synthetic mammography (Insight 2D) in comparison to full-field digital mammography (FFDM).

MATERIALS AND METHODS

In this study, twenty readers interpreted 350 screening and diagnostic cases of wide-angle digital breast tomosynthesis (DBT) plus Insight 2D and FFDM in two separate reading sessions separated by at least a 6-week washout period. Breast-level estimates of the area under the curve and sensitivity along with subject-level recall rate were measured and compared between wide-angle DBT plus Insight 2D and FFDM. The same measures were also assessed for dense breasts. A hierarchical analysis plan was used to control the study's type I error rate at 0.05.

RESULTS

The mean breast-level area under the curve for distinguishing breasts with cancer from non-cancer breasts was 0.893 with DBT plus Insight 2D versus 0.837 with FFDM, showing superiority of DBT plus Insight 2D (p < 0.001). Breast-level sensitivity was significantly superior for DBT plus Insight 2D in comparison to FFDM (0.852 vs. 0.805, p = 0.043). Subject-level recall rate for DBT plus Insight 2D was significantly lower in comparison to FFDM (0.344 vs. 0.473, p < 0.001). For dense breasts, the readers' accuracy with DBT plus Insight 2D was superior to their accuracy with FFDM (0.875 vs. 0.830, p = 0.026), and their recall rate was significantly lower for DBT plus Insight 2D in comparison to FFDM (0.338 vs. 0.441, p = 0.003).

CONCLUSION

Reader performance with wide-angle DBT plus Insight 2D is superior to that with FFDM, showing significantly higher breast-level accuracy and sensitivity and significantly lower recall rates.

Breast Cancer Screening in Women With Dense Breasts: Current Status and Future Directions for Appropriate Risk Stratification and Imaging Utilization

Breast density continues to be a prevailing topic in the field of breast imaging, with continued complexities contributing to overall confusion and controversy among patients and the medical community. In this article, we explore the current status of breast cancer screening in women with dense breasts including breast density legislation. Risk-based approaches to supplemental screening may be more financially cost-effective. While all advanced imaging modalities detect additional primarily invasive, node-negative cancers, the degree to which this occurs can vary by density category. Future directions include expanding the use of density-inclusive risk models with appropriate risk stratification and imaging utilization. Further research is needed, however, to better understand how to optimize population-based screening programs with knowledge of patients' individualized risk, including breast density assessment, to improve the benefit-to-harm ratio of breast cancer screening.

Performance evaluation of digital mammography, digital breast tomosynthesis and ultrasound in the detection of breast cancer using pathology as gold standard: an institutional experience

Background

Mammography is the primary imaging modality for diagnosing breast cancer in women more than 40 years of age. Digital breast tomosynthesis (DBT), when supplemented with digital mammography (DM), is useful for increasing the sensitivity and improving BIRADS characterization by removing the overlapping effect. Ultrasonography (US), when combined with the above combination, further increases the sensitivity and diagnostic confidence. Since most of the research regarding tomosynthesis has been in screening settings, we wanted to quantify its role in diagnostic mammography. The purpose of this study was to assess the performance of DM alone vs. DM combined with DBT vs. DM plus DBT and ultrasound in diagnosing malignant breast neoplasms with the gold standard being histopathology or cytology.

Results

A prospective study of 1228 breasts undergoing diagnostic or screening mammograms was undertaken at our institute. Patients underwent 2 views DM, single view DBT and US. BIRADS category was updated after each step. Final categorization was made with all three modalities combined and pathological correlation was done for those cases in which suspicious findings were detected, i.e. 256 cases. Diagnosis based on pathology was done for 256 cases out of which 193 (75.4%) were malignant and the rest 63 (24.6%) were benign. The diagnostic accuracy of DM alone was 81.1%. Sensitivity, Specificity, PPV and NPV were 87.8%, 60%, 81.3% and 61.1%, respectively. With DM + DBT the diagnostic accuracy was 84.8%. Sensitivity, Specificity, PPV and NPV were 92%, 56.5%, 89% and 65%, respectively. The diagnostic accuracy of DM + DBT + US was found to be 85.1% and Sensitivity, Specificity, PPV and NPV were 96.3%, 50.7%, 85.7% and 82%, respectively.

Conclusion

The combination of DBT to DM led to higher diagnostic accuracy, sensitivity and PPV. The addition of US to DM and DBT further increased the sensitivity and diagnostic accuracy and significantly increased the NPV even in diagnostic mammograms and should be introduced in routine practice for characterizing breast neoplasms.

Performance evaluation of digital breast tomosynthesis systems: physical methods and experimental data

Digital breast tomosynthesis (DBT) has become a well-established breast imaging technique, whose performance has been investigated in many clinical studies, including a number of prospective clinical trials. Results from these studies generally point to non-inferiority in terms of microcalcification detection and superior mass-lesion detection for DBT imaging compared to digital mammography (DM). This modality has become an essential tool in the clinic for assessment and ad-hoc screening but is not yet implemented in most breast screening programmes at a state or national level. While evidence on the clinical utility of DBT has been accumulating, there has also been progress in the development of methods for technical performance assessment and quality control of these imaging systems. DBT is a relatively complicated ‘pseudo-3D’ modality whose technical assessment poses a number of difficulties. This paper reviews methods for the technical performance assessment of DBT devices, starting at the component level in part one and leading up to discussion of system evaluation with physical test objects in part two. We provide some historical and basic theoretical perspective, often starting from methods developed for DM imaging. Data from a multi-vendor comparison are also included, acquired under the medical physics quality control protocol developed by EUREF and currently being consolidated by a European Federation of Organisations for Medical Physics working group. These data and associated methods can serve as a reference for the development of reference data and provide some context for clinical studies.

Breast cancer diagnosis using frequency decomposition of surface motion of actuated breast tissue

This paper presents a computationally simple diagnostic algorithm for breast cancer using a non-invasive Digital Image Elasto Tomography (DIET) system. N=14 women (28 breasts, 13 cancerous) underwent a clinical trial using the DIET system following mammography diagnosis. The screening involves steady state sinusoidal vibrations applied to the free hanging breast with cameras used to capture tissue motion. Image reconstruction methods provide surface displacement data for approximately 14,000 reference points on the breast surface. The breast surface was segmented into four radial and four vertical segments. Frequency decomposition of reference point motion in each segment were compared. Segments on the same vertical band were hypothesised to have similar frequency content in healthy breasts, with significant differences indicating a tumor, based on the stiffness dependence of frequency and tumors being 4~10 times stiffer than healthy tissue. Twelve breast configurations were used to test robustness of the method. Optimal breast configuration for the 26 breasts analysed (13 cancerous, 13 healthy) resulted in 85% sensitivity and 77% specificity. Combining two opposite configurations resulted in correct diagnosis of all cancerous breasts with 100% sensitivity and 69% specificity. Bootstrapping was used to fit a smooth receiver operator characteristic (ROC) curve to compare breast configuration performance with optimal area under the curve (AUC) of 0.85. Diagnostic results show diagnostic accuracy is comparable or better than mammography, with the added benefits of DIET screening, including portability, non-invasive screening, and no breast compression, with potential to increase screening participation and equity, improving outcomes for women.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Breast dosimetry in alternative X-ray-based imaging modalities used in current clinical practices

In X-ray breast imaging, Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT), are the standard and largely used techniques, both for diagnostic and screening purposes. Other techniques, such as dedicated Breast Computed Tomography (BCT) and Contrast Enhanced Mammography (CEM) have been developed as an alternative or a complementary technique to the established ones. The performance of these imaging techniques is being continuously assessed to improve the image quality and to reduce the radiation dose. These imaging modalities are predominantly used in the diagnostic setting to resolve incomplete or indeterminate findings detected with conventional screening examinations and could potentially be used either as an adjunct or as a primary screening tool in select populations, such as for women with dense breasts. The aim of this review is to describe the radiation dosimetry for these imaging techniques, and to compare the mean glandular dose with standard breast imaging modalities, such as DM and DBT.

Does residual ultrasound transmission gel affect the diagnostic ability of mammography?

This study aimed to assess whether residual ultrasound transmission gel (USTG) caused artifacts in mammography using a model 156 mammographic accreditation phantom and step phantom. Moreover, pig tissues with structures similar to those of the breast were imaged to assess whether USTG on the tissue appeared as a shadow on the mammogram, and how these shadows may be interpreted in clinical practice. The results showed that the visualization scores obtained for phantom mammograms decreased significantly for the fiber and mass samples after the application of USTG. Moreover, USTG on the tissues affected the visual evaluation of mammograms, leading to misinterpretation of mammographic findings.

Shape-Based Breast Lesion Classification Using Digital Tomosynthesis Images: The Role of Explainable Artificial Intelligence

Computer-aided diagnosis (CAD) systems can help radiologists in numerous medical tasks including classification and staging of the various diseases. The 3D tomosynthesis imaging technique adds value to the CAD systems in diagnosis and classification of the breast lesions. Several convolutional neural network (CNN) architectures have been proposed to classify the lesion shapes to the respective classes using a similar imaging method. However, not only is the black box nature of these CNN models questionable in the healthcare domain, but so is the morphological-based cancer classification, concerning the clinicians. As a result, this study proposes both a mathematically and visually explainable deep-learning-driven multiclass shape-based classification framework for the tomosynthesis breast lesion images. In this study, authors exploit eight pretrained CNN architectures for the classification task on the previously extracted regions of interests images containing the lesions. Additionally, the study also unleashes the black box nature of the deep learning models using two well-known perceptive explainable artificial intelligence (XAI) algorithms including Grad-CAM and LIME. Moreover, two mathematical-structure-based interpretability techniques, i.e., t-SNE and UMAP, are employed to investigate the pretrained models’ behavior towards multiclass feature clustering. The experimental results of the classification task validate the applicability of the proposed framework by yielding the mean area under the curve of 98.2%. The explanability study validates the applicability of all employed methods, mainly emphasizing the pros and cons of both Grad-CAM and LIME methods that can provide useful insights towards explainable CAD systems.

Comparison of breast density assessments between synthesized C-View™ & intelligent 2D™ mammography

OBJECTIVE

To compare breast density assessments between C-View™ and Intelligent 2D™, different generations of synthesized mammography (SM) from Hologic.

METHODS

In this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical Breast Imaging Reporting and Database System breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using VolparaTM. Differences in density measures by type of synthesized image were calculated using the pairwise t-test or McNemar's test, as appropriate.

RESULTS

67 patients (avg age 62.7; range 40-84) were included with an average of 13.3 months between the two exams. No difference was found in Breast Imaging Reporting and Database System density between the SM reconstructions (p = 0.74). Similarly, there was no difference in VolparaTM mean density grade (p = 0.71), mean breast volume (p = 0.48), mean dense volume (p = 0.43) or mean percent volumetric density (p = 0.12) between the exams.

CONCLUSION

We found no significant differences in clinical and automated breast density assessments between these two versions of SM.

ADVANCES IN KNOWLEDGE

Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening

The purpose of breast cancer screening is to find cancers early to reduce mortality and to allow successful treatment with less aggressive therapy. Mammography is the gold standard for breast cancer screening. Its efficacy in reducing mortality from breast cancer was proven in randomized controlled trials (RCTs) conducted from the early 1960s to the mid 1990s. Panels that recommend breast cancer screening guidelines have traditionally relied on the old RCTs, which did not include considerations of breast density, race/ethnicity, current hormone therapy, and other risk factors. Women do not all benefit equally from mammography. Mortality reduction is significantly lower in women with dense breasts because normal dense tissue can mask cancers on mammograms. Moreover, women with dense breasts are known to be at increased risk. To provide equity, breast cancer screening guidelines should be created with the goal of maximizing mortality reduction and allowing less aggressive therapy, which may include decreasing the interval between screening mammograms and recommending consideration of supplemental screening for women with dense breasts. This review will address the issue of dense breasts and the impact on the stage of breast cancer at the time of diagnosis, and discuss options for supplemental screening.

A Randomized Trial Comparing Breast Cancer Incidence and Interval Cancers after Tomosynthesis Plus Mammography versus Mammography Alone

Background Adding digital breast tomosynthesis (DBT) to digital mammography (DM) improves breast cancer screening sensitivity, but how this impacts mortality and other end points is unknown. Purpose To compare interval and overall breast cancer incidence after screening with DBT plus DM versus DM alone. Materials and Methods In this prospective trial (RETomo), women attending screening were randomized to one round of DBT plus DM (experimental arm) or to DM (control arm). All were then rescreened with DM after 12 months (women aged 45-49 years) or after 24 months (50-69 years). The primary outcome was interval cancer incidence. Cumulative incidence up to the subsequent screening round plus 9 months (21- and 33-month follow-up for women aged 45-49 and 50-69, respectively) was also reported. Ductal carcinomas in situ are included. Subgroup analyses by age and breast density were conducted; 95% CIs computed according to binomial distribution are reported. Results Baseline cancer detection was higher in the DBT plu DM arm than DM arm (101 of 13 356 women vs 61 of 13 521 women; relative detection, 1.7 [95% CI: 1.2, 2.3]). The mean age ± standard deviation for the women in both arms was 55 years ± 7. Interval cancer incidence was similar in the two arms (21 vs 22 cancers; relative incidence, 0.97 [95% CI: 0.53, 1.8]). Cumulative incidence remained higher in the DBT plus DM arm in women over 50 (153 vs 124 cancers; relative incidence, 1.2 [95% CI: 0.99, 1.6]), while it was similar in the two arms in women aged 45-49 (36 vs 41 cancers; relative incidence, 0.89 [95% CI: 0.57, 1.4]). Conclusion In women younger than 50 years, the benefit of early diagnosis seemed to be appreciable, while for women over age 50, the higher sensitivity of tomosynthesis plus mammography was not matched by a subsequent reduction in cancers at the next screening examination or in the intervening interval. Clinical trial registration no. NCT02698202 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Ray in this issue.

Single Center Evaluation of Comparative Breast Radiation dose of Contrast Enhanced Digital Mammography (CEDM), Digital Mammography (DM) and Digital Breast Tomosynthesis (DBT)

RATIONALE AND OBJECTIVES

The aim of this retrospective study is to compare the radiation dose received during CEDM, short and long protocol (CEDM SP and CEDM LP), with dose received during DM and DBT on patients with varying breast thickness, age and density.

MATERIALS AND METHODS

Between January 2019 and December 2019, patients having 6214 DM, 3662 DBT and 173 CEDM examinations in our department were analyzed. Protocol total single breast AGD has been evaluated for all clinical imaging protocols, extracting AGD values and exposure data from the dose DICOM Structured Report (SR) information stored in the hospital PACS system. Protocol AGD was calculated as the sum of single projection AGDs carried out in every exam for each clinical protocol. A total amount of 23,383 exams for each breast were analyzed. Protocol AGDs, stratified as a function of patient breast compression thickness, age, and breast density were assessed.

RESULTS

The total protocol AGD median values for each protocol are: 2.8 mGy for DM, 3.2 mGy for DBT, 6.0 mGy for DM+DBT, 4.5 mGy for CEDM SP, 7.4 mGy for CEDM SP_DBT (CEDM SP protocol with DBT), 8.4 mGy for CEDM LP and 11.6 mGy for CEDM LP_DBT (CEDM LP protocol with DBT). CEDM SP AGD median value is 59% higher than DM AGD median value and 40% lesser than DM+DBT AGD median; this last difference was statistically confirmed with a p-value <0.001. AGD value for each standard breast CEDM SP projection results to be below 3-mGy limit. AGD value for each standard breast CEDM SP projection results to be below 3 mGy, as required by international legislation. For dense breasts, the AGD median value is 4.2 mGy, with the first and third quartile of 3.3 mGy and 6.0 mGy respectively; for non-dense breasts, the AGD median value is 4.7 mGy, with first and third quartile of 3.5 mGy and 6.3 mGy respectively. The difference between the two groups was statistically tested and confirmed, with a p-value of 0.039.

CONCLUSION

CEDM SP results in higher radiation exposure compared with conventional DM and DBT but lower than the Combo mode. The dose administered during the CEDM SP is lower in patients with dense breasts regardless of their size. An interesting outcome, considering the ongoing studies on CEDM screening in patients with dense breasts.

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature.

True and Missed Interval Cancer in Organized Mammographic Screening: A Retrospective Review Study of Diagnostic and Prior Screening Mammograms

RATIONALE AND OBJECTIVES

To explore radiological aspects of interval breast cancer in a population-based screening program.

MATERIALS AND METHODS

We performed a consensus-based informed review of mammograms from diagnosis and prior screening from women diagnosed with interval cancer 2004-2016 in BreastScreen Norway. Cases were classified as true (no findings on prior screening mammograms), occult (no findings at screening or diagnosis), minimal signs (minor/non-specific findings) and missed (obvious findings). We analyzed mammographic findings, density, time since prior screening, and histopathological characteristics between the classification groups.

RESULTS

The study included 1010 interval cancer cases. Mean age at diagnosis was 61 years (SD = 6), mean time between screening and diagnosis 14 months (SD = 7). A total of 48% (479/1010) were classified as true or occult, 28% (285/1010) as minimal signs and 24% (246/1010) as missed. We observed no differences in mammographic density between the groups, except from a higher percentage of dense breasts in women with occult cancer. Among cancers classified as missed, about 1/3 were masses and 1/3 asymmetries at prior screening. True interval cancers were diagnosed later in the screening interval than the other classification categories. No differences in histopathological characteristics were observed between true, minimal signs and missed cases.

CONCLUSION

In an informed review, 24% of the interval cancers were classified as missed based on visibility and mammographic findings on prior screening mammograms. Three out of four true interval cancers were diagnosed in the second year of the screening interval. We observed no statistical differences in histopathological characteristics between true and missed interval cancers.

A novel few-views arrangement of the fixed X-ray tubes for tomosynthesis

PURPOSE

Tomosynthesis is a technique that reconstructs a volume image from limited-angle projection data. In conventional tomosynthesis, the examination time is long, so it can be difficult for patients to hold their breath during certain examinations, such as chest imaging. Few-views tomosynthesis, which uses a linear arrangement of fixed X-ray tubes and enables an image to be obtained within 1 s, was found to be useful in the clinical setting in our previous study. In the present study, we attempted to develop a novel few-views tomosynthesis system that can obtain images with an improved image quality.

METHODS

A novel few-views arrangement of X-ray tubes was proposed and the image reconstruction method with regularization term was applied. The linear arrangement was used for the X-ray tube arrangement in our previous few-views tomosynthesis, in contrast, a circular arrangement was proposed in this study. The validation of this system was conducted with a numerical simulation and a real data experiment.

RESULTS

The wider the scan angle, the more the object shadow spreads from "in-plane", allowing for artifact suppression. In the circular arrangement, the constant scan angle of θ is used, but in the linear arrangement the scan angle is set from 0 to θ. The artifacts in "out-of-plane" were more strongly suppressed in the circular arrangement than in the linear arrangement.

CONCLUSIONS

Artifacts spreading in the z-direction were more strongly suppressed using the circular arrangement than the linear arrangement. Therefore, the circular arrangement was deemed appropriate for few-views tomosynthesis.

Three-dimensional tomosynthesis versus two-dimensional mammography in detection and characterization of different breast lesions

Background

Breast cancer is considered the most serious lesion among different breast lesions. Mammography is the corner stone for screening for detection of breast cancer. It has been modified to digital mammography (DM) and then to digital breast tomosynthesis (DBT). Tomosynthesis is an emerging technique for diagnosis and screening of breast lesions.

The aim of this study is to interrogate whether the addition of DBT to DM helps in better detection and characterization of different breast lesions.

Methods

This is a prospective study carried on 38 female patients according to our inclusion criteria. All patients were evaluated by using DM alone and thereafter with the addition of DBT to DM. Recall rate was calculated, and the imaging findings of each case were correlated with the final diagnosis and follow-up.

Results

DM identified 32 lesions while DBT with DM identified 37 lesions. On DM alone, 17 lesions were characterized as masses, 5 as focal asymmetry, 2 as architectural distortion, 7 as microcalcification and 1 as macrocalcification. With the addition of DBT, 27 lesions were characterized as masses, 1 as focal asymmetry, 1 as architectural distortion, 7 as microcalcification and 1 as macrocalcification. So, there were better detection and characterization of lesions with the addition of DBT than DM alone. The sensitivity, specificity, AUC, positive and negative predictive values were significantly higher with the addition of DBT to DM (100%, 90.5%, 0.952, 90% and 100%, respectively) than with DM (77.8%, 80.9%, 0.794, 77.8% and 80.9%, respectively) for all breast lesions.

Conclusions

The addition of DBT to DM helps in better detection and characterization of different breast lesions. This leads to early detection of breast cancer, improvement of the performance of radiologists and saving time by reduction of recall rate.

Comparing the diagnostic efficacy of digital breast tomosynthesis with full-field digital mammography using BI-RADS scoring

Background

Mammography has been the mainstay for the detection of breast cancer over decades. It has gradually advanced from screen film to full-field digital mammography. Tomosynthesis has evolved as advanced imaging for early diagnosis of breast lesions with a promising role in both diagnostic and screening settings, particularly in dense and treated breasts.

Results

This study included 90 female patients according to our inclusion criteria. All patients perform full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) and were classified according to breast density and age groups. Breast imaging reporting and data system (BI-RADS) scoring was assigned for each case. This was correlated with the final diagnosis; the diagnostic indices of mammography were a sensitivity of 64.44%, a specificity of 77.78%, a positive predictive value (PPV) 74.63%, a negative predictive value (NPV) of 68.63%, and a diagnostic accuracy of 71.11%. Diagnostic indices of DBT were a sensitivity of 100%, a specificity of 97.77%, PPV 97.78%, NPV 100%, and diagnostic accuracy of 97.7%.

In patients with dense breasts American College of Radiology (ACR) (c and d), 61% of cases had changed their BIRADS scoring with the addition of tomosynthesis. Yet, in non-dense breast ACR (a and b), 45% of cases had changed BIRADS scoring with the addition of DBT to FFDM.

Conclusion

DBT is a promising imaging modality offering better detection and characterization of different breast abnormalities, especially in young females, and those with dense breasts with an increase of sensitivity and specificity than FFDM. This leads to a reduction in the recalled cases, negative biopsies, and assessing the efficacy of therapy as it enables improving detection of breast cancer and different breast lesions not visualized by conventional mammography

Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

RATIONALE AND OBJECTIVES

Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival.

MATERIALS AND METHODS

PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria.

RESULTS

Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed.

CONCLUSION

The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.