One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection: do we need more?

Digital breast tomosynthesis in mammographic screening: false negative cancer cases in the To-Be 1 trial

OBJECTIVES

The randomized controlled trial comparing digital breast tomosynthesis and synthetic 2D mammograms (DBT + SM) versus digital mammography (DM) (the To-Be 1 trial), 2016-2017, did not result in higher cancer detection for DBT + SM. We aimed to determine if negative cases prior to interval and consecutive screen-detected cancers from DBT + SM were due to interpretive error.

METHODS

Five external breast radiologists performed the individual blinded review of 239 screening examinations (90 true negative, 39 false positive, 19 prior to interval cancer, and 91 prior to consecutive screen-detected cancer) and the informed consensus review of examinations prior to interval and screen-detected cancers (n = 110). The reviewers marked suspicious findings with a score of 1-5 (probability of malignancy). A case was false negative if ≥ 2 radiologists assigned the cancer site with a score of ≥ 2 in the blinded review and if the case was assigned as false negative by a consensus in the informed review.

RESULTS

In the informed review, 5.3% of examinations prior to interval cancer and 18.7% prior to consecutive round screen-detected cancer were considered false negative. In the blinded review, 10.6% of examinations prior to interval cancer and 42.9% prior to consecutive round screen-detected cancer were scored ≥ 2. A score of ≥ 2 was assigned to 47.8% of negative and 89.7% of false positive examinations.

CONCLUSIONS

The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT + SM versus DM in the To-Be 1 trial is complex and not due to interpretive error alone.

CRITICAL RELEVANCE STATEMENT

The randomized controlled trial on digital breast tomosynthesis and synthetic 2D mammograms (DBT) and digital mammography (DM), 2016-2017, showed no difference in cancer detection for the two techniques. The rates of false negative screening examinations prior to interval and consecutive screen-detected cancer for DBT were consistent with the rates in prior DM reviews, indicating that the non-superior DBT performance in the trial might not be due to interpretive error alone.

KEY POINTS

• Screening with digital breast tomosynthesis (DBT) did not result in a higher breast cancer detection rate compared to screening with digital mammography (DM) in the To-Be 1 trial. • The false negative rates for examinations prior to interval and consecutive screen-detected cancer for DBT were determined in the trial to test if the lack of differences was due to interpretive error. • The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT versus DM was complex and not due to interpretive error alone.

Artificial intelligence for digital breast tomosynthesis: Impact on diagnostic performance, reading times, and workload in the era of personalized screening

The ultimate goals of the application of artificial intelligence (AI) to digital breast tomosynthesis (DBT) are the reduction of reading times, the increase of diagnostic performance, and the reduction of interval cancer rates. In this review, after outlining the journey from computer-aided detection/diagnosis systems to AI applied to digital mammography (DM), we summarize the results of studies where AI was applied to DBT, noting that long-term advantages of DBT screening and its crucial ability to decrease the interval cancer rate are still under scrutiny. AI has shown the capability to overcome some shortcomings of DBT in the screening setting by improving diagnostic performance and by reducing recall rates (from -2 % to -27 %) and reading times (up to -53 %, with an average 20 % reduction), but the ability of AI to reduce interval cancer rates has not yet been clearly investigated. Prospective validation is needed to assess the cost-effectiveness and real-world impact of AI models assisting DBT interpretation, especially in large-scale studies with low breast cancer prevalence. Finally, we focus on the incoming era of personalized and risk-stratified screening that will first see the application of contrast-enhanced breast imaging to screen women with extremely dense breasts. As the diagnostic advantage of DBT over DM was concentrated in this category, we try to understand if the application of AI to DM in the remaining cohorts of women with heterogeneously dense or non-dense breast could close the gap in diagnostic performance between DM and DBT, thus neutralizing the usefulness of AI application to DBT.

Comparison of Diagnostic Test Accuracy of Cone-Beam Breast Computed Tomography and Digital Breast Tomosynthesis for Breast Cancer: A Systematic Review and Meta-Analysis Approach

BACKGROUND

Cone-beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) remain the main 3D modalities for X-ray breast imaging. This study aimed to systematically evaluate and meta-analyze the comparison of diagnostic accuracy of CBBCT and DBT to characterize breast cancers.

METHODS

Two independent reviewers identified screening on diagnostic studies from 1 January 2015 to 30 December 2021, with at least reported sensitivity and specificity for both CBBCT and DBT. A univariate pooled meta-analysis was performed using the random-effects model to estimate the sensitivity and specificity while other diagnostic parameters like the area under the ROC curve (AUC), positive likelihood ratio (LR+), and negative likelihood ratio (LR-) were estimated using the bivariate model.

RESULTS

The pooled sensitivity specificity, LR+ and LR- and AUC at 95% confidence interval are 86.7% (80.3-91.2), 87.0% (79.9-91.8), 6.28 (4.40-8.96), 0.17 (0.12-0.25) and 0.925 for the 17 included studies in DBT arm, respectively, while, 83.7% (54.6-95.7), 71.3% (47.5-87.2), 2.71 (1.39-5.29), 0.20 (0.04-1.05), and 0.831 are the pooled sensitivity specificity, LR+ and LR- and AUC for the five studies in the CBBCT arm, respectively.

CONCLUSIONS

Our study demonstrates that DBT shows improved diagnostic performance over CBBCT regarding all estimated diagnostic parameters; with the statistical improvement in the AUC of DBT over CBBCT. The CBBCT might be a useful modality for breast cancer detection, thus we recommend more prospective studies on CBBCT application.

Comparison of two-view versus single-view digital breast tomosynthesis and 2D-mammography in breast cancer surveillance imaging

PURPOSE

Limited work has been performed for the implementation of digital breast tomosynthesis (DBT) in breast cancer surveillance imaging. The aim of this study was to investigate the differences between two different DBT implementations in breast cancer surveillance imaging, for patients with a personal history of breast cancer.

METHOD

The DBT implementations investigated were: (1) 2-view 2D digital mammography and 2-view DBT (2vDM&2vDBT) (2) 1-view (cranial-caudal) DM and 1-view (mediolateral-oblique) DBT (1vDM&1vDBT). Clinical performance of these two implementations was assessed retrospectively using observer studies with 118 sets of real patient images, from a single imaging centre, and six observers. Sensitivity, specificity and area under the curve (AUC) using the Jack-knife alternative free-response receiver operating characteristics (JAFROC) analysis were evaluated.

RESULTS

Results suggest that the two DBT implementations are not significantly different in terms of sensitivity, specificity and AUC. When looking at the two main different lesion types, non-calcifications and calcifications, and two different density levels, no difference in the performance of the two DBT implementations was found.

CONCLUSIONS

Since 1vDM&1vDBT exposes the patient to half the dose of 2vDM&2vDBT, it might be worth considering 1vDM&1vDBT in breast cancer surveillance imaging. However, larger studies are required to conclude on this matter.

One view or two views for wide-angle tomosynthesis with synthetic mammography in the assessment setting?

Objectives

To evaluate the diagnostic performance in the assessment setting of three protocols: one-view wide-angle digital breast tomosynthesis (WA-DBT) with synthetic mammography (SM), two-view WA-DBT/SM, and two-view digital mammography (DM).

Methods

Included in this retrospective study were patients who underwent bilateral two-view DM and WA-DBT. SM were reconstructed from the WA-DBT data. The standard of reference was histology and/or 2 years follow-up. Included were 205 women with 179 lesions (89 malignant, 90 benign). Four blinded readers randomly evaluated images to assess density, lesion type, and level of suspicion according to BI-RADS. Three protocols were evaluated: two-view DM, one-view (mediolateral oblique) WA-DBT/SM, and two-view WA-DBT/SM. Detection rate, sensitivity, specificity, and accuracy were calculated and compared using multivariate analysis. Reading time was assessed.

Results

The detection rate was higher with two-view WA-DBT/SM (p = 0.063). Sensitivity was higher for two-view WA-DBT/SM compared to two-view DM (p = 0.001) and one-view WA-DBT/SM (p = 0.058). No significant differences in specificity were found. Accuracy was higher with both one-view WA-DBT/SM and two-view WA-DBT/SM compared to DM (p = 0.003 and > 0.001, respectively). Accuracy did not differ between one- and two-view WA-DBT/SM. Two-view WA-DBT/SM performed better for masses and asymmetries. Reading times were significantly longer when WA-DBT was evaluated.

Conclusions

One-view and two-view WA-DBT/SM can achieve a higher diagnostic performance compared to two-view DM. The detection rate and sensitivity were highest with two-view WA-DBT/SM. Two-view WA-DBT/SM appears to be the most appropriate tool for the assessment of breast lesions.

Key Points

• Detection rate with two-view wide-angle digital breast tomosynthesis (WA-DBT) is significantly higher than with two-view digital mammography in the assessment setting.

• Diagnostic accuracy of one-view and two-view WA-DBT with synthetic mammography (SM) in the assessment setting is higher than that of two-view digital mammography.

• Compared to one-view WA-DBT with SM, two-view WA-DBT with SM seems to be the most appropriate tool for the assessment of breast lesions.

Reader performances in breast lesion characterization via DBT: One or two views and which view?

PURPOSE

To compare the performance in breast lesion characterization of one-view mediolateral (MLO) digital mammography plus digital breast tomosynthesis (DM-DBT) versus one-view craniocaudal (CC) DM-DBT versus two-view DM-DBT.

MATERIALS AND METHODS

The institutional review board approved this retrospective study conducted on 138 women from the population of a previous prospective multicenter study, with 69 consecutive patients with benign or high-risk lesions and 69 randomized patients with breast cancer, all confirmed at pathology. Four radiologists (two senior and two junior) blinded to the clinical, mammographic and pathological data independently reviewed the MLO DM-DBT views, the CC DM-DBT views and the MLO + CC DM-DBT views using the American College of Radiology Breast Imaging-Reporting and Data System criteria for index lesion characterization. Areas under the receiver were calculated and compared for each reader and imaging protocol.

RESULTS

No significant differences in breast cancer characterization were observed between single MLO and CC views for all the readers. The added value of a second view was statistically significant for characterization in pooled data and for junior readers but not for senior readers (p ranging from 0.15 to 0.57 depending on the view and the senior reader). Finally, in 4 breast cancer cases, lesions were only detectable on the CC DM-DBT view in two cases and on the MLO DM-DBT view in the two other cases.

CONCLUSION

Our results support the use of two-view DM-DBT for breast lesion characterization when the readers are inexperienced. There is no significant difference between CC and MLO views when diagnosis is performed with one view.

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.

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

Objectives

Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist.

Methods

A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance.

Results

On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115).

Conclusions

Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system.

Key Points

• Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time.

• The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams.

• The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

Are artificial intelligence systems useful in breast cancer screening programs?

Population-based breast cancer screening programs are efficacious in reducing the mortality due to breast cancer. These programs use mammography to screen the women who are invited to participate. Digital mammography makes it possible to develop computer-assisted diagnosis (CAD) systems that promise to reduce the workload of radiologists participating in screening programs. However, various studies have shown that CAD results in a high rate of false positive diagnoses. Systems based on artificial intelligence are being more widely implemented, and studies have shown that these systems have better diagnostic performance than traditional CAD systems. This article explains the fundamentals of artificial intelligence systems and an overview of possible applications of these systems within the framework of breast cancer screening programs.

Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment

This study objectively evaluates the similarity between standard full-field digital mammograms and two-dimensional synthesized digital mammograms (2DSM) in a cohort of women undergoing mammography. Under an institutional review board-approved data collection protocol, we retrospectively analyzed 407 women with digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) examinations performed from September 1, 2014, through February 29, 2016. Both FFDM and 2DSM images were used for the analysis, and 3216 available craniocaudal (CC) and mediolateral oblique (MLO) view mammograms altogether were included in the dataset. We analyzed the mammograms using a fully automated algorithm that computes 152 structural similarity, texture, and mammographic density-based features. We trained and developed two different global mammographic image feature analysis-based breast cancer detection schemes for 2DSM and FFDM images, respectively. The highest structural similarity features were obtained on the coarse Weber Local Descriptor differential excitation texture feature component computed on the CC view images (0.8770) and MLO view images (0.8889). Although the coarse structures are similar, the global mammographic image feature-based cancer detection scheme trained on 2DSM images outperformed the corresponding scheme trained on FFDM images, with area under a receiver operating characteristic curve (AUC) = 0.878 ± 0.034 and 0.756 ± 0.052, respectively. Consequently, further investigation is required to examine whether DBT can replace FFDM as a standalone technique, especially for the development of automated objective-based methods.

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

BACKGROUND

Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.

METHODS

Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.

RESULTS

The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.

CONCLUSIONS

The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

Evaluation of average glandular dose and investigation of the relationship with compressed breast thickness in dual energy contrast enhanced digital mammography and digital breast tomosynthesis

PURPOSE

To quantitatively assess the dose of Dual energy contrast enhanced digital mammography (CEDM) and digital breast tomosynthesis (DBT) and to investigate the relationship between average absorbed glandular dose (AGD), compressed breast thickness (CBT) and compression force (CF).

MATERIALS AND METHODS

All CEDM and DBT examinations were performed in cranio-caudal (CC) and medio-lateral oblique (MLO) view. Exposure parameters of 135 mammographic procedures that using AEC (automatic exposure control) mode were recorded. AGDs were calculated. Kruskal Wallis test was performed.

RESULTS

CBT population ranged from 23 to 94 mm with a thickness median value of 52 mm in CC view and of 57 mm in MLO views. CEDM AGD median value was significatively lower than DBT AGD in each views (p << 0.01). AGD showed a positive correlation and linear regression with CBT for both CEDM and DBT while CF did not show a correlation and linear regression with AGD. The highest values were found for MLO view: R2 of 0.74 for CEDM and R2 of 0.61 for DBT. Kruskal Wallis test shows that there was a difference statistically significant between AGD values of CEDM and DBT in CC view respect to MLO views (p < 0.01).

CONCLUSIONS

Dose values of both techniques meet the recommendations for maximum dose in mammography. The results of the present study indicated that there was significant difference between AGD for CEDM and DBT exposure in different views (AGD in CC views had the lowest value) and that CBT could influence the AGD while CF was not correlated to AGD.

Male patients with unilateral breast symptoms: an optimal imaging approach

OBJECTIVES

To evaluate the usefulness of bilateral mammography in male patients with unilateral breast symptoms, including investigation of the diagnostic performance of unilateral and bilateral reviews and the average glandular dose (AGD) per exposure.

METHODS

Two hundred seventy-one consecutive male patients (mean age, 57 years) with unilateral breast symptoms underwent bilateral mammography. Image interpretation was performed in two ways, first with a unilateral review of the symptomatic breast and then with a bilateral review. A modified BI-RADS scale (from 1 to 5) was used. The diagnostic performance of unilateral and bilateral reviews was compared, and contralateral breast abnormalities and the AGD per exposure were recorded. We also analyzed ultrasound (US) results and compared them with mammography.

RESULTS

Of 271 male patients, 29 were pathologically diagnosed with breast cancer. There was no bilateral breast cancer. The sensitivity, specificity, positive and negative predictive values, and accuracy were 96.6%, 96.7%, 77.8%, 99.6%, and 96.7%, respectively, for unilateral review, and 96.6%, 95.9%, 73.7%, 99.6%, and 95.9% for bilateral review. Receiver operator characteristic analysis showed excellent diagnostic performance for both methods: the area under the curve (AUC) was 0.966 for unilateral review and 0.962 for bilateral review (p = 0.415). The mean AGD per exposure was 1.10 ± 0.29 mGy for symptomatic breast and 1.04 ± 0.30 mGy for contralateral breast (p < 0.001). Diagnostic performance parameters of US were not significantly different from bilateral or unilateral review of mammography.

CONCLUSION

The diagnostic performance of unilateral mammography is comparable with bilateral mammography in male patients with unilateral breast symptoms. Unilateral mammography also has the advantage of reducing radiation exposure.

KEY POINTS

• There is limited knowledge about standardized guidelines or recommendations for imaging the male breast. • Unilateral mammography for male patients with unilateral breast symptoms showed comparable diagnostic performance with bilateral mammography. • Both unilateral and bilateral mammography showed excellent diagnostic performance in the assessment of male patients with unilateral breast symptoms.

Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis

OBJECTIVES

No consensus exists on digital breast tomosynthesis (DBT) utilization for breast cancer detection. We performed a diagnostic test accuracy systematic review and meta-analysis comparing DBT, combined DBT and digital mammography (DM), and DM alone for breast cancer detection in average-risk women.

METHODS

MEDLINE and EMBASE were searched until September 2018. Comparative design studies reporting on the diagnostic accuracy of DBT and/or DM for breast cancer detection were included. Demographic, methodologic, and diagnostic accuracy data were extracted. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Accuracy metrics were pooled using bivariate random-effects meta-analysis. The impact of multiple covariates was assessed using meta-regression.

PROSPERO ID

CRD 42018111287.

RESULTS

Thirty-eight studies reporting on 488,099 patients (13,923 with breast cancer) were included. Eleven studies were at low risk of bias. DBT alone, combined DBT and DM, and DM alone demonstrated sensitivities of 88% (95% confidence interval [CI] 83-92), 88% (CI 83-92), and 79% (CI 75-82), as well as specificities of 84% (CI 76-89), 81% (CI 73-88), and 79% (CI 71-85), respectively. The greater sensitivities of DBT alone and combined DBT and DM compared to DM alone were preserved in the combined meta-regression models accounting for other covariates (p = 0.003-0.006). No significant difference in diagnostic accuracy between DBT alone and combined DBT and DM was identified (p = 0.175-0.581).

CONCLUSIONS

DBT is more sensitive than DM, while the addition of DM to DBT provides no additional diagnostic benefit. Consideration of these findings in breast cancer imaging guidelines is recommended.

KEY POINTS

• Digital breast tomosynthesis with or without additional digital mammography is more sensitive in detecting breast cancer than digital mammography alone in women at average risk for breast cancer. • The addition of digital mammography to digital breast tomosynthesis provides no additional diagnostic benefit in detecting breast cancer compared to digital breast tomosynthesis alone. • The specificity of digital breast tomosynthesis with or without additional digital mammography is no different than digital mammography alone in the detection of breast cancer.

The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis

Aim

Digital Breast Tomosynthesis (DBT), with or without Digital Mammography (DM) or Synthetic Mammography (SM), has been introduced or is under consideration for its introduction in breast cancer screening in several countries, as it has been shown that it has advantages over DM. Despite this there is no agreement on how to implement DBT in screening, and in many cases there is a lack of official guidance on the optimum usage of each commercially available system. The aim of this review is to carry out a manufacturer-specific summary of studies on the implementation of DBT in breast cancer screening.

Methods

An exhaustive literature review was undertaken to identify clinical observer studies that evaluated at least one of five common metrics: sensitivity, specificity, area under the curve (AUC) of the receiver-operating characteristics (ROC) analysis, recall rate and cancer detection rate. Four common DBT implementation methods were discussed in this review: (1) DBT, (2) DM with DBT, (3) 1-view DBT with or without 1-view DM or 2-view DM and (4) DBT with SM.

Results

A summary of 89 studies, selected from a database of 677 studies, on the assessment of the implementation of DBT in breast cancer screening is presented in tables and discussed in a manufacturer- and metric-specific approach. Much more studies were carried out using some DBT systems than others. For one implementation method of DBT by one manufacturer there is a shortage of studies, for another implementation there are conflicting results. In some cases, there is a strong agreement between studies, making the advantages and disadvantages of each system clear.

Conclusion

The optimum implementation method of DBT in breast screening, in terms of diagnostic benefit and patient radiation dose, for one manufacturer does not necessarily apply to other manufacturers.

Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study

Purpose

To study the feasibility of automatically identifying normal digital mammography (DM) exams with artificial intelligence (AI) to reduce the breast cancer screening reading workload.

Methods and materials

A total of 2652 DM exams (653 cancer) and interpretations by 101 radiologists were gathered from nine previously performed multi-reader multi-case receiver operating characteristic (MRMC ROC) studies. An AI system was used to obtain a score between 1 and 10 for each exam, representing the likelihood of cancer present. Using all AI scores between 1 and 9 as possible thresholds, the exams were divided into groups of low- and high likelihood of cancer present. It was assumed that, under the pre-selection scenario, only the high-likelihood group would be read by radiologists, while all low-likelihood exams would be reported as normal. The area under the reader-averaged ROC curve (AUC) was calculated for the original evaluations and for the pre-selection scenarios and compared using a non-inferiority hypothesis.

Results

Setting the low/high-likelihood threshold at an AI score of 5 (high likelihood > 5) results in a trade-off of approximately halving (− 47%) the workload to be read by radiologists while excluding 7% of true-positive exams. Using an AI score of 2 as threshold yields a workload reduction of 17% while only excluding 1% of true-positive exams. Pre-selection did not change the average AUC of radiologists (inferior 95% CI > − 0.05) for any threshold except at the extreme AI score of 9.

Conclusion

It is possible to automatically pre-select exams using AI to significantly reduce the breast cancer screening reading workload.

Key Points

• There is potential to use artificial intelligence to automatically reduce the breast cancer screening reading workload by excluding exams with a low likelihood of cancer.

• The exclusion of exams with the lowest likelihood of cancer in screening might not change radiologists’ breast cancer detection performance.

• When excluding exams with the lowest likelihood of cancer, the decrease in true-positive recalls would be balanced by a simultaneous reduction in false-positive recalls.