Re-attendance in supplemental breast MRI screening rounds of the DENSE trial for women with extremely dense breasts.

OBJECTIVES: Supplemental MRI screening improves early breast cancer detection and reduces interval cancers in women with extremely dense breasts in a cost-effective way. Recently, the European Society of Breast Imaging recommended offering MRI screening to women with extremely dense breasts, but the debate on whether to implement it in breast cancer screening programs is ongoing. Insight into the participant experience and willingness to re-attend is important for this discussion. METHODS: We calculated the re-attendance rates of the second and third MRI screening rounds of the DENSE trial. Moreover, we calculated age-adjusted odds ratios (ORs) to study the association between characteristics and re-attendance. Women who discontinued MRI screening were asked to provide one or more reasons for this. RESULTS: The re-attendance rates were 81.3% (3458/4252) and 85.2% (2693/3160) in the second and third MRI screening round, respectively. A high age (> 65 years), a very low BMI, lower education, not being employed, smoking, and no alcohol consumption were correlated with lower re-attendance rates. Moderate or high levels of pain, discomfort, or anxiety experienced during the previous MRI screening round were correlated with lower re-attendance rates. Finally, a plurality of women mentioned an examination-related inconvenience as a reason to discontinue screening (39.1% and 34.8% in the second and third screening round, respectively). CONCLUSIONS: The willingness of women with dense breasts to re-attend an ongoing MRI screening study is high. However, emphasis should be placed on improving the MRI experience to increase the re-attendance rate if widespread supplemental MRI screening is implemented. CLINICAL RELEVANCE STATEMENT: For many women, MRI is an acceptable screening method, as re-attendance rates were high - even for screening in a clinical trial setting. To further enhance the (re-)attendance rate, one possible approach could be improving the overall MRI experience. KEY POINTS: • The willingness to re-attend in an ongoing MRI screening study is high. • Pain, discomfort, and anxiety in the previous MRI screening round were related to lower re-attendance rates. • Emphasis should be placed on improving MRI experience to increase the re-attendance rate in supplemental MRI screening.

Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk.

Purpose: Risk-stratified breast cancer screening might improve early detection and efficiency without comprising quality. However, modern mammography-based risk models do not ensure adaptation across vendor-domains and rely on cancer precursors, associated with short-term risk, which might limit long-term risk assessment. We report a cross-vendor mammographic texture model for long-term risk. Approach: The texture model was robustly trained using two systematically designed case-control datasets. Textural features, indicative of future breast cancer, were learned by excluding samples with diagnosed/potential malignancies from training. An augmentation-based domain adaption technique, based on flavorization of mammographic views, ensured generalization across vendor-domains. The model was validated in 66,607 consecutively screened Danish women with flavorized Siemens views and 25,706 Dutch women with Hologic-processed views. Performances were evaluated for interval cancers (IC) within 2 years from screening and long-term cancers (LTC) from 2 years after screening. The texture model was combined with established risk factors to flag 10% of women with the highest risk. Results: In Danish women, the texture model achieved an area under the receiver operating characteristic curve (AUC) of 0.71 and 0.65 for ICs and LTCs, respectively. In Dutch women with Hologic-processed views, the AUCs were not different from AUCs in Danish women with flavorized views. The AUC for texture combined with established risk factors increased to 0.68 for LTCs. The 10% of women flagged as high-risk accounted for 25.5% of ICs and 24.8% of LTCs. Conclusions: The texture model robustly estimated long-term breast cancer risk while adapting to an unseen processed vendor-domain and identified a clinically relevant high-risk subgroup.

Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture.

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts.

OBJECTIVES: To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts. METHODS: 430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity. RESULTS: The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve. CONCLUSION: Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI.

Automatic breast and fibro-glandular tissue (FGT) segmentation in breast MRI allows for the efficient and accurate calculation of breast density. The U-Net architecture, either 2D or 3D, has already been shown to be effective at addressing the segmentation problem in breast MRI. However, the lack of publicly available datasets for this task has forced several authors to rely on internal datasets composed of either acquisitions without fat suppression (WOFS) or with fat suppression (FS), limiting the generalization of the approach. To solve this problem, we propose a data-centric approach, efficiently using the data available. By collecting a dataset of T1-weighted breast MRI acquisitions acquired with the use of the Dixon method, we train a network on both T1 WOFS and FS acquisitions while utilizing the same ground truth segmentation. Using the "plug-and-play" framework nnUNet, we achieve, on our internal test set, a Dice Similarity Coefficient (DSC) of 0.96 and 0.91 for WOFS breast and FGT segmentation and 0.95 and 0.86 for FS breast and FGT segmentation, respectively. On an external, publicly available dataset, a panel of breast radiologists rated the quality of our automatic segmentation with an average of 3.73 on a four-point scale, with an average percentage agreement of 67.5%.

An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload.

Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms.

Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance.

OBJECTIVES: To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance. METHODS: A total of 2257 full-field digital mammography screening examinations, obtained 2011-2013, of women aged 50-69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0-95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers. RESULTS: Using a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1-28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1-8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5-4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8-25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0-3.5%). CONCLUSION: The application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice. KEY POINTS: • Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss. • Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives. • About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.

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.

Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial.

Background In the first (prevalent) supplemental MRI screening round of the Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial, a considerable number of breast cancers were found at the cost of an increased false-positive rate (FPR). In incident screening rounds, a lower cancer detection rate (CDR) is expected due to a smaller pool of prevalent cancers, and a reduced FPR, due to the availability of prior MRI examinations. Purpose To investigate screening performance indicators of the second round (incidence round) of the DENSE trial. Materials and Methods The DENSE trial (ClinicalTrials.gov: NCT01315015) is embedded within the Dutch population-based biennial mammography screening program for women aged 50-75 years. MRI examinations were performed between December 2011 and January 2016. Women were eligible for the second round when they again had a negative screening mammogram 2 years after their first MRI. The recall rate, biopsy rate, CDR, FPR, positive predictive values, and distributions of tumor characteristics were calculated and compared with results of the first round using 95% CIs and χ2 tests. Results A total of 3436 women (median age, 56 years; interquartile range, 48-64 years) underwent a second MRI screening. The CDR was 5.8 per 1000 screening examinations (95% CI: 3.8, 9.0) compared with 16.5 per 1000 screening examinations (95% CI: 13.3, 20.5) in the first round. The FPR was 26.3 per 1000 screening examinations (95% CI: 21.5, 32.3) in the second round versus 79.8 per 1000 screening examinations (95% CI: 72.4, 87.9) in the first round. The positive predictive value for recall was 18% (20 of 110 participants recalled; 95% CI: 12.1, 26.4), and the positive predictive value for biopsy was 24% (20 of 84 participants who underwent biopsy; 95% CI: 16.0, 33.9), both comparable to that of the first round. All tumors in the second round were stage 0-I and node negative. Conclusion The incremental cancer detection rate in the second round was 5.8 per 1000 screening examinations-compared with 16.5 per 1000 screening examinations in the first round. This was accompanied by a strong reduction in the number of false-positive results. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Moy and Gao in this issue.

Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI.

PURPOSE: To compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition. METHOD: From September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3 T during the same imaging examination. Four breast radiologists (4-13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n = 52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUCVGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate. RESULTS: Overall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUCVGC: 0.698, P = 0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUCVGC: 0.660, P = 0.022 and AUCVGC: 0.700 P = 0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUCVGC: 0.427, P = 0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P = 0.035). Sensitivity and specificity were comparable between both sequences (P = 0.760 and P = 0.549, respectively). CONCLUSIONS: Despite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

Supplemental MRI Screening for Women with Extremely Dense Breast Tissue.

BACKGROUND: Extremely dense breast tissue is a risk factor for breast cancer and limits the detection of cancer with mammography. Data are needed on the use of supplemental magnetic resonance imaging (MRI) to improve early detection and reduce interval breast cancers in such patients. METHODS: In this multicenter, randomized, controlled trial in the Netherlands, we assigned 40,373 women between the ages of 50 and 75 years with extremely dense breast tissue and normal results on screening mammography to a group that was invited to undergo supplemental MRI or to a group that received mammography screening only. The groups were assigned in a 1:4 ratio, with 8061 in the MRI-invitation group and 32,312 in the mammography-only group. The primary outcome was the between-group difference in the incidence of interval cancers during a 2-year screening period. RESULTS: The interval-cancer rate was 2.5 per 1000 screenings in the MRI-invitation group and 5.0 per 1000 screenings in the mammography-only group, for a difference of 2.5 per 1000 screenings (95% confidence interval [CI], 1.0 to 3.7; P<0.001). Of the women who were invited to undergo MRI, 59% accepted the invitation. Of the 20 interval cancers that were diagnosed in the MRI-invitation group, 4 were diagnosed in the women who actually underwent MRI (0.8 per 1000 screenings) and 16 in those who did not accept the invitation (4.9 per 1000 screenings). The MRI cancer-detection rate among the women who actually underwent MRI screening was 16.5 per 1000 screenings (95% CI, 13.3 to 20.5). The positive predictive value was 17.4% (95% CI, 14.2 to 21.2) for recall for additional testing and 26.3% (95% CI, 21.7 to 31.6) for biopsy. The false positive rate was 79.8 per 1000 screenings. Among the women who underwent MRI, 0.1% had either an adverse event or a serious adverse event during or immediately after the screening. CONCLUSIONS: The use of supplemental MRI screening in women with extremely dense breast tissue and normal results on mammography resulted in the diagnosis of significantly fewer interval cancers than mammography alone during a 2-year screening period. (Funded by the University Medical Center Utrecht and others; DENSE ClinicalTrials.gov number, NCT01315015.).

Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer.

The channelized-Hotelling observer (CHO) was investigated as a surrogate of human observers in task-based image quality assessment. The CHO with difference-of-Gaussian (DoG) channels has shown potential for the prediction of human detection performance in digital mammography (DM) images. However, the DoG channels employ parameters that describe the shape of each channel. The selection of these parameters influences the performance of the DoG CHO and needs further investigation. The detection performance of the DoG CHO was calculated and correlated with the detection performance of three humans who evaluated DM images in 2-alternative forced-choice experiments. A set of DM images of an anthropomorphic breast phantom with and without calcification-like signals was acquired at four different dose levels. For each dose level, 200 square regions-of-interest (ROIs) with and without signal were extracted. Signal detectability was assessed on ROI basis using the CHO with various DoG channel parameters and it was compared to that of the human observers. It was found that varying these DoG parameter values affects the correlation ( r 2 ) of the CHO with human observers for the detection task investigated. In conclusion, it appears that the the optimal DoG channel sets that maximize the prediction ability of the CHO might be dependent on the type of background and signal of ROIs investigated.

MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial.

BACKGROUND: Approximately 15% of all breast cancers occur in women with a family history of breast cancer, but for whom no causative hereditary gene mutation has been found. Screening guidelines for women with familial risk of breast cancer differ between countries. We did a randomised controlled trial (FaMRIsc) to compare MRI screening with mammography in women with familial risk. METHODS: In this multicentre, randomised, controlled trial done in 12 hospitals in the Netherlands, women were eligible to participate if they were aged 30-55 years and had a cumulative lifetime breast cancer risk of at least 20% because of a familial predisposition, but were BRCA1, BRCA2, and TP53 wild-type. Participants who were breast-feeding, pregnant, had a previous breast cancer screen, or had a previous a diagnosis of ductal carcinoma in situ were eligible, but those with a previously diagnosed invasive carcinoma were excluded. Participants were randomly allocated (1:1) to receive either annual MRI and clinical breast examination plus biennial mammography (MRI group) or annual mammography and clinical breast examination (mammography group). Randomisation was done via a web-based system and stratified by centre. Women who did not provide consent for randomisation could give consent for registration if they followed either the mammography group protocol or the MRI group protocol in a joint decision with their physician. Results from the registration group were only used in the analyses stratified by breast density. Primary outcomes were number, size, and nodal status of detected breast cancers. Analyses were done by intention to treat. This trial is registered with the Netherlands Trial Register, number NL2661. FINDINGS: Between Jan 1, 2011, and Dec 31, 2017, 1355 women provided consent for randomisation and 231 for registration. 675 of 1355 women were randomly allocated to the MRI group and 680 to the mammography group. 218 of 231 women opting to be in a registration group were in the mammography registration group and 13 were in the MRI registration group. The mean number of screening rounds per woman was 4·3 (SD 1·76). More breast cancers were detected in the MRI group than in the mammography group (40 vs 15; p=0·0017). Invasive cancers (24 in the MRI group and eight in the mammography group) were smaller in the MRI group than in the mammography group (median size 9 mm [5-14] vs 17 mm [13-22]; p=0·010) and less frequently node positive (four [17%] of 24 vs five [63%] of eight; p=0·023). Tumour stages of the cancers detected at incident rounds were significantly earlier in the MRI group (12 [48%] of 25 in the MRI group vs one [7%] of 15 in the mammography group were stage T1a and T1b cancers; one (4%) of 25 in the MRI group and two (13%) of 15 in the mammography group were stage T2 or higher; p=0·035) and node-positive tumours were less frequent (two [11%] of 18 in the MRI group vs five [63%] of eight in the mammography group; p=0·014). All seven tumours stage T2 or higher were in the two highest breast density categories (breast imaging reporting and data system categories C and D; p=0·0077) One patient died from breast cancer during follow-up (mammography registration group). INTERPRETATION: MRI screening detected cancers at an earlier stage than mammography. The lower number of late-stage cancers identified in incident rounds might reduce the use of adjuvant chemotherapy and decrease breast cancer-related mortality. However, the advantages of the MRI screening approach might be at the cost of more false-positive results, especially at high breast density. FUNDING: Dutch Government ZonMw, Dutch Cancer Society, A Sister's Hope, Pink Ribbon, Stichting Coolsingel, J&T Rijke Stichting.

A systematic review on the use of the breast lesion excision system in breast disease.

PURPOSE: To outline the current status of and provide insight into possible future research on the breast lesion excision system (BLES) as a diagnostic and therapeutic device. METHODS: A systematic search of the literature was performed using PubMed, Embase, and the Cochrane databases to identify relevant studies published between January 2002 and April 2018. Studies were considered eligible for inclusion if they evaluated the diagnostic or therapeutic accuracy or safety of BLES. RESULTS: Ultimately, 17 articles were included. The reported underestimation rates of atypical ductal hyperplasia and ductal carcinoma in situ (DCIS) ranged from 0 to 14.3% and from 0 to 22.2%, respectively. Complete excision rates for invasive ductal carcinoma and DCIS ranged from 5.3 to 76.3%. Bleeding was the most frequently reported complication (0-11.8%). Device-related complications may arise, with an empty basket being the most common (0.6-3.6%). Thermal damage of the specimen, caused by the use of a radiofrequency cutting wire, was reported in eight of the included studies. Most thermal artifacts were reported as superficial and small (0.1-1.9 mm). CONCLUSIONS: The BLES, an automated, image-guided, single-pass biopsy system for breast lesions using radiofrequency is designed to excise and retrieve an intact tissue specimen. It is an efficient and safe breast biopsy method with acceptable complication rates, which may be used as an alternative to vacuum-assisted biopsies. The variable rate of complete excision raises questions about the possibility to use BLES as a therapeutic device for the excision of small lesions. Further research should focus on this aspect of BLES.

Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program : A retrospective cohort study.

OBJECTIVES: The purpose of this study is to evaluate the predictive value of the amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE), measured at baseline on breast MRI, for breast cancer development and risk of false-positive findings in women at increased risk for breast cancer. METHODS: Negative baseline MRI scans of 1533 women participating in a screening program for women at increased risk for breast cancer between January 1, 2003, and January 1, 2014, were selected. Automated tools based on deep learning were used to obtain quantitative measures of FGT and BPE. Logistic regression using forward selection was used to assess relationships between FGT, BPE, cancer detection, false-positive recall, and false-positive biopsy. RESULTS: Sixty cancers were detected in follow-up. FGT was only associated to short-term cancer risk; BPE was not associated with cancer risk. High FGT and BPE did lead to more false-positive recalls at baseline (OR 1.259, p = 0.050, and OR 1.475, p = 0.003) and to more frequent false-positive biopsies at baseline (OR 1.315, p = 0.049, and OR 1.807, p = 0.002), but were not predictive for false-positive findings in subsequent screening rounds. CONCLUSIONS: FGT and BPE, measured on baseline MRI, are not predictive for overall breast cancer development in women at increased risk. High FGT and BPE lead to more false-positive findings at baseline. KEY POINTS: • Amount of fibroglandular tissue is only predictive for short-term breast cancer risk in women at increased risk. • Background parenchymal enhancement measured on baseline MRI is not predictive for breast cancer development in women at increased risk. • High amount of fibroglandular tissue and background parenchymal enhancement lead to more false-positive findings at baseline MRI.

Change in mammographic density across birth cohorts of Dutch breast cancer screening participants.

High mammographic density is a well-known risk factor for breast cancer. This study aimed to search for a possible birth cohort effect on mammographic density, which might contribute to explain the increasing breast cancer incidence. We separately analyzed left and right breast density of Dutch women from a 13-year period (2003-2016) in the breast cancer screening programme. First, we analyzed age-specific changes in average percent dense volume (PDV) across birth cohorts. A linear regression analysis (PDV vs. year of birth) indicated a small but statistically significant increase in women of: 1) age 50 and born from 1952 to 1966 (left, slope = 0.04, p = 0.003; right, slope = 0.09, p < 0.0001); 2) age 55 and born from 1948 to 1961 (right, slope = 0.04, p = 0.01); and 3) age 70 and born from 1933 to 1946 (right, slope = 0.05, p = 0.002). A decrease of total breast volume seemed to explain the increase in PDV. Second, we compared proportion of women with dense breast in women born in 1946-1953 and 1959-1966, and observed a statistical significant increase of proportion of highly dense breast in later born women, in the 51 to 55 age-groups for the left breast (around a 20% increase in each age-group), and in the 50 to 56 age-groups for the right breast (increase ranging from 27% to 48%). The study indicated a slight increase in mammography density across birth cohorts, most pronounced for women in their early 50s, and more marked for the right than for the left breast.

Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI.

OBJECTIVES: We investigated artificial intelligence (AI)-based classification of benign and malignant breast lesions imaged with a multiparametric breast magnetic resonance imaging (MRI) protocol with ultrafast dynamic contrast-enhanced MRI, T2-weighted, and diffusion-weighted imaging with apparent diffusion coefficient mapping. MATERIALS AND METHODS: We analyzed 576 lesions imaged with MRI, including a consecutive set of biopsied malignant (368) and benign (149) lesions, and an additional set of 59 benign lesions proven by follow-up. We used deep learning methods to interpret ultrafast dynamic contrast-enhanced MRI and T2-weighted information. A random forests classifier combined the output with patient information (PI; age and BRCA status) and apparent diffusion coefficient values obtained from diffusion-weighted imaging to perform the final lesion classification. We used receiver operating characteristic (ROC) analysis to evaluate our results. Sensitivity and specificity were compared with the results of the prospective clinical evaluation by radiologists. RESULTS: The area under the ROC curve was 0.811 when only ultrafast dynamics was used. The final AI system that combined all imaging information with PI resulted in an area under the ROC curve of 0.852, significantly higher than the ultrafast dynamics alone (P = 0.002). When operating at the same sensitivity level of radiologists in this dataset, this system produced 19 less false-positives than the number of biopsied benign lesions in our dataset. CONCLUSIONS: Use of adjunct imaging and PI has a significant contribution in diagnostic performance of ultrafast breast MRI. The developed AI system for interpretation of multiparametric ultrafast breast MRI may improve specificity.