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Objectives To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes. Methods The MIPA observational study enrolled women aged 18–80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis. Results A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p ≤ 0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p p = 0.957) for D-MRI, and 1.9 (p Conclusions Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer. Key Points • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups.

Purpose In this study, we compare readout-segmented echo-planar imaging (rs-EPI) Diffusion Weighted Imaging (DWI) to a work-in-progress single-shot EPI with modified Inversion Recovery Background Suppression (ss-EPI-mIRBS) sequence at 3 T using a b-value of 2000 s/mm2 on image quality, lesion visibility and evaluation time. Method From September 2017 to December 2018, 23 women (one case used for training) with known breast cancer were included in this study, after providing signed informed consent. Women were scanned with the conventional rs-EPI sequence and the work-in-progress ss-EPI-mIRBS during the same examination. Four breast radiologists (4–13 years of experience) independently scored both series for overall image quality (1: extremely poor to 9: excellent). All lesions (47 in total, 36 malignant, and 11 benign and high-risk) were evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible) and probability of malignancy (BI-RADS 1 to 5). ADC values were determined by measuring signal intensity in the lesions using dynamic contrast-enhanced (DCE) images for reference. Evaluation times for all assessments were automatically recorded. Results were analyzed using the visual grading characteristics (VGC) and the resulting area under the curve (AUCVGC) method. Statistical analysis was performed in SPSS, with McNemar tests, and paired t-tests used for comparison. Results No significant differences were detected between the two sequences in image quality (AUCVGC: 0.398, p = 0.087) and lesion visibility (AUCVGC: 0.534, p = 0.336) scores. Lesion characteristics (e.g benign and high-risk, versus malignant; small (≤10 mm) vs. larger (>10 mm)) did not result in different image quality or lesion visibility between sequences. Sensitivity (rs-EPI: 72.2% vs. ss-EPImIRBS: 78.5%, p = 0.108) and specificity (70.5% vs. 56.8%, p = 0.210, respectively) were comparable. In both sequences the mean ADC value was higher for benign and high-risk lesions than for malignant lesions (ss-EPI-mIRBS: p = 0.022 and rs-EPI: p = 0.055). On average, ss-EPI-mIRBS resulted in decreased overall reading time by 7.7 s/case (p = 0.067); a reduction of 17%. For malignant lesions, average reading time was significantly shorter using ss-EPI-mIRBS compared to rs-EPI (64.0 s/lesion vs. 75.9 s/lesion, respectively, p = 0.039). Conclusion Based on this study, the ss-EPI sequence using a b-value of 2000 s/mm2 enables for a mIRBS acquisition with quality and lesion conspicuity that is comparable to conventional rs-EPI, but with a decreased reading time.

Purpose Problem solving magnetic resonance imaging (MRI) is used to exclude malignancy in women with equivocal findings on conventional imaging. However, recommendations on its use for women recalled after screening are lacking. This study evaluates the impact of problem solving MRI on diagnostic workup among women recalled from the Dutch screening program, as well as time trends and inter-hospital variation in its use. Methods Women who were recalled at screening mammography in the South of the Netherlands (2008–2017) were included. Two-year follow-up data were collected. Diagnostic-workup and accuracy of problem solving MRI were evaluated and time trends and inter-hospital variation in its use were examined. Results In the study period 16,175 women were recalled, of whom 906 underwent problem solving MRI. Almost half of the women (45.4%) who underwent problem solving MRI were referred back to the screening program without further workup. The sensitivity, specificity, and positive and negative predictive values of problem solving MRI were 98.2%, 70.0%, 31.1%, and 99.6%, respectively. The percentage of recalled women receiving problem solving MRI fluctuated over time (4.7%–7.2%) and significantly varied among hospitals (2.2%–7.0%). Conclusion The use of problem solving MRI may exclude malignancy in recalled women. The use of problem solving MRI varied over time and among hospitals, which indicates the need for guidelines on problem solving MRI., Highlights • Problem solving MRI did correctly refer back women to the screening program. • The sensitivity and specificity of problem solving MRI were 98.2% and 70.0%. • Positive and negative predictive values of problem solving MRI were 31.1% and 99.6%. • By excluding malignancy, problem solving MRI may reduce invasive diagnostic workup.

Breast cancer is one of the most diagnosed types of cancer worldwide. Volumetric ultrasound breast imaging, combined with MRI can improve lesion detection rate, reduce examination time, and improve lesion diagnosis. However, to our knowledge, there are no 3D US breast imaging systems available that facilitate 3D US - MRI image fusion. In this paper, a novel Automated Cone-based Breast Ultrasound System (ACBUS) is introduced. The system facilitates volumetric ultrasound acquisition of the breast in a prone position without deforming it by the US transducer. Quality of ACBUS images for reconstructions at different voxel sizes (0.25 and 0.50 mm isotropic) was compared to quality of the Automated Breast Volumetric Scanner (ABVS) (Siemens Ultrasound, Issaquah, WA, USA) in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and resolution using a custom made phantom. The ACBUS image data were registered to MRI image data utilizing surface matching and the registration accuracy was quantified using an internal marker. The technology was also evaluated in vivo. The phantom-based quantitative analysis demonstrated that ACBUS can deliver volumetric breast images with an image quality similar to the images delivered by a currently commercially available Siemens ABVS. We demonstrate on the phantom and in vivo that ACBUS enables adequate MRI-3D US fusion. To our conclusion, ACBUS might be a suitable candidate for a second-look breast US exam, patient follow-up, and US guided biopsy planning.

Purpose To determine the added value of mammography in women with focal breast complaints and the utility of initial targeted ultrasound in this setting. Methods Women with symptomatic breast disease who were evaluated by breast imaging (mammography/digital breast tomosynthesis and ultrasound) between January 2016 and December 2016 in the Radboud University Medical Centre were included. We retrospectively collected the following data: date of birth, indication of imaging, visibility on mammography/ultrasound, whether biopsy was taken, additional findings, BI-RADS-classification, pathology and follow-up results. Results A total of 494 women were included (mean age 46.5, range 30 to 93). In 49 women (9.9%), symptomatic breast cancer was diagnosed, all visible during targeted ultrasound. The negative predictive value of targeted ultrasound was very high (99.8%). Additional findings on mammography were significantly more often malignant when the symptomatic lesion was also malignant (3.8% vs 70%, P Conclusions The contribution of mammography for cancer detection in women with focal breast complaints is very low when targeted ultrasound is performed. Additional findings are most common in patients with symptomatic breast cancer. Our results suggest that initial targeted ultrasound is a more appropriate initial tool for the evaluation of focal breast complaints. Mammography could be performed on indication only.

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.

Purpose To assess the feasibility of completely excising small breast cancers using the automated, image-guided, single-pass radiofrequency-based breast lesion excision system (BLES) under ultrasound (US) guidance. Methods From February 2018 to July 2019, 22 patients diagnosed with invasive carcinomas ≤ 15 mm at US and mammography were enrolled in this prospective, multi-center, ethics board-approved study. Patients underwent breast MRI to verify lesion size. BLES-based excision and surgery were performed during the same procedure. Histopathology findings from the BLES procedure and surgery were compared, and total excision findings were assessed. Results Of the 22 patients, ten were excluded due to the lesion being > 15 mm and/or being multifocal at MRI, and one due to scheduling issues. The remaining 11 patients underwent BLES excision. Mean diameter of excised lesions at MRI was 11.8 mm (range 8.0–13.9 mm). BLES revealed ten (90.9%) invasive carcinomas of no special type, and one (9.1%) invasive lobular carcinoma. Histopathological results were identical for the needle biopsy, BLES, and surgical specimens for all lesions. None of the BLES excisions were adequate. Margins were usually compromised on both sides of the specimen, indicating that the excised volume was too small. Margin assessment was good for all BLES specimens. One technical complication occurred (retrieval of an empty BLES basket, specimen retrieved during subsequent surgery). Conclusions BLES allows accurate diagnosis of small invasive breast carcinomas. However, BLES cannot be considered as a therapeutic device for small invasive breast carcinomas due to not achieving adequate excision.

Objectives Automated ultrasound of the breast has the advantage to have the whole breast scanned by technicians. Consequently, feedback to the radiologist about concurrent focal abnormalities (e.g., palpable lesions) is lost. To enable marking of patient- or physician-reported focal abnormalities, we aimed to develop skin markers that can be used without disturbing the interpretability of the image. Methods Disk-shaped markers were casted out of silicone. In this IRB-approved prospective study, 16 patients were included with a mean age of 57 (39–85). In all patients, the same volume was imaged twice using an automated breast ultrasound system, once with and once without a marker in place. Nine radiologists from two medical centers filled scoring forms regarding image quality, image interpretation, and confidence in providing a diagnosis based on the images. Results Marker adhesion was sufficient for automated scanning. Observer scores showed a significant shift in scores from excellent to good regarding diagnostic yield/image quality (χ2, 15.99, p χ2, 21.20, p χ2, 6.00, p = 0.20). Conclusion Inexpensive, easy producible skin markers can be used for accurate lesion marking in automated ultrasound examinations of the breast while image interpretability is preserved. Any marker-induced noise and decreased image quality did not affect confidence in providing a diagnosis. Key Points • The use of a skin marker enables the reporting radiologist to identify a location which a patient is concerned about. • The developed skin marker can be used for accurate breast lesion marking in ultrasound examinations.

Abstract The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications. Key Points • The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use. • Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters. • Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Although computers have had a role in interpretation of mammograms for at least two decades, their impact on performance has not lived up to expectations. However, in the last five years, the field of medical image analysis has undergone a revolution due to the introduction of deep learning convolutional neural networks – a form of artificial intelligence (AI). Because of their considerably higher performance compared to conventional computer aided detection methods, these AI algorithms have resulted in renewed interest in their potential for interpreting breast images in stand-alone mode. For this, first the actual capability of the algorithms, compared to breast radiologists, needs to be well understood. Although early studies have pointed to the comparable performance between AI systems and breast radiologists in interpreting mammograms, these comparisons have been performed in laboratory conditions with limited, enriched datasets. AI algorithms with performance comparable to breast radiologists could be used in a number of different ways, the most impactful being pre-selection, or triaging, of normal screening mammograms that would not need human interpretation. Initial studies evaluating this proposed use have shown very promising results, with the resulting accuracy of the complete screening process not being affected, but with a significant reduction in workload. There is a need to perform additional studies, especially prospective ones, with large screening data sets, to both gauge the actual stand-alone performance of these new algorithms, and the impact of the different implementation possibilities on screening programs., Highlights • AI-based mammography interpretation systems are feasible for stand-alone mode use. • Studies to date have shown that their performance approximates that of radiologists. • Larger scale, prospective screening trials are needed to determine their impact. • Once proven, AI identification of normal cases could reduce the radiologist workload.

PURPOSE: We determined the incidence and effects of different screening intervals prior to a true positive recall on the tumour characteristics of screen-detected cancers (SDC) and interval cancers (ICs) at biennial screening mammography.METHODS: A consecutive series of 553020 subsequent screens was included, obtained in a Dutch screening region between January 2009 and July 2019. During 2-year follow-up, we obtained data on radiological procedures, pathology and surgical interventions of all recalled women.RESULTS: A total of 13,221 women were recalled (2.4% recall rate), yielding 3662 women with a SDC (6.6 SDCs per 1000 screen). Of these, 3477 (94.9%) had attended their two most recent screens as scheduled (i.e., 2-year screening interval), whereas the interval between the two most recent screens was four years or at least six years in respectively 132 (3.6%) and 53 (1.4%) women. There was a trend of higher cancer detection rates in case of longer screening intervals. The proportions of DCIS versus invasive cancer, as well as tumour histology, tumour size, axillary lymph node status, B&R grading, hormone receptor status and type of surgical treatment (breast conserving surgery or mastectomy) were comparable for women with a 2-year or 4-year interval between their two latest screens. SDCs in women with at least six years between their two latest screens were more frequently estrogen receptor negative or triple negative and were more frequently treated by mastectomy. All tumour characteristics mentioned above were less favourable for ICs than SDCs.CONCLUSIONS: A vast majority of women with a SDC had a 2-year screening interval between their two latest screens. A screening interval of at least six years had a slight negative influence on the tumour characteristics and treatment of SDCs.

Contains fulltext : 283299.pdf (Publisher’s version ) (Open Access) This review gives an overview of the current state of deep learning research in breast cancer imaging. Breast imaging plays a major role in detecting breast cancer at an earlier stage, as well as monitoring and evaluating breast cancer during treatment. The most commonly used modalities for breast imaging are digital mammography, digital breast tomosynthesis, ultrasound and magnetic resonance imaging. Nuclear medicine imaging techniques are used for detection and classification of axillary lymph nodes and distant staging in breast cancer imaging. All of these techniques are currently digitized, enabling the possibility to implement deep learning (DL), a subset of Artificial intelligence, in breast imaging. DL is nowadays embedded in a plethora of different tasks, such as lesion classification and segmentation, image reconstruction and generation, cancer risk prediction, and prediction and assessment of therapy response. Studies show similar and even better performances of DL algorithms compared to radiologists, although it is clear that large trials are needed, especially for ultrasound and magnetic resonance imaging, to exactly determine the added value of DL in breast cancer imaging. Studies on DL in nuclear medicine techniques are only sparsely available and further research is mandatory. Legal and ethical issues need to be considered before the role of DL can expand to its full potential in clinical breast care practice.

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

To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI.An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test.Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform.• Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.

Background Extremely dense breast tissue is associated with increased breast cancer risk and limited sensitivity of mammography. The DENSE trial showed that additional magnetic resonance imaging (MRI) screening in women with extremely dense breasts resulted in a substantial reduction in interval cancers. The cost-effectiveness of MRI screening for these women is unknown. Methods We used the MISCAN-breast microsimulation model to simulate several screening protocols containing mammography and/or MRI to estimate long-term effects and costs. The model was calibrated using results of the DENSE trial and adjusted to incorporate decreases in breast density with increasing age. Screening strategies varied in the number of MRIs and mammograms offered to women ages 50-75 years. Outcomes were numbers of breast cancers, life-years, quality-adjusted life-years (QALYs), breast cancer deaths, and overdiagnosis. Incremental cost-effectiveness ratios (ICERs) were calculated (3% discounting), with a willingness-to-pay threshold of €22 000. Results Calibration resulted in a conservative fit of the model regarding MRI detection. Both strategies of the DENSE trial were dominated (biennial mammography; biennial mammography plus MRI). MRI alone every 4 years was cost-effective with €15 620 per QALY. Screening every 3 years with MRI alone resulted in an incremental cost-effectiveness ratio of €37 181 per QALY. All strategies with mammography and/or a 2-year interval were dominated because other strategies resulted in more additional QALYs per additional euro. Alternating mammography and MRI every 2 years was close to the efficiency frontier. Conclusions MRI screening is cost-effective for women with extremely dense breasts, when applied at a 4-year interval. For a willingness to pay more than €22 000 per QALY gained, MRI at a 3-year interval is cost-effective as well.

Objectives Vacuum-assisted biopsy (VAB) of the breast seems unsuitable for rapid processing due to large size. We tested microwave-based acceleration. Methods As a proof-of-principle study, 9-gauge VAB specimens were taken from eight mastectomy specimens. Forty-two biopsy specimens were processed. Quality of H&E was evaluated in 84 slides, and estrogen receptor (ER), progesterone receptor (PR), E-cadherin, and human epidermal growth factor receptor 2 (HER2) stains were evaluated in six slides. Preoperative biopsy specimens were used as a control. Results Diagnostic quality of H&E slides was good in 87%, reasonable in 12%, and low in 1%. Quality of E-cadherin was good in 75% and reasonable in 25%. Quality of ER was good in 83% and reasonable in 17%. PR and both HER2 immunohistochemistry and fluorescence in situ hybridization were good in all slides. Quality of experimental slides was similar to control slides. Conclusions Nine-gauge VAB specimens can be processed within 4 hours. Slides are suitable for all routine pathologic stains. This enables a same-day diagnosis.

Aim This study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes. Materials and methods All 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and histopathological findings. Kruskal–Wallis tests were used to analyze the relationship between US and OA/US features and molecular subtypes of breast cancer. If a significant relationship was found, additional Wilcoxon–Mann–Whitney tests were used to identify the differences between molecular subtype groups. Results US sound transmission helped to differentiate between LUMA and LUMB, LUMB and TNBC, and LUMB and all other molecular subtypes combined (p values

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.

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., European Radiology, 29 (9), ISSN:0938-7994, ISSN:1432-1084

Contains fulltext : 208984.pdf (Publisher’s version ) (Open Access) Multiple studies in the first decade of the 21(st) century have established contrast-enhanced breast MRI as a screening modality for women with a hereditary or familial increased risk for the development of breast cancer. In recent studies, in women with various risk profiles, the sensitivity ranges between 81% and 100%, which is approximately twice as high as the sensitivity of mammography. The specificity increases in follow-up rounds to around 97%, with positive predictive values for biopsy in the same range as for mammography. MRI preferentially detects the more aggressive/invasive types of breast cancer, but has a higher sensitivity than mammography for any type of cancer. This performance implies that in women screened with breast MRI, all other examinations must be regarded as supplemental. Mammography may yield ~5% additional cancers, mostly ductal carcinoma in situ, while slightly decreasing specificity and increasing the costs. Ultrasound has no supplemental value when MRI is used. Evidence is mounting that in other groups of women the performance of MRI is likewise superior to more conventional screening techniques. Particularly in women with a personal history of breast cancer, the gain seems to be high, but also in women with a biopsy history of lobular carcinoma in situ and even women at average risk, similar results are reported. Initial outcome studies show that breast MRI detects cancer earlier, which induces a stage-shift increasing the survival benefit of screening. Cost-effectiveness is still an issue, particularly for women at lower risk. Since costs of the MRI scan itself are a driving factor, efforts to reduce these costs are essential. The use of abbreviated MRI protocols may enable more widespread use of breast MRI for screening. Level of Evidence: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:377-390.

Background This study aimed to examine the association between preoperative magnetic resonance imaging (MRI) and surgical margin involvement, as well as to determine the factors associated with positive resection margins in screen-detected breast cancer patients undergoing breast-conserving surgery (BCS). Methods Breast cancer patients eligible for BCS and diagnosed after biennial screening mammography in the south of The Netherlands (2008–2017) were retrospectively included. Missing values were imputed and multivariable regression analyses were performed to analyze whether preoperative MRI was related to margin involvement after BCS, as well as to examine what factors were associated with positive resection margins, defined as more than focally (>4 mm) involved. Results Overall, 2483 patients with invasive breast cancer were enrolled, of whom 123 (5.0%) had more than focally involved resection margins. In multivariable regression analyses, preoperative MRI was associated with a reduced risk of positive resection margins after BCS (adjusted odds ratio [OR] 0.56, 95% confidence interval [CI] 0.33–0.96). Lobular histology (adjusted OR 2.86, 95% CI 1.68–4.87), large tumor size (per millimeter increase, adjusted OR 1.05, 95% CI 1.03–1.07), high (>75%) mammographic density (adjusted OR 3.61, 95% CI 1.07–12.12), and the presence of microcalcifications (adjusted OR 4.45, 95% CI 2.69–7.37) and architectural distortions (adjusted OR 1.85, 95% CI 1.01–3.40) were independently associated with positive resection margins after BCS. Conclusions Preoperative MRI was associated with lower risk of positive resection margins in patients with invasive breast cancer eligible for BCS using multivariable analysis. Furthermore, specific mammographic characteristics and tumor characteristics were independently associated with positive resection margins after BCS.

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Purpose The presence of extensive ductal carcinoma in situ (DCIS) adjacent to HER2-positive invasive breast cancer (IBC) is often a contra-indication for breast-conserving surgery, even in case of excellent treatment response of the invasive component. Data on the response of DCIS to neoadjuvant systemic treatment (NST) are limited. Therefore, we estimated the response of adjacent DCIS to NST-containing HER2-blockade in HER2-positive breast cancer patients and assessed the association of clinicopathological and radiological factors with response. Methods Pre-NST biopsies were examined to determine presence of DCIS in all women with HER2-positive IBC treated with trastuzumab-containing NST ± pertuzumab between 2004 and 2017 in a comprehensive cancer center. When present, multiple DCIS factors, including grade, calcifications, necrosis, hormone receptor, and Ki-67 expression, were scored. Associations of clinicopathological and radiological factors with complete response were assessed using logistic regression models. Results Adjacent DCIS, observed in 138/316 patients with HER2-positive IBC, was eradicated after NST in 46% of patients. Absence of calcifications suspicious for malignancy on pre-NST mammography (odds ratio (OR) 3.75; 95% confidence interval (95% CI) 1.72–8.17), treatment with dual HER2-blockade (OR 2.36; 95% CI 1.17–4.75), a (near) complete response on MRI (OR 3.55; 95% CI 1.31–9.64), and absence of calcifications (OR 3.19; 95% CI 1.34–7.60) and Ki-67 > 20% in DCIS (OR 2.74; 95% CI 1.09–6.89) on pre-NST biopsy were significantly associated with DCIS response. Conclusions As DCIS can respond to NST containing HER2-blockade, the presence of extensive DCIS in HER2-positive breast cancer before NST should not always indicate a mastectomy. The predictive factors we found could be helpful when considering breast-conserving surgery in these patients.

Contains fulltext : 246122.pdf (Publisher’s version ) (Closed access) PURPOSE: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the 3D spatial information and temporal information obtained from the early-phase of the dynamic acquisition. METHODS: The proposed CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1 weighted sequences, which are preprocessed for motion compensation, temporal normalization, and are cropped before passing into the model. The model is optimized to enable the detection of relatively small breast lesions in a screening setting, focusing on detection of lesions that are harder to differentiate from confounding structures inside the breast. RESULTS: The method was developed based on a dataset consisting of 489 ultrafast MRI studies obtained from 462 patients containing a total of 572 lesions (365 malignant, 207 benign) and achieved a detection rate, sensitivity, and detection rate of benign lesions of 0.90 (0.876-0.934), 0.95 (0.934-0.980), and 0.81 (0.751-0.871) at four false positives per normal breast with 10-fold cross-testing, respectively. CONCLUSIONS: The deep learning architecture used for the proposed CADe application can efficiently detect benign and malignant lesions on ultrafast DCE-MRI. Furthermore, utilizing the less visible hard-to-detect lesions in training improves the learning process and, subsequently, detection of malignant breast lesions.

Contains fulltext : 232884.pdf (Publisher’s version ) (Open Access) 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 3T 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.

Objectives Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue. Methods This observational study enrolled women aged 18–80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases. Results Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p p p p Conclusions Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup. Key Points • In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making.

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.

Contains fulltext : 225795.pdf (Publisher’s version ) (Closed access) Despite its high diagnostic performance, the use of breast MRI in the preoperative setting is controversial. It has the potential for personalized surgical management in breast cancer patients, but two of three randomized controlled trials did not show results in favor of its introduction for assessing the disease extent before surgery. Meta-analyses showed a higher mastectomy rate in women undergoing preoperative MRI compared to those who do not. Nevertheless, preoperative breast MRI is increasingly used and a survey from the American Society of Breast Surgeons showed that 41% of respondents ask for it in daily practice. In this context, a large-scale observational multicenter international prospective analysis (MIPA study) was proposed under the guidance of the European Network for the Assessment of Imaging in Medicine (EuroAIM). The aims were (1) to prospectively and systematically collect data on consecutive women with a newly diagnosed breast cancer, not candidates for neoadjuvant therapy, who are offered or not offered breast MRI before surgery according to local practice; (2) to compare these two groups in terms of surgical and clinical endpoints, adjusting for covariates. The underlying hypotheses are that MRI does not cause additional mastectomies compared to conventional imaging, while reducing the reoperation rate in all or in subgroups of patients. Ninety-six centers applied to a web-based call; 36 were initially selected based on volume and quality standards; 27 were active for enrollment. On November 2018, the target of 7000 enrolled patients was reached. The MIPA study is presently at the analytic phase. Key Points • Breast MRI has a high diagnostic performance but its utility in the preoperative setting is controversial. • A large-scale observational multicenter prospective study was launched to compare women receiving with those not receiving preoperative MRI. • Twenty-seven centers enrolled more than 7000 patients. The study is presently at the analytic phase.

Importance For women with a 20% or more familial risk of breast cancer without a known BRCA1/2 (BRCA1, OMIM; and BRCA2, OMIM) or TP53 (OMIM) variant, screening guidelines vary substantially, and cost-effectiveness analyses are scarce.Objective To assess the cost-effectiveness of magnetic resonance imaging (MRI) screening strategies for women with a 20% or more familial risk for breast cancer without a known BRCA1/2 or TP53 variant.Design, Setting, and Participants In this economic evaluation, conducted from February 1, 2019, to May 25, 2020, microsimulation modeling was used to estimate costs and effectiveness on a lifetime horizon from age 25 years until death of MRI screening among a cohort of 10 million Dutch women with a 20% or more familial risk for breast cancer without a known BRCA1/2 or TP53 variant. A Dutch screening setting was modeled. Most data were obtained from the randomized Familial MRI Screening (FaMRIsc) trial, which included Dutch women aged 30 to 55 years. A health care payer perspective was applied.Interventions Several screening protocols with varying ages and intervals including those of the randomized FaMRIsc trial, consisting of the mammography (Mx) protocol (annual mammography and clinical breast examination) and the MRI protocol (annual MRI and clinical breast examination plus biennial mammography).MAIN OUTCOMES AND MEASURES Costs, life-years, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were calculated and discounted by 3%. A threshold of (sic)22 000 (US $24 795.87) per QALY was applied.RESULTS This economic evaluation modeling study estimated that, on a lifetime horizon per 1000 women with the Mx protocol of the FaMRIsc trial, 346 breast cancers would be detected, and 49 women were estimated to die from breast cancer, resulting in 22 885 QALYs and total costs of (sic)7 084 767 (US $7 985 134.61). The MRI protocol resulted in 79 additional QALYs and additional (sic)2 657 266 (US $2 994 964.65). Magnetic resonance imaging performed only every 18 months between the ages of 35 and 60 years followed by the national screening program was considered optimal, with an ICER of (sic)21 380 (US $24 097.08) compared with the previous nondominated strategy in the ranking, when applying the National Institute for Health and Care Excellence threshold. Annual screening alternating MRI and mammography between the ages of 35 and 60 years, followed by the national screening program, gave similar outcomes. Higher thresholds would favor annual MRI screening. The ICER was most sensitive to the unit cost of MRI and the utility value for ductal carcinoma in situ and localized breast cancer.CONCLUSIONS AND RELEVANCE This study suggests that MRI screening every 18 months between the ages of 35 and 60 years for women with a family history of breast cancer is cost-effective within the National Institute for Health and Care Excellence threshold for all densities. Higher thresholds would favor annual MRI screening. These outcomes support a change of current screening guidelines for this specific risk group and support MRI screening.

Aim Oligometastatic breast cancer (OMBC) is a disease-entity with potential for long-term remission in selected patients. Those with truly limited metastatic load (rather than occult widespread metastatic disease) may benefit from multimodality treatment including local ablative therapy of distant metastases. In this systematic review, we studied factors associated with long-term survival in patients with OMBC. Methods Eligible studies included patients with OMBC who received a combination of local and systemic therapy as multimodal approach and reported overall survival (OS) or progression-free survival (PFS), or both. The Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of each included study. Independent prognostic factors for OS and/or PFS are summarized. Results Of 1271 screened abstracts, 317 papers were full-text screened and twenty studies were included. Eleven of twenty studies were classified as acceptable quality. Definition of OMBC varied between studies and mostly incorporated the number and/or location of metastases. The 5-year OS ranged between 30 and 79% and 5-year PFS ranged between 25 and 57%. Twelve studies evaluated prognostic factors for OS and/or PFS in multivariable models. A solitary metastasis, >24 months interval between primary tumor and OMBC, no or limited involved axillary lymph nodes at primary diagnosis, and hormone-receptor positivity were associated with better outcome. HER2-positivity was associated with worse outcome, but only few patients received anti-HER2 therapy. Conclusions OMBC patients with a solitary distant metastasis and >24 months disease-free interval have the best OS and may be optimal candidates to consider a multidisciplinary approach.

Contains fulltext : 232918.pdf (Publisher’s version ) (Open Access) PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). RESULTS: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96). CONCLUSIONS: The proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

Purpose BRCA2 mutation carriers are offered annual breast screening with MRI and mammography. The aim of this study was to investigate the supplemental value of mammographic screening over MRI screening alone. Methods In this multicenter study, proven BRCA2 mutation carriers, who developed breast cancer during screening using both digital mammography and state-of-art breast MRI, were identified. Clinical data were reviewed to classify cases in screen-detected and interval cancers. Imaging was reviewed to assess the diagnostic value of mammography and MRI, using the Breast Imaging and Data System (BI-RADS) classification allocated at the time of diagnosis. Results From January 2003 till March 2019, 62 invasive breast cancers and 23 ductal carcinomas in situ were diagnosed in 83 BRCA2 mutation carriers under surveillance. Overall screening sensitivity was 95.2% (81/85). Four interval cancers occurred (4.7% (4/85)). MRI detected 73 of 85 breast cancers (sensitivity 85.8%) and 42 mammography (sensitivity 49.9%) (p Eight mammography-only lesions occurred. In 1 of 17 women younger than 40 years, a 6-mm grade 3 DCIS, retrospectively visible on MRI, was detected with mammography only in a 38-year-old woman. The other 7 mammography-only breast cancers were diagnosed in women aged 50 years and older, increasing sensitivity in this subgroup from 79.5% (35/44) to 95.5% (42/44) (p ≤ 0.001). Conclusions In BRCA2 mutation carriers younger than 40 years, the benefit of mammographic screening over MRI was very small. In carriers of 50 years and older, mammographic screening contributed significantly. Hence, we propose to postpone mammographic screening in BRCA2 mutation carriers to at least age 40.

Due to the shift towards minimal invasive treatment, accurate tumor size estimation is essential for small breast cancers. The purpose of this study was to determine the reliability of MRI-based tumor size measurements with respect to clinical, histological and radiomics characteristics in small invasive or in situ carcinomas of the breast to select patients for minimal invasive therapy.All consecutive cases of cT1 invasive breast carcinomas that underwent pre-operative MRI, treated in two hospitals between 2005 and 2016, were identified retrospectively from the Dutch cancer registry and cross-correlated with local databases. Concordance between MRI-based measurements and final pathological size was analyzed. The influence of clinical, histological and radiomics characteristics on the accuracy of MRI size measurements were analyzed.Analysis included 343 cT1 breast carcinomas in 336 patients (mean age, 55 years; range, 25-81 years). Overall correlation of MRI measurements with pathology was moderately strong (ρ = 0.530, P 0.001), in 42 cases (12.2%) MRI underestimated the size with more than 5 mm. Underestimation occurs more often in grade 2 and grade 3 disease than in low grade invasive cancers. In DCIS the frequency of underestimation is higher than in invasive breast cancer. Unfortunately, none of the patient, imaging or biopsy characteristics appeared predictive for underestimation.Size measurements of small breast cancers on breast MRI are within 5 mm of pathological size in 88% of patients. Nevertheless, underestimation cannot be adequately predicted, particularly for grade 2 and grade 3 tumors, which may hinder patient selection for minimal invasive therapy.

Item does not contain fulltext Screening for breast cancer reduces breast cancer-related mortality and earlier detection facilitates less aggressive treatment. Unfortunately, current screening modalities are imperfect, suffering from limited sensitivity and high false-positive rates. Novel techniques in the field of breast imaging may soon play a role in breast cancer screening: digital breast tomosynthesis, contrast material-enhanced spectral mammography, US (automated three-dimensional breast US, transmission tomography, elastography, optoacoustic imaging), MRI (abbreviated and ultrafast, diffusion-weighted imaging), and molecular breast imaging. Artificial intelligence and radiomics have the potential to further improve screening strategies. Furthermore, nonimaging-based screening tests such as liquid biopsy and breathing tests may transform the screening landscape. (c) RSNA, 2020 Online supplemental material is available for this article.

Purpose To assess the ability of optoacoustic (OA) ultrasonography (US) to help correctly downgrade benign masses classified as Breast Imaging Reporting and Data System (BI-RADS) 4a and 4b to BI-RADS 3 or 2. Materials and Methods OA/US technology uses laser light to detect relative amounts of oxygenated and deoxygenated hemoglobin in and around suspicious breast masses. In this prospective, multicenter study, results of 209 patients with 215 breast masses classified as BI-RADS 4a or 4b at US are reported. Patients were enrolled between 2015 and 2016. Masses were first evaluated with US with knowledge of previous clinical information and imaging results, and from this information a US imaging-based probability of malignancy (POM) and BI-RADS category were assigned to each mass. The same masses were then re-evaluated at OA/US. During the OA/US evaluation, radiologists scored five OA/US features, and then reassigned an OA/US-based POM and BI-RADS category for each mass. BI-RADS downgrade and upgrade percentages at OA/US were assessed by using a weighted sum of the five OA feature scores. Results At OA/US, 47.9% (57 of 119; 95% CI: 0.39, 0.57) of benign masses classified as BI-RADS 4a and 11.1% (three of 27; 95% CI: 0.03, 0.28) of masses classified as BI-RADS 4b were correctly downgraded to BI-RADS 3 or 2. Two of seven malignant masses classified as BI-RADS 4a at US were incorrectly downgraded, and one of 60 malignant masses classified as BI-RADS 4b at US was incorrectly downgraded for a total of 4.5% (three of 67; 95% CI: 0.01, 0.13) false-negative findings. Conclusion At OA/US, benign masses classified as BI-RADS 4a could be downgraded in BI-RADS category, which would potentially decrease biopsies negative for cancer and short-interval follow-up examinations, with the limitation that a few masses may be inappropriately downgraded.

Purpose: To evaluate the real-life performance of a breast cancer screening program for women with different categories of increased breast cancer risk with multiple follow-up rounds in an academic hospital with a large screening population. Materials and Methods: Screening examinations (magnetic resonance [MR] imaging and mammography) for women at increased breast cancer risk (January 1, 2003, to January 1, 2014) were evaluated. Risk category, age, recall for workup of screening-detected abnormalities, biopsy, and histopathologic diagnosis were recorded. Recall rate, biopsy rate, positive predictive value of recall, positive predictive value of biopsy, cancer detection rate, sensitivity, and specificity were calculated for first and follow-up rounds. Results: There were 8818 MR and 6245 mammographic examinations performed in 2463 women. Documented were 170 cancers; of these, there were 129 screening-detected cancers, 16 interval cancers, and 25 cancers discovered at prophylactic mastectomy. Overall sensitivity was 75.9% including the cancers discovered at prophylactic mastectomy (95% confidence interval: 69.5%, 82.4%) and 90.0% excluding those cancers (95% confidence interval: 83.3%, 93.7%). Sensitivity was lowest for carriers of the BRCA1 mutation (66.1% and 81.3% when including and not including cancers in prophylactic mastectomy specimens, respectively). Specificity was higher at follow-up (96.5%; 95% confidence interval: 96.0%, 96.9%) than in first rounds (85.1%; 95% confidence interval: 83.4%, 86.5%) and was high for both MR imaging (97.1%; 95% confidence interval: 96.7%, 97.5%) and mammography (98.7%; 95% confidence interval: 98.3%, 99.0%). Positive predictive value of recall and positive predictive value of biopsy were lowest in women who had only a family history of breast cancer. Conclusion: Screening performance was dependent on risk category. Sensitivity was lowest in carriers of the BRCA1 mutation. The specificity of high-risk breast screening improved at follow-up rounds.

To compare breast cancer detection and depiction between planar synthetic mammography (SM) and rotating synthetic mammography (RM) generated from digital breast tomosynthesis (DBT).In a fully-crossed multi-reader multi-case (MRMC) study, three radiologists retrospectively reviewed 190 cases (27 malignant, 31 benign, 132 normal), once with SM alone and once with RM alone, the DBT stack of slices was not reviewed. Lesions were scored using BI-RADS® and level of suspiciousness (1-10). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were computed using MRMC Analysis of Variance using the open-access software iMRMC. Additionally, readers were asked to make a visual grading analysis (VGA) on visibility of calcifications and soft tissue lesions (1-5 scale with 5 = Excellent visualization). The VGA scores were analyzed using the visual grading characteristics (VGC) method.On average, the AUC was similar between SM and RM (0.66 versus 0.67, P = 0.818). The sensitivity was equivalent (0.62 versus 0.60, P = 0.794), while specificity was significantly lower in SM than in RM (0.66 versus 0.72, P = 0.028). Radiologists significantly (P 0.05) preferred the display of all types of lesions in RM over SM. The average reading time per case was higher for RM than for SM (30 s versus 23 s, P 0.05).Radiologists achieve similar cancer detection with RM as with SM. They prefer the 3D-like rotating representation of soft tissue lesions and calcifications in comparison to the 2D visualization, which might improve their specificity, but at the expense of longer reading time.

Item does not contain fulltext In the past four decades, a variety of methods for minimal or non-invasive diagnosis and treatment of breast cancer have been introduced. Although breast imaging has become more and more specific for diagnosis, specimen biopsy with histopathological confirmation is still necessary. Core-needle biopsy under ultrasound guidance is the appropriate first choice for the diagnosis of most lesions. Fine-needle aspiration is of interest for identification of the presence of metastatic disease in abnormal lymph nodes. For microcalcifications, vacuum-assisted biopsy is recommended, especially with stereotactic guidance. In recent years different therapeutic techniques have been developed for the treatment of solid lesions, including breast cancer. Certainly, with the improvement of technology and medical scientific progress, it is becoming more common to use minimal- or non-invasive therapies. The proposed minimally invasive techniques may offer complete treatment of breast cancer, with better cosmetic results, less psychological stress, and shorter hospital stays. In this article, the strengths and weaknesses of the different diagnostic and therapeutic techniques are presented, and promising techniques for the future are discussed.

Background The image quality of digital breast tomosynthesis (DBT) volumes depends greatly on the reconstruction algorithm. Purpose To compare two DBT reconstruction algorithms used by the Siemens Mammomat Inspiration system, filtered back projection (FBP), and FBP with iterative optimizations (EMPIRE), using qualitative analysis by human readers and detection performance of machine learning algorithms. Material and Methods Visual grading analysis was performed by four readers specialized in breast imaging who scored 100 cases reconstructed with both algorithms (70 lesions). Scoring (5-point scale: 1 = poor to 5 = excellent quality) was performed on presence of noise and artifacts, visualization of skin-line and Cooper’s ligaments, contrast, and image quality, and, when present, lesion visibility. In parallel, a three-dimensional deep-learning convolutional neural network (3D-CNN) was trained (n = 259 patients, 51 positives with BI-RADS 3, 4, or 5 calcifications) and tested (n = 46 patients, nine positives), separately with FBP and EMPIRE volumes, to discriminate between samples with and without calcifications. The partial area under the receiver operating characteristic curve (pAUC) of each 3D-CNN was used for comparison. Results EMPIRE reconstructions showed better contrast (3.23 vs. 3.10, P = 0.010), image quality (3.22 vs. 3.03, P Conclusion The new algorithm provides DBT volumes with better contrast and image quality, fewer artifacts, and improved visibility of calcifications for human observers, as well as improved detection performance with deep-learning algorithms.

While magnetic resonance imaging (MRI) is considered a helpful diagnostic tool in breast imaging, discussions are ongoing about appropriate protocols and indications. The European Society of Breast Imaging (EUSOBI) launched a survey to evaluate the utilisation of breast MRI in clinical practice. An online survey reviewed by the EUSOBI board and committees was distributed amongst members. The questions encompassed: training and experience; annual breast MRI and MRI-guided-intervention workload; examination protocols; indications; reporting habits and preferences. Data were summarised and subgroups compared using χ2 test. Of 647 EUSOBI members, 177 (27.4%) answered the survey. The majority were radiologists (90.5%), half of them based in academic centres (51.9%). Common indications for MRI included cancer staging, treatment monitoring, high-risk screening and problem-solving, and differed significantly between countries (p≤0.03). Structured reporting and BI-RADS were mostly used. Breast radiologists with ≤10 years of experience preferred inclusion of additional techniques, such as T2/STIR (p=0.03) and DWI (p=0.08) in the scan protocol. MRI-guided interventions were performed by a minority of participants (35.4%). The utilisation of breast MRI in clinical practice is generally in line with international recommendations. There are substantial differences between countries. MRI-guided interventions and functional MRI parameters are not widely available. • MRI is commonly used for the detection and characterisation of breast lesions. • Clinical practice standards are generally in line with current recommendations. • Standardised criteria and diagnostic categories (mainly BI-RADS) are widely adopted. • Younger radiologists value additional techniques, such as T2/STIR and DWI. • MRI-guided breast biopsy is not widely available.

Contains fulltext : 174846.pdf (Publisher’s version ) (Closed access) OBJECTIVES: To investigate time to enhancement (TTE) as novel dynamic parameter for lesion classification in breast magnetic resonance imaging (MRI). METHODS: In this retrospective study, 157 women with 195 enhancing abnormalities (99 malignant and 96 benign) were included. All patients underwent a bi-temporal MRI protocol that included ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (1.0x0.9x2.5mm, temporal resolution 4.32s), during the inflow of contrast agent. TTE derived from TWIST series and relative enhancement versus time curve type derived from volumetric interpolated breath-hold examination (VIBE) series were assessed and combined with basic morphological information to differentiate benign from malignant lesions. Receiver operating characteristic analysis and kappa statistics were applied. RESULTS: TTE had a significantly better discriminative ability than curve type (p

Contains fulltext : 235583.pdf (Publisher’s version ) (Open Access) Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the introduction of digitization, the computer interpretation of images has been a subject of intense interest, resulting in the introduction of computer-aided detection (CADe) and diagnosis (CADx) algorithms in the early 2000's. Although they were introduced with high expectations, the potential improvement in the clinical realm failed to materialize, mostly due to the high number of false positive marks per analyzed image. In the last five years, the artificial intelligence (AI) revolution in computing, driven mostly by deep learning and convolutional neural networks, has also pervaded the field of automated breast cancer detection in digital mammography and digital breast tomosynthesis. Research in this area first involved comparison of its capabilities to that of conventional CADe/CADx methods, which quickly demonstrated the potential of this new technology. In the last couple of years, more mature and some commercial products have been developed, and studies of their performance compared to that of experienced breast radiologists are showing that these algorithms are on par with human-performance levels in retrospective data sets. Although additional studies, especially prospective evaluations performed in the real screening environment, are needed, it is becoming clear that AI will have an important role in the future breast cancer screening realm. Exactly how this new player will shape this field remains to be determined, but recent studies are already evaluating different options for implementation of this technology. The aim of this review is to provide an overview of the basic concepts and developments in the field AI for breast cancer detection in digital mammography and digital breast tomosynthesis. The pitfalls of conventional methods, and how these are, for the most part, avoided by this new technology, will be discussed. Importantly, studies that have evaluated the current capabilities of AI and proposals for how these capabilities should be leveraged in the clinical realm will be reviewed, while the questions that need to be answered before this vision becomes a reality are posed.

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.

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Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence–based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists—especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images. © RSNA, 2019 Online supplemental material is available for this article.

Contains fulltext : 208973.pdf (Publisher’s version ) (Open Access) MRI of the breast has the highest sensitivity for breast cancer detection among current clinical imaging modalities and is indispensable for breast imaging practice. While the basis of breast MRI consists of T1-weighted contrast-enhanced imaging, T2-weighted, ultrafast, and diffusion-weighted imaging may be used to improve lesion characterization. Such multiparametric assessment of breast lesions allows for excellent discrimination between benign and malignant breast lesions. Indications for breast MRI are expanding. In preoperative staging, multiple studies confirm the superiority of MRI to other imaging modalities for tumor size estimation and detection of additional tumor foci in the ipsilateral and contralateral breast. Ongoing studies show that in experienced hands this can be used to improve breast cancer surgery, although there is no evidence of improved long-term outcomes. Screening indications are likewise growing as evidence is accumulating that OncologicRI depicts cancers at an earlier stage than mammography in all women. To manage the associated costs for screening, the use of abbreviated protocols may be beneficial. In patients treated with neoadjuvant chemotherapy, MRI is used to document response. It is essential to realize that oncologic and surgical response are different, and evaluation should be adapted to the underlying question.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.

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