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A method called ‘soft thermal printing’ (STP) was developed to ensure the optimal transfer of 50 µm-thick dry epoxy resist films (DF-1050) on small-sized samples. The aim was the uniform fabrication of high aspect ratio polymer-based MOEMS (micro-optical-electrical-mechanical system) on small and/or fragile samples, such as GaAs. The printing conditions were optimized, and the resulting thickness uniformity profiles were compared to those obtained via lamination and SU-8 standard spin-coating. Under the best conditions tested, STP and lamination produced similar results, with a maximum deviation to the central thickness of 3% along the sample surface, compared to greater than 40% for SU-8 spin-coating. Both methods were successfully applied to the collective fabrication of DF1050-based MOEMS designed for the dynamic focusing of VCSELs (vertical-cavity surface-emitting lasers). Similar, efficient electro-thermo-mechanical behaviour was obtained in both cases. [ABSTRACT FROM AUTHOR]

In order to compensate for the loss of performance when scaling resonant sensors down to NEMS, it proves extremely useful to study the behavior of resonators up to very high displacements and hence high nonlinearities. This work describes a comprehensive nonlinear multiphysics model based on the Euler-Bernoulli equation which includes both mechanical and electrostatic nonlinearities valid up to displacements comparable to the gap in the case of an electrostatically actuated doubly clamped beam. Moreover, the model takes into account the fringing field effects, significant for thin resonators. The model has been compared to both numerical integrations and electrical measurements of devices fabricated on 200 mm SOI wafers; it shows very good agreement with both. An important contribution of this work is the provision for closed-form expressions of the critical amplitude and the pull-in domain initiation amplitude including all nonlinearities. This model allows designers to cancel out nonlinearities by tuning some design parameters and thus gives the possibility to drive the resonator beyond its critical amplitude. Consequently, the sensor performance can be enhanced to the maximum below the pull-in instability, while keeping a linear behavior. [ABSTRACT FROM AUTHOR]

Invasive lobular carcinoma (ILC) is the second-most common histologic subtype of breast cancer, constituting 5% to 15% of all breast cancers. It is characterized by an infiltrating growth pattern that may decrease detectability on mammography and US. The use of digital breast tomosynthesis (DBT) improves conspicuity of ILC, and sensitivity is 80% to 88% for ILC. Sensitivity of mammography is lower in dense breasts, and breast tomosynthesis has better sensitivity for ILC in dense breasts compared with digital mammography (DM). Screening US identifies additional ILCs even after DBT, with a supplemental cancer detection rate of 0 to 1.2 ILC per 1000 examinations. Thirteen percent of incremental cancers found by screening US are ILCs. Breast MRI has a sensitivity of 93% for ILC. Abbreviated breast MRI also has high sensitivity but may be limited due to delayed enhancement in ILC. Contrast-enhanced mammography has improved sensitivity for ILC compared with DM, with higher specificity than breast MRI. In summary, supplemental screening modalities increase detection of ILC, with MRI demonstrating the highest sensitivity., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Beyond the technical aspects, success and long-term patient outcomes of image-guided breast biopsies depend on the overall patient experience. Patient experience in turn is influenced by intangible factors, such as environmental features during the procedure; patient-centered communication prior to, during, and subsequent to the procedure; and management of expectations and biopsy complications. Here, we review evidence-based literature and results of a national Society of Breast Imaging survey on approaches to both mitigate and manage common image-guided core biopsy complications as well as nontechnical strategies to improve the patient biopsy experience., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Image-guided biopsy is an integral step in the diagnosis and management of suspicious image-detected breast or axillary lesions, allowing for accurate diagnosis and, if indicated, treatment planning. Tissue sampling can be performed under guidance of a full spectrum of breast imaging modalities, including stereotactic, tomosynthesis, sonographic, and MRI, each with its own set of advantages and limitations. Procedural planning, which includes consideration of technical, patient, and lesion factors, is vital for diagnostic accuracy and limitation of complications. The purpose of this paper is to review and provide guidance for breast imaging radiologists in selecting the best procedural approach for the individual patient to ensure accurate diagnosis and optimal patient outcomes. Common patient and lesion factors that may affect successful sampling and contribute to postbiopsy complications are reviewed and include obesity, limited patient mobility, patient motion, patients prone to vasovagal reactions, history of anticoagulation, and lesion location, such as proximity to vital structures or breast implant., (© Society of Breast Imaging 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Purpose: With transition from supine to prone position, tenting of the pectoralis major occurs, displacing the muscle from the chest wall and shifting the level I and II axillary spaces. For patients for whom we aim to treat the level I and II axillae using the prone technique, accurate delineation of these nodal regions is necessary. Although different consensus guidelines exist for delineation of nodal anatomy in supine position, to our knowledge, there are no contouring guidelines in the prone position that account for this change in nodal anatomy., Methods and Materials: The level I and II nodal contours from the Radiation Therapy Oncology Group (RTOG) breast cancer supine atlas were adapted for prone position by 2 radiation oncologists and a breast radiologist based on anatomic changes observed from supine to prone positioning on preoperative diagnostic imaging. Forty-three patients from a single institution treated with prone high tangents from 2012 to 2018 were identified as representative cases to delineate the revised level I and II axillae on noncontrast computed tomography (CT) scans obtained during radiation simulation. The revised nodal contours were reviewed by an expanded expert multidisciplinary panel including breast radiologists, radiation oncologists, and surgical oncologists for consistency and reproducibility., Results: Consensus was achieved among the panel in order to create modifications from the RTOG breast atlas for CT-based contouring of the level I and II axillae in prone position using bone, muscle, and skin as landmarks. This atlas provides representative examples and accompanying descriptions for the changes described to the caudal and anterior borders of level II and the anterior, posterior, medial, and lateral borders of level I. A step-by-step guide is provided for properly identifying the revised anterior border of the level I axilla., Conclusions: The adaptations to the RTOG breast cancer atlas for prone positioning will enable radiation oncologists to more accurately target the level I and II axillae when the axillae are targets in addition to the breast., (Copyright © 2024 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

Objective: The availability of same-day services in breast imaging is an important topic given potential advantages for timely diagnoses and patient experiences, but there are potential barriers that lead facilities to not offer these services. We sought to understand current practice patterns and radiologist perspectives on offering same-day services., Methods: The Society of Breast Imaging (SBI) Patient Care & Delivery Committee developed a 19-question survey that was emailed to all 3449 active members of the SBI in May 2023. An exemption from the institutional review board was obtained at the lead author's institution. The survey consisted of 19 questions that were designed to understand the scope, perceptions, barriers, and logistics of same-day services. Comparisons were made between responses for offering same-day services (screening interpretation, diagnostic examinations, biopsies) and respondent demographics., Results: A total of 437 American and Canadian members participated, yielding a response rate of 12.7%. Respondents were most commonly in private practice (43.0%, 188/437), working in an outpatient medical center-based clinic (41.9%, 183/437), and without trainees (64.5%, 282/437). Respondents estimated 12.1% of screening examinations were interpreted while patients waited, which was significantly more common in free-standing breast imaging clinics (P = .028) and practices without trainees (P = .036). Respondents estimated 15.0% of diagnostic examinations were performed same day, which was more common in academic and private practices (P = .03) and practices without trainees (P = .01). Respondents estimated 11.5% of biopsies were performed the same day as the recommendation, which had no association with practice type/context, presence of trainees, number of mammography units, number of radiologists, or number of technologists. Long patient travel distance and limited patient mobility were the most cited reasons for offering patients same-day services., Conclusion: Offering same-day breast imaging services varies among institutions and may be influenced by factors such as practice context and type and the presence of trainees., (© Society of Breast Imaging 2024. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This paper introduces the design of a reconfigurable transmitarray operating within the D-band frequency range (110-170 GHz). The transmitarray unit cell is composed of three metal layers and two quartz dielectric substrates. It achieves a 1-bit phase shift resolution through the alternating states of two innovative switches integrated into the active transmitting patch of the unit cell. To address the challenge of miniaturization in the D-band, compact switches compatible with the proposed unit cell dimensions are introduced. These switches are constructed using phase change materials (PCM) that change between amorphous and crystalline states when exposed to heat. The paper includes a full-wave simulation of the unit cell, demonstrating an insertion loss below 1.5 dB across a wide frequency band of 27%. Additionally, a 10 [Formula: see text] 10 elements transmitarray is synthesized using a numerical tool and its theoretical results are compared to full-wave electromagnetic simulations for validation purposes. The results indicate that by incorporating the proposed switches into the unit cell, the transmitarray achieves promising reconfiguration capabilities within the D-band. Moreover, the paper presents the architecture of a command line designed to bias the PCM switches. Notably, this command line represents a novel approach, as it enables individual biasing of each PCM switch using direct current (DC). The influence of these command lines on the transmitarray's performance is thoroughly investigated. Although there is a compromise in the 1-dB gain bandwidth, the overall behavior of the transmitarray remains encouraging., (© 2024. The Author(s).)

3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC), uses 77.98%-90.05% less GPU memory and 91.23%-96.02% less computation. While it is trained only with image-level labels, without segmentation labels, it explains its predictions by providing pixel-level saliency maps. On a dataset collected at NYU Langone Health, including 85,526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0.831 (95% CI: 0.769-0.887) in classifying breasts with malignant findings using 3D mammography. This is comparable to the performance of GMIC on FFDM (0.816, 95% CI: 0.737-0.878) and synthetic 2D (0.826, 95% CI: 0.754-0.884), which demonstrates that 3D-GMIC successfully classified large 3D images despite focusing computation on a smaller percentage of its input compared to GMIC. Therefore, 3D-GMIC identifies and utilizes extremely small regions of interest from 3D images consisting of hundreds of millions of pixels, dramatically reducing associated computational challenges. 3D-GMIC generalizes well to BCS-DBT, an external dataset from Duke University Hospital, achieving an AUC of 0.848 (95% CI: 0.798-0.896).

Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Beads based on a mannuronate(M)-rich alginate (86 % M units) were prepared by adding the polysaccharide solution to a crosslinking bath containing different concentrations (0.5, 2 and 10 wt%) of XCl 2 where X = Ca, Cu or Zn. Primarily focus was on Zn, due to its antioxidant, anti-inflammatory and anti-microbial capabilities. The beads were characterized by Field-Emission Scanning Electron Microscopy (FESEM), Fourier-Transform Infra-Red spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), Small-Angle X-ray Scattering (SAXS) and compression tests. The crosslinking agent significantly influenced the properties of the resulting beads. Specifically, Ca-based beads exhibited a smoother surface, while Cu- and Zn-based beads appeared rougher. Interestingly, Zn-based beads displayed a core-shell structure. Young moduli ranged from 3500 and 7000 MPa, with the highest values observed for Zn-beads. SAXS investigation at 0.5 wt% XCl 2 suggested increase in the densely packed domains amount in the order: Ca < Cu < Zn. Extended X-ray Absorption Fine Structure (EXAFS) showed that the coordination number was 4.3 ± 0.4 for Cu, and 4.0 ± 0.2 and 1.1 ± 0.1 for Zn in 0.5 wt% XCl 2 alginate xerogels, in agreement with reported Density Functional Calculations on Cu 2+ - and Zn 2+ -MM complexes. The results from FT-IR, compositional analysis and EXAFS collectively suggested a bridging coordination for these systems., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Breast cancer is the most common cancer in women, with the incidence rising substantially with age. Older women are a vulnerable population at increased risk of developing and dying from breast cancer. However, women aged 75 years and older were excluded from all randomized controlled screening trials, so the best available data regarding screening benefits and risks in this age group are from observational studies and modeling predictions. Benefits of screening in older women are the same as those in younger women: early detection of smaller lower-stage cancers, resulting in less invasive treatment and lower morbidity and mortality. Mammography performs significantly better in older women with higher sensitivity, specificity, cancer detection rate, and positive predictive values, accompanied by lower recall rates and false positives. The overdiagnosis rate is low, with benefits outweighing risks until age 90 years. Although there are conflicting national and international guidelines about whether to continue screening mammography in women beyond age 74 years, clinicians can use shared decision making to help women make decisions about screening and fully engage them in the screening process. For women aged 75 years and older in good health, continuing annual screening mammography will save the most lives. An informed discussion of the benefits and risks of screening mammography in older women needs to include each woman's individual values, overall health status, and comorbidities. This article will review the benefits, risks, and controversies surrounding screening mammography in women 75 years old and older and compare the current recommendations for screening this population from national and international professional organizations. © RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone., (© RSNA, 2023.)

The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, the use of DBT, as compared with DM, raises new challenges, including a larger number of acquired images and thus longer interpretation times. While most current artificial intelligence (AI) applications are developed for DM, there are multiple potential opportunities for AI to augment the benefits of DBT. During the diagnostic steps of lesion detection, characterization, and classification, AI algorithms may not only assist in the detection of indeterminate or suspicious findings but also aid in predicting the likelihood of malignancy for a particular lesion. During image acquisition and processing, AI algorithms may help reduce radiation dose and improve lesion conspicuity on synthetic two-dimensional DM images. The use of AI algorithms may also improve workflow efficiency and decrease the radiologist's interpretation time. There has been significant growth in research that applies AI to DBT, with several algorithms approved by the U.S. Food and Drug Administration for clinical implementation. Further development of AI models for DBT has the potential to lead to improved practice efficiency and ultimately improved patient health outcomes of breast cancer screening and diagnostic evaluation. See the invited commentary by Bahl in this issue. © RSNA, 2022.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a high sensitivity in detecting breast cancer but often leads to unnecessary biopsies and patient workup. We used a deep learning (DL) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. On the internal test set ( n = 3936 exams), our system achieved an area under the receiver operating characteristic curve (AUROC) of 0.92 (95% CI: 0.92 to 0.93). In a retrospective reader study, there was no statistically significant difference ( P = 0.19) between five board-certified breast radiologists and the DL system (mean ΔAUROC, +0.04 in favor of the DL system). Radiologists' performance improved when their predictions were averaged with DL's predictions [mean ΔAUPRC (area under the precision-recall curve), +0.07]. We demonstrated the generalizability of the DL system using multiple datasets from Poland and the United States. An additional reader study on a Polish dataset showed that the DL system was as robust to distribution shift as radiologists. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. Using decision curve analysis, we showed that the DL system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding biopsies yielding benign results in up to 20% of all patients with BI-RADS category 4 lesions. Last, we performed an error analysis, investigating situations where DL predictions were mostly incorrect. This exploratory work creates a foundation for deployment and prospective analysis of DL-based models for breast MRI.

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson's paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison., (© 2022. The Author(s).)

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis., (© 2021. The Author(s).)

Bismuth-antimony alloy (Bi 1 -  x Sb x ) is the first reported 3D topological insulator (TI). Among many TIs reported to date, it remains the most promising for spintronic applications thanks to its large conductivity, its colossal spin Hall angle, and the possibility to build low-current spin-orbit-torque magnetoresistive random access memories. Nevertheless, the 2D integration of TIs on industrial standards is lacking. In this work, we report the integration of high-quality rhombohedral BiSb(0001) topological insulators on a cubic GaAs(001) substrate. We demonstrate a clear epitaxial relationship at the interface, a fully relaxed TI layer, and the growth of a rhombohedral matrix on top of the cubic substrate. The antimony composition of the Bi 1 -  x Sb x layer is perfectly controlled and covers almost the whole TI window. For optimized growth conditions, the sample generates a semiconductor band structure at room temperature in the bulk and exhibits metallic surface states at 77 K.

Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and human epidermal growth factor 2-overexpressing breast cancers, as well as locally advanced and inflammatory breast cancers. The rationales for neoadjuvant therapy are to shrink tumor size and potentially decrease the extent of surgery, to serve as an in vivo test of response to therapy, and to reveal prognostic information for the patient. MRI is the most accurate modality to demonstrate response to therapy and to help ensure accurate presurgical planning. Changes in lesion diameter, volume, and enhancement are used to predict complete response, partial response, or nonresponse to therapy. However, residual disease may be overestimated or underestimated at MRI. Fibrosis, necrotic tumors, and residual benign masses may be causes of overestimation of residual disease. Nonmass lesions, invasive lobular carcinoma, hormone receptor-positive tumors, nonconcentric shrinkage patterns, the use of antiangiogenic therapy, and late-enhancing foci may be causes of underestimation of residual disease. In patients with known axillary lymph node metastasis, neoadjuvant therapy may be followed by targeted axillary dissection to avoid the potential morbidity associated with an axillary lymph node dissection. Diffusion-weighted imaging, radiomics, machine learning, and deep learning methods are under investigation to improve MRI accuracy in predicting treatment response. © RSNA, 2021.

Cancer is a major health issue concerning to all of us. Current treatment options are still limited due to not-selective action. Encapsulation is contemplated as an innovative approach to address systemic toxicity and tumor resistance caused by traditional therapies, while increasing encapsulated compounds bioavailability. The coating material of capsules strongly determines the success of the system. Since alginate has been proved non-toxic, biocompatible and biodegradable, it is considered a potential vehicle for therapeutic factors encapsulation. Besides, it has the particular ability to form hydrogels, which hold a high-water content and greatly resemble to natural soft tissues. The present review exposes the state-of-the-art and the most sophisticated alginate-based systems for cancer therapy and research. It begins with an overview of alginate hydrogels and the qualities that make them especially suitable for biomedical applications. In the following section, the application of alginate hydrogels as pioneering strategies for cancer treatment is described. Several examples of alginate-based delivery systems of therapeutic drugs, proteins and nucleic acids are provided. Significant emphasis is placed in both oral delivery systems and colorectal cancer therapy. Moreover, the role of alginate 3-D scaffolds for both cell culture and delivery is explained. Lastly, other applications of alginate-based hydrogels such as tumor biomarkers immunosensing and fluorescent surgical marker are included., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Breast MR imaging is the most sensitive imaging method for the detection of breast cancer and detects more aggressive malignancies than mammography and ultrasound examination. Despite these advantages, breast MR imaging has low use rates for breast cancer screening. Abbreviated breast MR imaging, in which a limited number of breast imaging sequences are obtained, has been proposed as a way to solve cost and patient tolerance issues while preserving the high cancer detection rate of breast MR imaging. This review discusses abbreviated breast MR imaging, including protocols, multicenter clinical trial results, clinical workflow implementation challenges, and future directions., Competing Interests: Disclosure The authors state no relevant conflict of interest or financial disclosures., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women., Competing Interests: Disclosure The authors have nothing to disclose pertaining to this work., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The goals of imaging after neoadjuvant therapy for breast cancer are to monitor the response to therapy and facilitate surgical planning. MRI has been found to be more accurate than mammography, ultrasound, or clinical exam in evaluating treatment response. However, MRI may both overestimate and underestimate residual disease. The accuracy of MRI is dependent on tumor morphology, histology, shrinkage pattern, and molecular subtype. Emerging MRI techniques that combine functional information such as diffusion, metabolism, and hypoxia may improve MR accuracy. In addition, machine-learning techniques including radiomics and radiogenomics are being studied with the goal of predicting response on pretreatment imaging. This article comprehensively reviews response assessment on breast MRI and highlights areas of ongoing research. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3 J. MAGN. RESON. IMAGING 2020;52:1587-1606., (© 2020 International Society for Magnetic Resonance in Medicine.)

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018., (© 2019 International Society for Magnetic Resonance in Medicine.)