Women 75 Years Old or Older: To Screen or Not to Screen?

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.

Problem-solving Breast MRI.

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.

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

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.

Bilateral gradient-echo spectroscopic imaging with correction of frequency variations for measurement of fatty acid composition in mammary adipose tissue.

PURPOSE: To develop a simultaneous dual-slab three-dimensional gradient-echo spectroscopic imaging (GSI) technique with frequency drift compensation for rapid (<6 min) bilateral measurement of fatty acid composition (FAC) in mammary adipose tissue. METHODS: A bilateral GSI sequence was developed using a simultaneous dual-slab excitation followed by 128 monopolar echoes. A short train of navigator echoes without phase or partition encoding was included at the beginning of each pulse repetition time period to correct for frequency variation caused by respiration and heating of the cryostat. Voxel-wise spectral fitting was applied to measure the areas of the lipid spectral peaks to estimate the number of double-bond (ndb), number of methylene-interrupted double-bond (nmidb), and chain length (cl). The proposed method was tested in an oil phantom and 10 postmenopausal women to assess the influence of the frequency variation on FAC estimation. RESULTS: The frequency drift observed over 5:27 min during the phantom scan was about 10 Hz. Phase correction based on the navigator reduced the median error of ndb, nmidb, and cl from 9.7%, 17.6%, and 3.2% to 2.1%, 9.5%, and 2.8%, respectively. The in vivo data showed a mean ± standard deviation frequency drift of 17.4 ± 2.5 Hz, with ripples at 0.3 ± 0.1 Hz. Our reconstruction algorithm successfully separated signals from the left and right breasts with negligible residual aliasing. Phase correction reduced the interquartile range within each subject's adipose tissue of ndb, nmidb, and cl by 18.4 ± 10.6%, 18.5 ± 13.9%, and 18.4 ± 10.6%, respectively. CONCLUSION: This study shows the feasibility of obtaining bilateral spectroscopic imaging data in the breast and that incorporation of a frequency navigator improves the estimation of FAC.

Prospective multicenter assessment of patient preferences for properties of gadolinium-based contrast media and their potential socioeconomic impact in a screening breast MRI setting.

OBJECTIVE: It is unknown how patients prioritize gadolinium-based contrast media (GBCM) benefits (detection sensitivity) and risks (reactions, gadolinium retention, cost). The purpose of this study is to measure preferences for properties of GBCM in women at intermediate or high risk of breast cancer undergoing annual screening MRI. METHODS: An institutional reviewed board-approved prospective discrete choice conjoint survey was administered to patients at intermediate or high risk for breast cancer undergoing screening MRI at 4 institutions (July 2018-March 2020). Participants were given 15 tasks and asked to choose which of two hypothetical GBCM they would prefer. GBCMs varied by the following attributes: sensitivity for cancer detection (80-95%), intracranial gadolinium retention (1-100 molecules per 100 million administered), severe allergic-like reaction rate (1-19 per 100,000 administrations), mild allergic-like reaction rate (10-1000 per 100,000 administrations), out-of-pocket cost ($25-$100). Attribute levels were based on published values of existing GBCMs. Hierarchical Bayesian analysis was used to derive attribute "importance." Preference shares were determined by simulation. RESULTS: Response (87% [247/284]) and completion (96% [236/247]) rates were excellent. Sensitivity (importance = 44.3%, 95% confidence interval = 42.0-46.7%) was valued more than GBCM-related risks (mild allergic-like reaction risk (19.5%, 17.9-21.1%), severe allergic-like reaction risk (17.0%, 15.8-18.1%), intracranial gadolinium retention (11.6%, 10.5-12.7%), out-of-pocket expense (7.5%, 6.8-8.3%)). Lower income participants placed more importance on cost and less on sensitivity (p < 0.01). A simulator is provided that models GBCM preference shares by GBCM attributes and competition. CONCLUSIONS: Patients at intermediate or high risk for breast cancer undergoing MRI screening prioritize cancer detection over GBCM-related risks, and prioritize reaction risks over gadolinium retention. KEY POINTS: • Among women undergoing annual breast MRI screening, cancer detection sensitivity (attribute "importance," 44.3%) was valued more than GBCM-related risks (mild allergic reaction risk 19.5%, severe allergic reaction risk 17.0%, intracranial gadolinium retention 11.6%, out-of-pocket expense 7.5%). • Prospective four-center patient preference data have been incorporated into a GBCM choice simulator that allows users to input GBCM properties and calculate patient preference shares for competitor GBCMs. • Lower-income women placed more importance on out-of-pocket cost and less importance on cancer detection (p < 0.01) when prioritizing GBCM properties.

Breast MRI for Evaluation of Response to Neoadjuvant Therapy.

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.

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer.

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.

Atypical Ductal Hyperplasia and Lobular Neoplasia: Update and Easing of Guidelines.

OBJECTIVE. Atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), and lobular carcinoma in situ (LCIS) are among high-risk lesions that have been previously recommended for surgical excision when diagnosed on core needle biopsy. Recent studies have examined whether imaging surveillance is a reasonable alternative to surgical management for these lesions. This article synthesizes the evidence regarding management of atypical hyperplasia and LCIS diagnosed on core needle biopsy and clinical implications of these diagnoses on future breast cancer risk as well as highlights areas of further research needed to improve practice guidelines for these high-risk lesions. CONCLUSION. Although surgical excision is still recommended after diagnosis of ADH on core needle biopsy, in specific circumstances ALH and LCIS can safely be managed by imaging surveillance.

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence.

OBJECTIVE: The purpose of this article is to compare traditional versus machine learning-based computer-aided detection (CAD) platforms in breast imaging with a focus on mammography, to underscore limitations of traditional CAD, and to highlight potential solutions in new CAD systems under development for the future. CONCLUSION: CAD development for breast imaging is undergoing a paradigm shift based on vast improvement of computing power and rapid emergence of advanced deep learning algorithms, heralding new systems that may hold real potential to improve clinical care.

Overstated Harms of Breast Cancer Screening? A Large Outcomes Analysis of Complications Associated With 9-Gauge Stereotactic Vacuum-Assisted Breast Biopsy.

OBJECTIVE: The purpose of this study was to assess the rate, type, and severity of complications related to 9-gauge stereotactic vacuum-assisted breast biopsy (SVAB) and to delineate associated factors that may contribute to a higher rate of complications. MATERIALS AND METHODS: This retrospective study included 4776 patients who underwent SVAB between 2003 and 2016. A total of 319 patients with documented postbiopsy complications were identified. Complications were subcategorized as bleeding, pain, lightheadedness, bruising, and other complications, and their severity was classified as minor, moderate, or severe. Hematoma volumes were correlated with biopsy location and complication severity. A group of control subjects who underwent SVAB but had no complications was compared with the group of study patients with regard to age, biopsy location, lesion type, and pathologic findings. Postbiopsy screening adherence was assessed. Statistical analyses were performed using the Fisher exact, Mann-Whitney, Kruskal-Wallis, and Spearman rank correlation tests. RESULTS: Of the 319 patients with complications who were identified (representing 6.7% of the 4776 patients who underwent SVAB), 307 (96.2%) had mild complications, 12 (3.8%) had moderate complications, and no patients had severe complications. The most common complication was bleeding or hematoma (89.3% of patients [285/319]), followed by pain (6.9% [22/319]), lightheadedness (0.9% [3/319]), bruising (0.9% [3/319]), and other complications (1.9% [6/319]). No significant differences were noted between the study group and the control group in terms of age (p = 0.474), biopsy location (p = 0.065), histologic findings (p = 0.056), or lesion type (p = 0.568). Hematoma volume (median, 7.5 cm3) did not correspond to the severity of complications. Larger hematoma volumes were associated with a posterior biopsy location (p = 0.008). The rate of return to annual screening after biopsy was not adversely affected by the presence of biopsy complications. CONCLUSION: Clinically significant complications associated with SVAB were exceedingly rare (0.3%) in this large study spanning 13 years.

What Happens after a Diagnosis of High-Risk Breast Lesion at Stereotactic Vacuum-assisted Biopsy? An Observational Study of Postdiagnosis Management and Imaging Adherence.

Purpose To assess adherence with annual or biennial screening mammography after a diagnosis of high-risk lesion(s) at stereotactic biopsy with or without surgical excision and to identify clinical factors that may affect screening adherence after a high-risk diagnosis. Materials and Methods This institutional review board-approved HIPAA-compliant retrospective study included 208 patients who underwent stereotactic biopsy between January 2012 and December 2014 that revealed a high-risk lesion. Whether the patient underwent surgical excision and/or follow-up mammography was documented. Adherence of these women to a protocol of subsequent mammography within 1 year (9-18 months) or within 2 years (9-30 months) was compared with that of 45 508 women with normal screening mammograms who were imaged during the same time period at the same institution. Possible factors relevant to postdiagnosis management and screening adherence were assessed. Consultation with a breast surgeon was identified by reviewing clinical notes. Uptake of pharmacologic chemoprevention following diagnosis (patient decision to take chemopreventive medications) was assessed. The Fisher exact test was used to compare annual or biennial screening adherence rates. Binary logistic regression was used to identify factors predictive of whether women returned for screening within selected time frames. Results In total, 913 (1.3%) of 67 874 women were given a recommendation to undergo stereotactic biopsy, resulting in diagnosis of 208 (22.8%) of 913 high-risk lesions. Excluding those with a prior personal history of breast cancer or upgrade to cancer at surgery, 124 (66.7%) of 186 women underwent surgery and 62 (33.3%) did not. Overall post-high-risk diagnosis adherence to annual or biennial mammography was similar to that in control subjects (annual, 56.4% vs 50.8%, P = .160; biennial, 62.0% vs 60.1%, P = .630). Adherence was significantly better in the surgical group than in the nonsurgical group for annual mammography (70.0% vs 32.0%; odds ratio [OR] = 5.0; 95% confidence interval [CI]: 2.4, 10.1; P < .001) and for biennial mammography (74.3% vs 40.0%; OR = 4.3; 95% CI: 2.1, 8.8; P < .001). Among the patients in the nonsurgical group, those adherent to annual or biennial mammography were significantly more likely to have seen a breast surgeon than the nonadherent women (annual, 77.3% vs 35.7%, P = .005; biennial, 67.9% vs 36.4%, P = .045). All patients receiving chemopreventive agents underwent a surgical consultation (100%; n = 21). Conclusion Although diagnosis of a high-risk lesion at stereotactic breast biopsy did not compromise overall adherence to subsequent mammographic screening, patients without surgical excision, particularly those who did not undergo a surgical consultation, had significantly lower imaging adherence and chemoprevention uptake as compared with their counterparts who underwent surgery, suggesting that specialist care may be important in optimizing management. © RSNA, 2018.

Feasibility analysis of early temporal kinetics as a surrogate marker for breast tumor type, grade, and aggressiveness.

BACKGROUND: Screening breast MRI has been shown to preferentially detect high-grade ductal carcinoma in situ (DCIS) and invasive carcinoma, likely due to increased angiogenesis resulting in early initial uptake of contrast. As interest grows in abbreviated screening breast MRI (AB-MRI), markers of early contrast washin that can predict tumor grade and potential aggressiveness are of clinical interest. PURPOSE: To evaluate the feasibility of using the initial enhancement ratio (IER) as a surrogate marker for tumor grade, hormone receptor status, and prognostic markers, as an initial step to being incorporated into AB-MRI. STUDY TYPE: Retrospective. SUBJECTS: In all, 162 women (mean 55.0 years, range 32.8-87.7 years) with 187 malignancies imaged January 2012-November 2015. FIELD STRENGTH/SEQUENCE: Images were acquired at 3.0T with a T1 -weighted gradient echo fat-suppressed-volume interpolated breath-hold sequence. ASSESSMENT: Subjects underwent dynamic contrast-enhanced breast MRI with a 7-channel breast coil. IER (% signal increase over baseline at the first postcontrast acquisition) was assessed and correlated with background parenchymal enhancement, washout curves, stage, and final pathology. STATISTICAL TESTS: Chi-square test, Spearman rank correlation, Mann-Whitney U-tests, Bland-Altman analysis, and receiver operating characteristic curve analysis. RESULTS: IER was higher for invasive cancer than for DCIS (R1/R2, P < 0.001). IER increased with tumor grade (R1: r = 0.56, P < 0.001, R2: r = 0.50, P < 0.001), as ki-67 increased (R1: r = 0.35, P < 0.001; R2 r = 0.35, P < 0.001), and for node-positive disease (R1/R2, P = 0.001). IER was higher for human epidermal growth factor receptor two-positive and triple negative cancers than for estrogen receptor-positive / progesterone receptor-positive tumors (R1 P < 0.001-0.002; R2 P = 0.0.001-0.011). IER had higher sensitivity (80.6% vs. 75.5%) and specificity (55.8% vs. 48.1%) than washout curves for positive nodes, higher specificity (48.1% vs. 36.5%) and positive predictive value (70.2% vs. 66.7%) for high ki-67, and excellent interobserver agreement (intraclass correlation coefficient = 0.82). DATA CONCLUSION: IER, a measurement of early contrast washin, is associated with higher-grade malignancies and tumor aggressiveness and might be potentially incorporated into an AB-MRI protocol. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1692-1700.

Radiologic-Pathologic Discordance and Outcome After MRI-Guided Vacuum-Assisted Biopsy.

OBJECTIVE: The purpose of this study was to determine the rate, characteristics, and outcomes of discordant MRI-guided vacuum-assisted biopsy (VAB) in women with suspected breast cancer. MATERIALS AND METHODS: This retrospective study reviewed 1314 MRI-guided VABs performed in 1211 women between 2007 and 2013 and yielded 25 discordant results in 24 women. MRI characteristics; BI-RADS assessments; whether the lesion was missed, partially sampled, or excised at biopsy; and biopsy and surgical pathology results were reviewed. Statistical analyses were performed using Fisher exact and Mann-Whitney U tests. RESULTS: Among 1314 lesions that underwent MRI-guided VAB, 25 results were discordant (1.9%; 95% CI, 1.2-2.8%), and nine lesions with discordant results (36.0%, 95% CI, 18.5-56.9%) were malignant at surgical excision (three invasive ductal carcinoma and six ductal carcinoma in situ). There was no significant association between malignancy and lesion type, size, enhancement pattern, BI-RADS assessment, or clinical indication. Forty-four percent (11/25) of discordant lesions were missed, 48.0% (12/25) were partially sampled, and 8.0% (2/25) appeared to have been excised. Of the nine malignant lesions, 44.4% (4/9) discordant malignant lesions were missed, 44.4% (4/9) were partially sampled, and 11.1% (1/9) appeared to have been excised. Lesion sizes and types were similar in the missed and partially excised groups. CONCLUSION: The potential for false-negative results at MRI-guided VAB underscores the importance of radiologic-histologic correlation and imaging review after biopsy. Rebiopsy or excision in discordant cases is therefore recommended.

Evaluation of Breast Lipid Composition in Patients with Benign Tissue and Cancer by Using Multiple Gradient-Echo MR Imaging.

Purpose To demonstrate the feasibility of the use of a rapid, noninvasive, in vivo imaging method to measure fatty acid fractions of breast adipose tissue during diagnostic breast magnetic resonance (MR) examinations and to investigate associations between fatty acid fractions in breast adipose tissue and breast cancer status by using this method. Materials and Methods The institutional review board approved this retrospective HIPAA-compliant study and informed consent was waived. Between July 2013 and September 2014, multiple-echo three-dimensional gradient-echo data were acquired for 89 women. Spectra were generated and used to estimate fractions of monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and saturated fatty acid (SFA) in the breast adipose tissue. Analysis of covariance and exact Mann-Whitney tests were used to compare groups and the Spearman rank correlation coefficient was used to characterize the association of each imaging measure with each attribute. Results For postmenopausal women, MUFA was lower (0.38 ± 0.06 vs 0.46 ± 0.10; P < .05) and SFA was higher (0.31 ± 0.07 vs 0.19 ± 0.11; P < .05) for women with invasive ductal carcinoma than for those with benign tissue. No correlation was found between body mass index (BMI) and fatty acid fractions in breast adipose tissue. In women with benign tissue, postmenopausal women had a higher PUFA (0.35 ± 0.06 vs 0.27 ± 0.05; P < .01) and lower SFA (0.19 ± 0.11 vs 0.30 ± 0.12; P < .05) than premenopausal women. Conclusion There is a possible link between the presence of invasive ductal carcinoma and fatty acid fractions in breast adipose tissue for postmenopausal women in whom BMI values are not correlated with the fatty acid fractions. (©) RSNA, 2016 Online supplemental material is available for this article.

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.

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).

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer.

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Monitoring Response to Neoadjuvant Systemic Therapy for Breast Cancer: 2022 Update.

Imaging plays a vital role in managing patients undergoing neoadjuvant chemotherapy, as treatment decisions rely heavily on accurate assessment of response to therapy. This document provides evidence-based guidelines for imaging breast cancer before, during, and after initiation of neoadjuvant chemotherapy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Improving breast cancer diagnostics with deep learning for MRI.

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.

ACR Appropriateness Criteria® Evaluation of Nipple Discharge: 2022 Update.

The type of nipple discharge dictates the appropriate imaging study. Physiologic nipple discharge is common and does not require diagnostic imaging. Pathologic nipple discharge in women, men, and transgender patients necessitates breast imaging. Evidence-based guidelines were used to evaluate breast imaging modalities for appropriateness based on patient age and gender. For an adult female or male 40 years of age or greater, mammography or digital breast tomosynthesis (DBT) is performed initially. Breast ultrasound is usually performed at the same time with rare exception. For males or females 30 to 39 years of age, mammography/DBT or breast ultrasound is performed based on institutional preference and individual patient considerations. For young women less than 30 years of age, ultrasound is performed first with mammography/DBT added if there are suspicious findings or if the patient is at elevated lifetime risk for developing breast cancer. There is a high incidence of breast cancer in males with pathologic discharge. Men 25 years and older should be evaluated using mammography/DBT and ultrasound added when indicted. In transfeminine (male-to-female) patients, mammography/DBT and ultrasound are useful due to the increased incidence of breast cancer. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer-reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which peer-reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.