The frequency of missed breast cancers in women participating in a high-risk MRI screening program

Reducing Errors in Breast Imaging: Insights From Missed and Near-Missed Cases

Errors and misdiagnosis in breast imaging are significant concerns for breast imaging radiologists due to the negative impacts on patients and the high legal risks. Using missed and nearly missed diagnoses of breast cancer cases, this article introduces radiologists to common factors contributing to errors and misdiagnosis in breast imaging, including radiologist errors, improper imaging techniques, lesion characteristics, and work environment challenges. The article also provides practical recommendations and potential strategies to reduce these errors focusing on actions applicable to individual radiologists. Understanding the common causes of diagnostic errors in breast imaging and implementing targeted mitigating strategies can help radiologists improve diagnostic precision, reduce malpractice risk, and enhance patient care.

Early Detection of Breast Cancer in MRI Using AI

RATIONALE AND OBJECTIVES

To develop and evaluate an AI algorithm that detects breast cancer in MRI scans up to one year before radiologists typically identify it, potentially enhancing early detection in high-risk women.

MATERIALS AND METHODS

A convolutional neural network (CNN) AI model, pre-trained on breast MRI data, was fine-tuned using a retrospective dataset of 3029 MRI scans from 910 patients. These contained 115 cancers that were diagnosed within one year of a negative MRI. The model aimed to identify these cancers, with the goal of predicting cancer development up to one year in advance. The network was fine-tuned and tested with 10-fold cross-validation. Mean age of patients was 52 years (range, 18-88 years), with average follow-up of 4.3 years (range 1-12 years).

RESULTS

The AI detected cancers one year earlier with an area under the ROC curve of 0.72 (0.67-0.76). Retrospective analysis by a radiologist of the top 10% highest risk MRIs as ranked by the AI could have increased early detection by up to 30%. (35/115, CI:22.2-39.7%, 30% sensitivity). A radiologist identified a visual correlate to biopsy-proven cancers in 83 of prior-year MRIs (83/115, CI: 62.1-79.4%). The AI algorithm identified the anatomic region where cancer would be detected in 66 cases (66/115, CI:47.8-66.5%); with both agreeing in 54 cases (54/115, CI:%37.5-56.4%).

CONCLUSION

This novel AI-aided re-evaluation of "benign" breasts shows promise for improving early breast cancer detection with MRI. As datasets grow and image quality improves, this approach is expected to become even more impactful.

Tumor Growth Rate of Luminal and Nonluminal Invasive Breast Cancer Calculated on MRI Imaging

PURPOSE

Calculation of the size growth of different types of breast carcinoma based on follow-up data in breast MRI.

PATIENTS AND METHODS

Patients were included if they had been diagnosed with an invasive breast carcinoma in the current MRI (aMRI), and had also undergone a breast MRI (pMRI) with unsuspicious findings (MR BIRADS 1 or 2) within 5 years prior to diagnosis. If retrospective analysis of pMRI revealed signs of the current carcinoma, a quantitative one-dimensional-analysis of size progression of the carcinoma over time was performed, and growth rates for different tumor types were calculated.

RESULTS

About 204 patients with 208 invasive breast carcinomas (74 luminal A, 105 luminal B, nonluminal 29) were included. In 129 carcinomas, there were signs of the current tumor in the pMRI. Based on the interval between pMRI and aMRI (average 21 months), the average tumor doubling time was 1126 days (3.1 years), 624 days (1.7 years), and 254 days (0.7 years) of luminal A, luminal B, and nonluminal. The average tumor size was 4.3 mm in the pMRI, and 9.5 mm in aMRI. In 79 cases, the pMRI showed no signs of the actual carcinoma. In this group, the average current tumor size was 8.5 mm.

CONCLUSION

The study provides specific information on the growth rate of luminal and nonluminal breast cancer. According to this, early detection intervals for nonhigh-risk women using MRI of 2 to 3 years, and for high-risk (HR) women of 1 year appear reasonable. Data also provide a well-founded basis for medico-legal judgements.

Breast imaging with an ultra-low field MRI scanner: a pilot study

Breast cancer screening is necessary to reduce mortality due to undetected breast cancer. Current methods have limitations, and as a result many women forego regular screening. Magnetic resonance imaging (MRI) can overcome most of these limitations, but access to conventional MRI is not widely available for routine annual screening. Here, we used an MRI scanner operating at ultra-low field (ULF) to image the left breasts of 11 women (mean age, 35 years ±13 years) in the prone position. Three breast radiologists reviewed the imaging and were able to discern the breast outline and distinguish fibroglandular tissue (FGT) from intramammary adipose tissue. Additionally, the expert readers agreed on their assessment of the breast tissue pattern including fatty, scattered FGT, heterogeneous FGT, and extreme FGT. This preliminary work demonstrates that ULF breast MRI is feasible and may be a potential option for comfortable, widely deployable, and low-cost breast cancer diagnosis and screening.

[WITHDRAWN] Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection

This paper has been withdrawn by Lukas Hirsch. Major revisions and rewriting in progress.

High-Temporal/High-Spatial Resolution Breast Magnetic Resonance Imaging Improves Diagnostic Accuracy Compared With Standard Breast Magnetic Resonance Imaging in Patients With High Background Parenchymal Enhancement

PURPOSE

To compare breast magnetic resonance imaging (MRI) diagnostic performance using a standard high-spatial resolution protocol versus a simultaneous high-temporal/high-spatial resolution (HTHS) protocol in women with high levels of background parenchymal enhancement (BPE).

MATERIALS AND METHODS

We conducted a retrospective study of contrast-enhanced breast MRIs performed at our institution before and after the introduction of the HTHS protocol. We compared diagnostic performance of the HTHS and standard protocol by comparing cancer detection rate (CDR) and positive predictive value of biopsy (PPV3) among women with high BPE (ie, marked or moderate).

RESULTS

Among women with high BPE, the HTHS protocol demonstrated increased CDR (23.6 per 1,000 patients v 7.9 per 1,000 patients; P = 0. 013) and increased PPV3 (16.0% v 6.3%; P = .021) compared with the standard protocol. This corresponded to a 9.8% (95% CI, 1.29 to 18.3) decrease in the proportion of unnecessary biopsies among high-BPE patients and an additional cancer yield of 15.7 per 1,000 patients (95% CI, 1.3 to 18.3).

CONCLUSION

Among women with high BPE, HTHS MRI improved diagnostic performance, leading to an additional cancer yield of 15.7 cancers per 1,000 women and concomitantly decreasing unnecessary biopsies by 9.8%. A multisite prospective trial is warranted to confirm these findings and to pave the way for more widespread clinical implementation.

Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.

Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study

BACKGROUND

International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the applicability of deep learning-based anomaly detection to identify anomalous changes in negative breast CE-MRI screens associated with future lesion emergence.

METHODS

In this prospective study, we trained a generative adversarial network on dynamic CE-MRI of 33 high-risk women who participated in a screening program but did not develop BC. We defined an anomaly score as the deviation of an observed CE-MRI scan from the model of normal breast tissue variability. We evaluated the anomaly score's association with future lesion emergence on the level of local image patches (104,531 normal patches, 455 patches of future lesion location) and entire CE-MRI exams (21 normal, 20 with future lesion). Associations were analyzed by receiver operating characteristic (ROC) curves on the patch level and logistic regression on the examination level.

RESULTS

The local anomaly score on image patches was a good predictor for future lesion emergence (area under the ROC curve 0.804). An exam-level summary score was significantly associated with the emergence of lesions at any location at a later time point (p = 0.045).

CONCLUSIONS

Breast cancer lesions are associated with anomalous appearance changes in breast CE-MRI occurring before the lesion emerges in high-risk women. These early image signatures are detectable and may be a basis for adjusting individual BC risk and personalized screening.

RELEVANCE STATEMENT

Anomalies in screening MRI preceding lesion emergence in women at high-risk of breast cancer may inform individualized screening and intervention strategies.

KEY POINTS

• Breast lesions are associated with preceding anomalies in CE-MRI of high-risk women. • Deep learning-based anomaly detection can help to adjust risk assessment for future lesions. • An appearance anomaly score may be used for adjusting screening interval times.

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.

Evaluation of the Pink Luminous Breast LED-Based Technology Device as a Screening Tool for the Early Detection of Breast Abnormalities

Breast cancer is the leading cause of sex-specific female cancer deaths in the United States. Detection at earlier stages contributes to decreasing the mortality rate. The mammogram is the "Gold Standard" for breast cancer screening with an estimated sensitivity of 86.9% and a specificity of 88.9%. However, these values are negatively affected by the breast density considered a risk factor for developing breast cancer. Herein, we validate the novel LED-based medical device Pink Luminous Breast (PLB) by comparison with the mammogram using a double blinded approach. The PLB works by emitting a LED red light with a harmless spectrum of 640-800 nanometers. This allows the observation of abnormalities represented by dark or shadow areas. In this study, we evaluated the sensitivity and specificity of the PLB device as a screening tool for the early detection of breast abnormalities. Our results show that the PLB device has a high sensitivity (89.6%) and specificity (96.4%) for detecting breast abnormalities comparable to the adjusted mammogram values: 86.3 and 68.9%, respectively. The percentage of presence of breast density was 78.2% using PLB vs. 72.9% with the mammogram. Even with higher findings of breast density, the PLB is still capable of detecting 9.4% of calcifications compared to 6.2% in mammogram results and the reported findings for cysts, masses, or tumor-like abnormalities was higher using the PLB (6.5%) than the mammogram (5.6%). A 100% of the participants felt comfortable using the device without feeling pain or discomfort during the examination with 100% acceptability. The PLB positive validation shows its potential for routine breast screening at non-clinical settings. The PLB provides a rapid, non-invasive, portable, and easy-to-use tool for breast screening that can complement the home-based breast self-examination technique or the clinical breast examination. In addition, the PLB can be conveniently used for screening breasts with surgical implants. PLB provides an accessible and painless breast cancer screening tool. The PLB use is not intended to replace the mammogram for breast screening but rather to use it as an adjunct or complemental tool as part of more efficient earlier detection strategies contributing to decrease mortality rates.

Missed Breast Cancers on MRI in High-Risk Patients: A Retrospective Case–Control Study

Purpose: To determine if MRI features and molecular subtype influence the detectability of breast cancers on MRI in high-risk patients. Methods and Materials: Breast cancers in a high-risk population of 104 patients were diagnosed following MRI describing a BI-RADS 4–5 lesion. MRI characteristics at the time of diagnosis were compared with previous MRI, where a BI-RADS 1–2–3 lesion was described. Results: There were 77 false-negative MRIs. A total of 51 cancers were overlooked and 26 were misinterpreted. There was no association found between MRI characteristics, the receptor type and the frequency of missed cancers. The main factors for misinterpreted lesions were multiple breast lesions, prior biopsy/surgery and long-term stability. Lesions were mostly overlooked because of their small size and high background parenchymal enhancement. Among missed lesions, 50% of those with plateau kinetics on initial MRI changed for washout kinetics, and 65% of initially progressively enhancing lesions then showed plateau or washout kinetics. There were more basal-like tumours in BRCA1 carriers (50%) than in non-carriers (13%), p = 0.0001, OR = 6.714, 95% CI = [2.058–21.910]. The proportion of missed cancers was lower in BRCA carriers (59%) versus non-carriers (79%), p < 0.05, OR = 2.621, 95% CI = [1.02–6.74]. Conclusions: MRI characteristics or molecular subtype do not influence breast cancer detectability. Lesions in a post-surgical breast should be assessed with caution. Long-term stability does not rule out malignancy and multimodality evaluation is of added value. Lowering the biopsy threshold for lesions with an interval change in kinetics for a type 2 or 3 curve should be considered. There was a higher rate of interval cancers in BRCA 1 patients attributed to lesions more aggressive in nature.

Computing schizophrenia: ethical challenges for machine learning in psychiatry

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.

What's Hot in Breast MRI

Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.

Breast Cancer Screening with Abbreviated Breast MRI: 3-year Outcome Analysis

Background Data are limited regarding the performance of abbreviated screening breast MRI during consecutive years and the characteristics of breast cancers missed and detected with it. Purpose To assess the longitudinal diagnostic performance of abbreviated screening MRI and to determine whether the screening outcomes of abbreviated MRI differed between yearly time periods for 3 consecutive years. Materials and Methods This retrospective study included 1975 consecutive women who underwent abbreviated screening MRI between September 2015 and August 2018. Breast Imaging Reporting and Data System (BI-RADS) categories 3-5 defined positive results, and BI-RADS categories 1-2 defined negative results. Cancer detection rate (CDR), sensitivity, specificity, positive predictive value (PPV), abnormal interpretation rate (AIR), and interval cancer rate were assessed annually. Yearly performance measures were compared with the Fisher exact test by using the permutation method. Clinical-pathologic and imaging characteristics of the missed and detected cancers were compared by using the Fisher exact test and the Wilcoxon rank sum test. Results A total of 1975 women (median age, 49 years; interquartile range, 44-56 years) underwent 3037 abbreviated MRI examinations over 3 years. CDR (year 1 to year 3, 6.9-10.7 per 1000 examinations), positive predictive value for recall (9.7% [six of 62] to 15.6% [12 of 77]), positive predictive value for biopsy (31.6% [six of 19] to 63.2% [12 of 19]), sensitivity (75.0% [six of eight] to 80.0% [12 of 15]), and specificity (93.5% [807 of 863] to 94.1% [1041 of 1106]) were highest in year 3, and AIR (7.1% [62 of 871] to 6.9% [77 of 1121]) was lowest in year 3. However, all outcome measures did not differ statistically between years 1, 2, and 3 (all P > .05). The interval cancer rate was 0.66 per 1000 examinations (two of 3037). Thirty-eight breast cancers were identified in 36 women; 29 were detected with abbreviated MRI, but nine were missed. Of these, seven were detected with other imaging modalities after negative results at the last screening MRI examination, and two were interval cancers. All missed cancers were node-negative early-stage invasive cancers and were smaller (median size, 0.8 cm vs 1.2 cm; P = .01) than detected cancers. Conclusion Screening outcome measures of abbreviated MRI were sustained without significant differences between 3 consecutive years. All cancers missed at abbreviated MRI were node-negative invasive cancers and tended to be smaller than detected cancers. © RSNA, 2021 See also the editorial by Lee in this issue. Online supplemental material is available for this article.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Breast Cancer Cell Detection and Characterization from Breast Milk-Derived Cells

Radiologic techniques remain the main method for early detection for breast cancer and are critical to achieve a favorable outcome from cancer. However, more sensitive detection methods to complement radiologic techniques are needed to enhance early detection and treatment strategies. Using our recently established culturing method that allows propagation of normal and cancerous breast epithelial cells of luminal origin, flow cytometry characterization, and genomic sequencing, we show that cancer cells can be detected in breast milk. Cells derived from milk from the breast with cancer were enriched for CD49f⁺/EpCAM^-, CD44⁺/CD24^-, and CD271⁺ cancer stem-like cells (CSC). These CSCs carried mutations within the cytoplasmic retention domain of HDAC6, stop/gain insertion in MORF4L1, and deletion mutations within SWI/SNF complex component SMARCC2. CSCs were sensitive to HDAC6 inhibitors, BET bromodomain inhibitors, and EZH2 inhibitors, as mutations in SWI/SNF complex components are known to increase sensitivity to these drugs. Among cells derived from breast milk of additional ten women not known to have breast cancer, two of them contained cells that were enriched for the CSC phenotype and carried mutations in NF1 or KMT2D, which are frequently mutated in breast cancer. Breast milk-derived cells with NF1 mutations also carried copy-number variations in CDKN2C, PTEN, and REL genes. The approach described here may enable rapid cancer cell characterization including driver mutation detection and therapeutic screening for pregnancy/postpartum breast cancers. Furthermore, this method can be developed as a surveillance or early detection tool for women at high risk for developing breast cancer. SIGNIFICANCE: These findings describe how a simple method for characterization of cancer cells in pregnancy and postpartum breast cancer can be exploited as a surveillance tool for women at risk of developing breast cancer.

The supplemental value of mammographic screening over breast MRI alone in BRCA2 mutation carriers

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 < 0.001).

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.

Breast Heterogeneity: Obstacles to Developing Universal Biomarkers of Breast Cancer Initiation and Progression

BACKGROUND

Predicting outcomes and response to therapy through biomarkers is a major challenge in cancer research. In previous studies, we suggested that inappropriate "normal" tissue samples used for comparison with tumors, inter-individual heterogeneity in gene expression, and genetic ancestry all influence biomarker expression in tumors. The aim of this study was to investigate these factors in breast cancer using breast tissues from healthy women and normal tissue adjacent to tumor (NAT) with matrix metalloproteinase 7 (MMP7) as a candidate biomarker.

STUDY DESIGN

RNA sequencing was performed on primary luminal progenitor cells from healthy breast, NATs, and tumors to identify transcriptomes enriched in NATs and breast cancer. Expression of select genes was validated via quantitative reverse transcription polymerase chain reaction of RNA and via immunohistochemistry of a tissue microarray of normal, NAT, and tumor samples of different genetic ancestry.

RESULTS

Twenty-six genes were significantly overexpressed in NATs and tumors compared with healthy controls at messenger RNA level and formed a para-inflammatory network. MMP7 had the greatest expression in tumor cells, with upregulation confirmed by quantitative reverse transcription polymerase chain reaction. Tumor-enriched but not NAT-enriched expression of MMP7 compared with healthy controls was reproduced at protein levels. When stratified by genetic ancestry, tumor-specific increase of MMP7 reached statistical significance in women of European ancestry.

CONCLUSIONS

Transcriptome differences across healthy, NAT, and tumor tissue in breast cancer demonstrate an active para-inflammatory network in NATs and indicate unsuitability of NATs as "normal controls" in biomarker discovery. The discordance between transcriptomic and proteomic MMP7 expression in NATs and the influence of genetic ancestry on its protein expression highlight the complexity in developing universally acceptable biomarkers of breast cancer and the importance of genetic ancestry in biomarker development.

Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features

PURPOSE

In this study, we propose a new computer-aided diagnosis (CADx) to distinguish between malign and benign mass and non-mass lesions in breast DCE-MRI. For this purpose, we introduce new frequency textural features.

METHODS

In this paper, we propose novel normalized frequency-based features. These are obtained by applying the dual-tree complex wavelet transform to MRI slices containing a lesion for specific decomposition levels. The low-pass and band-pass frequency coefficients of the dual-tree complex wavelet transform represent the general shape and texture features, respectively, of the lesion. The extraction of these features is computationally efficient. We employ a support vector machine to classify the lesions, and investigate modified cost functions and under- and oversampling strategies to handle the class imbalance.

RESULTS

The proposed method has been tested on a dataset of 80 patients containing 103 lesions. An area under the curve of 0.98 for the mass and 0.94 for the non-mass lesions is obtained. Similarly, accuracies of 96.9% and 89.8%, sensitivities of 93.8% and 84.6% and specificities of 98% and 92.3% are obtained for the mass and non-mass lesions, respectively.

CONCLUSION

Normalized frequency-based features can characterize benign and malignant lesions efficiently in both mass- and non-mass-like lesions. Additionally, the combination of normalized frequency-based features and three-dimensional shape descriptors improves the CADx performance.

Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening

OBJECTIVES

Breast cancer screening using magnetic resonance imaging (MRI) has limited accessibility due to high costs of breast MRI. Ultrafast dynamic contrast-enhanced breast MRI can be acquired within 2 minutes. We aimed to assess whether screening performance of breast radiologist using an ultrafast breast MRI-only screening protocol is as good as performance using a full multiparametric diagnostic MRI protocol (FDP).

MATERIALS AND METHODS

The institutional review board approved this study, and waived the need for informed consent. Between January 2012 and June 2014, 1791 consecutive breast cancer screening examinations from 954 women with a lifetime risk of more than 20% were prospectively collected. All women were scanned using a 3 T protocol interleaving ultrafast breast MRI acquisitions in a full multiparametric diagnostic MRI protocol consisting of standard dynamic contrast-enhanced sequences, diffusion-weighted imaging, and T2-weighted imaging. Subsequently, a case set was created including all biopsied screen-detected lesions in this period (31 malignant and 54 benign) and 116 randomly selected normal cases with more than 2 years of follow-up. Prior examinations were included when available. Seven dedicated breast radiologists read all 201 examinations and 153 available priors once using the FDP and once using ultrafast breast MRI only in 2 counterbalanced and crossed-over reading sessions.

RESULTS

For reading the FDP versus ultrafast breast MRI alone, sensitivity was 0.86 (95% confidence interval [CI], 0.81-0.90) versus 0.84 (95% CI, 0.78-0.88) (P = 0.50), specificity was 0.76 (95% CI, 0.74-0.79) versus 0.82 (95% CI, 0.79-0.84) (P = 0.002), positive predictive value was 0.40 (95% CI, 0.36-0.45) versus 0.45 (95% CI, 0.41-0.50) (P = 0.14), and area under the receiver operating characteristics curve was 0.89 (95% CI, 0.82-0.96) versus 0.89 (95% CI, 0.82-0.96) (P = 0.83). Ultrafast breast MRI reading was 22.8% faster than reading FDP (P < 0.001). Interreader agreement is significantly better for ultrafast breast MRI (κ = 0.730; 95% CI, 0.699-0.761) than for the FDP (κ = 0.665; 95% CI, 0.633-0.696).

CONCLUSIONS

Breast MRI screening using only an ultrafast breast MRI protocol is noninferior to screening with an FDP and may result in significantly higher screening specificity and shorter reading time.

Redefining the sensitivity of screening mammography: A review

From its inception, screening mammography has enjoyed a perceived level of sensitivity that is inconsistent with available evidence. The original data that imparted erroneous beliefs about sensitivity were based on a variety of misleading definitions and approaches, such as the inclusion of palpable tumors, using the inverse of interval cancer rates (often tied to an arbitrary 12 month interval), and quoting prevalence screen sensitivity wherein tumors are larger than those found on incidence screens. This review addresses the background for the overestimation of mammographic sensitivity, and how a major adjustment in our thinking is overdue now that multi-modality imaging allows us to determine real time mammographic sensitivity. Although a single value for mammographic sensitivity is disingenuous, given the wide range based on background density, it is important to realize that a sensitivity gap between belief and reality still exists in the early detection of breast cancer using mammography alone, in spite of technologic advances. Failure to recognize this gap diminishes the acceptance of adjunct methods of breast imaging that greatly complement detection rates.

Contrast-enhanced MRI for breast cancer screening

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.

The added value of mammography in different age-groups of women with and without BRCA mutation screened with breast MRI

Background

Breast magnetic resonance imaging (MRI) is the most sensitive imaging method for breast cancer detection and is therefore offered as a screening technique to women at increased risk of developing breast cancer. However, mammography is currently added from the age of 30 without proven benefits. The purpose of this study is to investigate the added cancer detection of mammography when breast MRI is available, focusing on the value in women with and without BRCA mutation, and in the age groups above and below 50 years.

Methods

This retrospective single-center study evaluated 6553 screening rounds in 2026 women at increased risk of breast cancer (1 January 2003 to 1 January 2014). Risk category (BRCA mutation versus others at increased risk of breast cancer), age at examination, recall, biopsy, and histopathological diagnosis were recorded. Cancer yield, false positive recall rate (FPR), and false positive biopsy rate (FPB) were calculated using generalized estimating equations for separate age categories (< 40, 40–50, 50–60, ≥ 60 years). Numbers of screens needed to detect an additional breast cancer with mammography (NSN) were calculated for the subgroups.

Results

Of a total of 125 screen-detected breast cancers, 112 were detected by MRI and 66 by mammography: 13 cancers were solely detected by mammography, including 8 cases of ductal carcinoma in situ. In BRCA mutation carriers, 3 of 61 cancers were detected only on mammography, while in other women 10 of 64 cases were detected with mammography alone. While 77% of mammography-detected-only cancers were detected in women ≥ 50 years of age, mammography also added more to the FPR in these women. Below 50 years the number of mammographic examinations needed to find an MRI-occult cancer was 1427.

Conclusions

Mammography is of limited added value in terms of cancer detection when breast MRI is available for women of all ages who are at increased risk. While the benefit appears slightly larger in women over 50 years of age without BRCA mutation, there is also a substantial increase in false positive findings in these women.