Abbreviated protocol breast MRI: The past, present, and future

MRI for hepatocellular carcinoma and the role of abbreviated MRI for surveillance of hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) constitutes the majority of liver cancers and significantly impacts global cancer mortality. While ultrasound (US) with or without alpha-fetoprotein is the mainstay for HCC surveillance, its limitations highlight the necessity for more effective surveillance tools. Therefore, this review explores evolving imaging modalities and abbreviated magnetic resonance imaging (MRI) (AMRI) protocols as promising alternatives, addressing challenges in HCC surveillance.

AREAS COVERED

This comprehensive review delves into the evaluation and challenges of HCC surveillance tools, focusing on non-contrast abbreviated MRI (NC-AMRI) and contrast-enhanced abbreviated MRI protocols. It covers the implementation of AMRI for HCC surveillance, patient preferences, adherence, and strategies for optimizing cost-effectiveness. Additionally, the article provides insights into prospects for HCC surveillance by summarizing meta-analyses, prospective studies, and ongoing clinical trials evaluating AMRI protocols.

EXPERT OPINION

The opinions underscore the transformative impact of AMRI on HCC surveillance, especially in overcoming US limitations. Promising results from NC-AMRI protocols indicate its potential for high-risk patient surveillance, though prospective studies in true surveillance settings are essential for validation. Future research should prioritize risk-stratified AMRI protocols and address cost-effectiveness for broader clinical implementation, alongside comparative analyses with US for optimal surveillance strategies.

Auditing Abbreviated Breast MR Imaging: Clinical Considerations and Implications

Abbreviated breast MR (AB-MR) imaging is a relatively new breast imaging tool, which maintains diagnostic accuracy while reducing image times compared with full-protocol breast MR (FP-MR) imaging. Breast imaging audits involve calculating individual and organizational metrics, which can be compared with established benchmarks, providing a standard against which performance can be measured. Unlike FP-MR imaging, there are no established benchmarks for AB-MR imaging but studies demonstrate comparable performance for cancer detection rate, positive predictive value 3, sensitivity, and specificity with T2. We review the basics of performing an audit, including strategies to implement if benchmarks are not being met.

Optimized, Person-Centered Workflow Design for a High-Throughput Breast MRI Screening Facility-A Simulation Study

OBJECTIVES

This project aims to model an optimal scanning environment for breast magnetic resonance imaging (MRI) screening based on real-life data to identify to what extent the logistics of breast MRI can be optimized.

MATERIALS AND METHODS

A novel concept for a breast MRI screening facility was developed considering layout of the building, workflow steps, used resources, and MRI protocols. The envisioned screening facility is person centered and aims for an efficient workflow-oriented design. Real-life data, collected from existing breast MRI screening workflows, during 62 scans in 3 different hospitals, were imported into a 3D simulation software for designing and testing new concepts. The model provided several realistic, virtual, logistical pathways for MRI screening and their outcome measures: throughput, waiting times, and other relevant variables.

RESULTS

The total average appointment time in the baseline scenario was 25:54 minutes, with 19:06 minutes of MRI room occupation. Simulated improvements consisted of optimizing processes and resources, facility layout, and scanning protocol. In the simulation, time spent in the MRI room was reduced by introducing an optimized facility layout, dockable tables, and adoption of an abbreviated MRI scanning protocol. The total average appointment time was reduced to 19:36 minutes, and in this scenario, the MRI room was occupied for 06:21 minutes. In the most promising scenario, screening of about 68 people per day (10 hours) on a single MRI scanner could be feasible, compared with 36 people per day in the baseline scenario.

CONCLUSIONS

This study suggests that by optimizing workflow MRI for breast screening total appointment duration and MRI occupation can be reduced. A throughput of up to 6 people per hour may be achieved, compared with 3 people per hour in the current setup.

Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) has been regarded as one of the standard treatments for patients with locally advanced breast cancer. No previous study has investigated the feasibility of using a contrast-enhanced spectral mammography (CESM)-based radiomics nomogram to predict pathological complete response (pCR) after NAC.

OBJECTIVE

To develop and validate a CESM-based radiomics nomogram to predict pCR after NAC in breast cancer.

METHODS

A total of 118 patients were enrolled, which are divided into a training dataset including 82 patients (with 21 pCR and 61 non-pCR) and a testing dataset of 36 patients (with 9 pCR and 27 non-pCR). The tumor regions of interest (ROIs) were manually segmented by two radiologists on the low-energy and recombined images and radiomics features were extracted. Intraclass correlation coefficients (ICCs) were used to assess the intra- and inter-observer agreements of ROI features extraction. In the training set, the variance threshold, SelectKBest method, and least absolute shrinkage and selection operator regression were used to select the optimal radiomics features. Radiomics signature was calculated through a linear combination of selected features. A radiomics nomogram containing radiomics signature score (Rad-score) and clinical risk factors was developed. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate prediction performance of the radiomics nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the radiomics nomogram.

RESULTS

The intra- and inter- observer ICCs were 0.769-0.815 and 0.786-0.853, respectively. Thirteen radiomics features were selected to calculate Rad-score. The radiomics nomogram containing Rad-score and clinical risk factor showed an encouraging calibration and discrimination performance with area under the ROC curves of 0.906 (95% confidence interval (CI): 0.840-0.966) in the training dataset and 0.790 (95% CI: 0.554-0.952) in the test dataset.

CONCLUSIONS

The CESM-based radiomics nomogram had good prediction performance for pCR after NAC in breast cancer; therefore, it has a good clinical application prospect.

Clinical utility of abbreviated breast MRI based on diffusion tensor imaging in patients underwent breast conservative therapy

PURPOSE

To evaluate the added value of the diffusion tensor imaging (DTI) parameters to abbreviated breast MRI protocol in differentiating recurrent breast cancer from post-operative changes in cases of breast conservative surgery (BCS).

METHODS

This prospective study was approved by our institutional review board. Written informed consent was obtained in all patients. 47 female patients (mean age, 49 years; range, 32-66 years) that previously underwent breast conservative surgery with a palpable mass were included in this study (62 breast lesions). Two abbreviated MRI protocols were compared using 1.5 Tesla MRI, AB-MRI 1 (axial T1, T2, pre-contrast T1, 1st post-contrast and subtracted images) and AB-MRI 2 (same sequences plus adding DTI). In both protocols, the wash-in rate was calculated. Histopathology was used as the standard of reference. Appropriate statistical tests were used to assess sensitivity, specificity, and diagnostic accuracy for each protocol.

RESULTS

The mean total acquisition time was of 6 min for AB-MRI 1 and 10 min for AB-MRI 2 protocols while the mean interpretation time was of 57.5 and 75 s, respectively. Among analyzed DTI parameters, MD (mean diffusivity) showed the highest sensitivity (96.43%) and specificity (91.18%) (P value =  < 0.001). FA (fractional anisotropy), AD (axial diffusivity) and RD (radial diffusivity) showed sensitivity = (78.57%, 82.14% and 85.71%), specificity = (88.24, 85.29% and 79.41%), respectively, P value (< 0.001).

CONCLUSION

DTI may be included in abbreviated MRI protocols without a significant increase in acquisition time and with the advantage of increasing specificity and clinical utility in the characterization of post-conservative breast lesions.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast

PURPOSE

A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition.

THEORY

Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters.

METHODS

The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points.

RESULTS

The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve .

CONCLUSION

It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis

PURPOSE OF REVIEW

This article aims to provide an updated overview of the indications for diagnostic breast magnetic resonance imaging (MRI), discusses the available and novel imaging exams proposed for breast cancer detection, and discusses considerations when performing breast MRI in the clinical setting.

RECENT FINDINGS

Breast MRI is superior in identifying lesions in women with a very high risk of breast cancer or average risk with dense breasts. Moreover, the application of breast MRI has benefits in numerous other clinical cases as well; e.g., the assessment of the extent of disease, evaluation of response to neoadjuvant therapy (NAT), evaluation of lymph nodes and primary occult tumor, evaluation of lesions suspicious of Paget's disease, and suspicious discharge and breast implants. Breast cancer is the most frequently detected tumor among women around the globe and is often diagnosed as a result of abnormal findings on mammography. Although effective multimodal therapies significantly decline mortality rates, breast cancer remains one of the leading causes of cancer death. A proactive approach to identifying suspicious breast lesions at early stages can enhance the efficacy of anti-cancer treatments, improve patient recovery, and significantly improve long-term survival. However, the currently applied mammography to detect breast cancer has its limitations. High false-positive and false-negative rates are observed in women with dense breasts. Since approximately half of the screening population comprises women with dense breasts, mammography is often incorrectly used. The application of breast MRI should significantly impact the correct cases of breast abnormality detection in women. Radiomics provides valuable data obtained from breast MRI, further improving breast cancer diagnosis. Introducing these constantly evolving algorithms in clinical practice will lead to the right breast detection tool, optimized surveillance program, and individualized breast cancer treatment.

A Retrospective Analysis of Sternal Lesions Detected on Breast MRI in Patients Without History of Cancer

OBJECTIVE

To determine the imaging characteristics and stability over time of sternal lesions identified on breast MRI in patients without history of cancer.

METHODS

An IRB-approved retrospective analysis of all breast MRIs performed at our institution from September 1, 2017 to December 1, 2021 that included one of several key words related to the sternum. Studies with history of non-dermatologic malignancy including breast cancer, absence of a true sternal lesion, or presence of symptoms during the examination were excluded. Imaging was reviewed for size, distribution, signal characteristics, and presence of contrast enhancement, perilesional edema, periosteal edema, or intralesional fat. Available comparison imaging, clinical history, and follow-up recommendations were reviewed. Descriptive statistics were used to summarize lesion data.

RESULTS

Of 60 lesions included from 60 patients, 40 lesions with more than two years of comparison imaging were either stable or decreased in size and none demonstrated change in signal characteristics. The majority of these presumed benign lesions demonstrated hypointense signal on T1-weighted sequences (21/40, 52.5%), hyperintense signal on fluid-sensitive sequences (33/40, 82.5%), contrast enhancement (32/40, 80.0%), and absence of clear intralesional fat (29/40, 72.5%). One patient who did not have comparison imaging was diagnosed with malignancy (multiple myeloma) eight months following their MRI. This lesion demonstrated uniquely diffuse and heterogeneous enhancement but did not undergo biopsy.

CONCLUSION

Sternal lesions in women without history of non-dermatologic malignancy have a very low likelihood of malignancy. Common imaging characteristics of the presumed benign lesions can inform imaging recommendations when incidental sternal lesions are discovered.

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head

INTRODUCTION AND BACKGROUND

Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality.

METHODS

This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets.

RESULTS

A detailed evaluation of the proposed model's accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset.

CONCLUSIONS

The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.

A Concise Review on the Utilization of Abbreviated Protocol Breast MRI over Full Diagnostic Protocol in Breast Cancer Detection

Breast MRI possesses high sensitivity for detecting breast cancer among the current clinical modalities and is an indispensable imaging practice. Breast MRI comprises diffusion-weighted imaging, ultrafast, and T2 weighted and T1 weighted CE (contrast-enhanced) imaging that may be utilized for improving the characterization of the lesions. This multimodal evaluation of breast lesions enables outstanding discrimination between the malignant and benign and malignant lesions. The expanding indications of breast MRI confirm the far superiority of MRI in preoperative staging, especially in the estimation of tumour size and identifying tumour foci in the contralateral and ipsilateral breast. Recent studies depicted that experts can meritoriously utilize this tool for improving breast cancer surgery despite their existence of no significant long term outcomes. For managing the, directly and indirectly, associated screening cost, abbreviated protocols are found to be more beneficial. Further, in some of the patients who were treated with neoadjuvant chemotherapy, breast MRI is utilized for documenting response. It is therefore essential to realise that oncological screening must be easily available, cost-effective, and time-consuming. Earlier detection of this short sequence protocol leads to prior and early breast cancer disease in high risky female populations like women with dense breasts, prehistoric evidence, etc. This proper utilization of AP reduces unnecessary mastectomies. Hence, this review focused on the explorative information for strongly suggesting the benefits of AP breast MRI compared to full diagnostic protocol MRI.

US Cancer Screening Recommendations: Developments and the Impact of COVID-19

The USPSTF and ACS recommend screening for breast, cervical, colorectal, and lung cancers. Rates of cancer screening, diagnosis, and treatment decreased significantly in the US and other developed nations during the height of the COVID-19 pandemic and lockdown (April 2020) and have since recovered, although not to baseline levels in many cases. For breast cancer, the USPSTF recommends biennial screening with mammography for women aged 50−74, while the ACS recommends annual screening for women aged 45−54, who may transition to biennial after 55. Minority and rural populations have lower rates of screening and lower utilization of DBT, which offers superior sensitivity and specificity. Among 20 US health networks in April 2020, mammography rates were down 89.2% and new breast cancer diagnoses down by 50.5%. For cervical cancer, the USPSTF recommends cervical cytology every three years for women 21−65, or cytology+hrHPV co-testing every five years for women aged 30−65. Cervical cancer screening rates declined by 87% in April 2020 and recovered to a 40% decline by June 2020, with American Indians and Asians most severely affected. For colorectal cancer (CRC), the USPSTF and ACS recommend screening for ages 45−75, recently lowered from a starting age of 50. Most commonly-used modalities include annual FIT testing, FIT+DNA testing every three years, and colonoscopy every ten years, with shorter repeat if polyps are found. In the US, CRC screenings were down by 79−84.5% in April 2020 across several retrospective studies. Patient encounters for CRC were down by 39.9%, and a UK-based model predicted that 5-year-survival would decrease by 6.4%. The USPSTF recommends screening low dose CT scans (LDCTs) for ages 50−80 with a >20 pack-year smoking history who have smoked within the past 15 years. In April 2020, screening LDCTs fell by 72−78% at one US institution and lung cancer diagnoses were down 39.1%.

Artificial intelligence for breast cancer analysis: Trends & directions

Breast cancer is one of the leading causes of death among women. Early detection of breast cancer can significantly improve the lives of millions of women across the globe. Given importance of finding solution/framework for early detection and diagnosis, recently many AI researchers are focusing to automate this task. The other reasons for surge in research activities in this direction are advent of robust AI algorithms (deep learning), availability of hardware that can run/train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths and limitations. It also enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade to detect breast cancer using various imaging modalities. Primarily, in this article we have focused on reviewing frameworks that have reported results using mammograms as it is the most widely used breast imaging modality that serves as the first test that medical practitioners usually prescribe for the detection of breast cancer. Another reason for focusing on mammogram imaging modalities is the availability of its labelled datasets. Datasets availability is one of the most important aspects for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.

Cost-effectiveness of abbreviated-protocol MRI screening for women with mammographically dense breasts in a national breast cancer screening program

Introduction

Magnetic resonance imaging (MRI) has shown the potential to improve the screening effectiveness among women with dense breasts. The introduction of fast abbreviated protocols (AP) makes MRI more feasible to be used in a general population. We aimed to investigate the cost-effectiveness of AP-MRI in women with dense breasts (heterogeneously/extremely dense) in a population-based screening program.

Methods

A previously validated model (SiMRiSc) was applied, with parameters updated for women with dense breasts. Breast density was assumed to decrease with increased age. The base scenarios included six biennial AP-MRI strategies, with biennial mammography from age 50–74 as reference. Fourteen alternative scenarios were performed by varying screening interval (triennial and quadrennial) and by applying a combined strategy of mammography and AP-MRI. A 3% discount rate for both costs and life years gained (LYG) was applied. Model robustness was evaluated using univariate and probabilistic sensitivity analyses.

Results

The six biennial AP-MRI strategies ranged from 132 to 562 LYG per 10,000 women, where more frequent application of AP-MRI was related to higher LYG. The optimal strategy was biennial AP-MRI screening from age 50–65 for only women with extremely dense breasts, producing an incremental cost-effectiveness ratio of € 18,201/LYG. At a threshold of € 20,000/LYG, the probability that the optimal strategy was cost-effective was 79%.

Conclusion

Population-based biennial breast cancer screening with AP-MRI from age 50–65 for women with extremely dense breasts might be a cost-effective alternative to mammography, but is not an option for women with heterogeneously dense breasts.

•

AP-MRI can be cost-effective for screening women with extremely dense breast.

•

The more frequent the use of AP-MRI, the more life years will be gained.

•

Biennial AP-MRI for women with extremely dense breast up to age 65 is optimal.

Evaluation of an Ultra-Short MRI Protocol for Cerebral Staging Examinations in Melanoma Patients

PURPOSE

 Due to its high sensitivity and lack of radiation, MRI is often used to stage cerebral tumors in patients. In contrast, the relatively long examination times and the limited availability of MRI slots at the clinic might delay these examinations. The aim of this study was to compare an ultra-short MRI protocol with the routinely used standard protocol.

MATERIALS AND METHODS

 Cerebral MRI of 147 patients with malignant melanoma were evaluated retrospectively, whereby only two sequences (FLAIR images and contrast-enhanced T1 MPR images) were evaluated in one group and images from the whole examination were available for the second group, including five sequences (DWI, T2 TSE, FLAIR, native and contrast-enhanced T1 TSE, and contrast-enhanced T1 MPR). The results of the two groups were compared and tested to determine whether the ultra-short approach was inferior to the full examination.

RESULTS

 13.6 % of the patients had cerebral metastases. Overall, 73 metastases were detected: 60 were located supratentorially and 13 infratentorially. Concerning the detection of cerebral metastases, the ultra-short MRI examination, involving only a FLAIR and a contrast-enhanced T1 MPR sequence, was not inferior to the full MRI protocol in general (p = 0.017) and separated by location for supratentorial (p = 0.026) and infratentorial (p = 0.001) metastases.

CONCLUSION

 For staging purposes, a focused, ultra-short MRI protocol is not inferior to a standard MRI examination. This might open up opportunities for faster staging processes and a more efficient use of the often-restricted MRI capacities.

KEY POINTS

  · Short MRI protocols for cerebral staging are not inferior to standard examinations.. · Contrast-enhanced images represent the centerpiece of an ultra-short MRI protocol.. · Short MRI protocols might enable a more efficient use of restricted resources..

CITATION FORMAT

· Peters S, Gärtner F, Austein F et al. Evaluation of an Ultra-Short MRI Protocol for Cerebral Staging Examinations in Melanoma Patients. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1669-9408.

MRI versus Mammography plus Ultrasound in Women at Intermediate Breast Cancer Risk: Study Design and Protocol of the MRIB Multicenter, Randomized, Controlled Trial

In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40-59, with a 15-30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15-30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, K^trans (volume transfer constant) and v_e (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the K^trans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

Clinical usefulness of the fast protocol of breast diffusion-weighted imaging using 3T magnetic resonance imaging with a 16-channel breast coil

We aimed to evaluate the usefulness of a fast protocol of diffusion-weighted imaging (DWI) with one excitation using 3T magnetic resonance imaging (MRI) and a 16-channel breast coil. We analyzed 30 lesions from 27 women between February 2020 and June 2020. The visibility score (from 1 = extremely poor to 5 = excellent) and apparent diffusion coefficient (ADC) value between one and four excitations were evaluated by two readers. The image acquisition time was 40 s for one excitation and 1 min 52 s for four excitations. The visibility scores were 4.630 ± 0.718 and 4.267 ± 1.015 for one excitation and 4.730 ± 0.691 and 4.200 ± 1.000 for four excitations by the two readers. There was no significant difference in the visibility (P = 0.184 and P = 0.423), mean ADC value (P = 0.918 and P = 0.417), and minimum ADC value (P = 0.936 and P = 0.443) between one and four excitations by the two readers. Despite the short acquisition time, the visibility score and ADC values of one-excitation DWI were comparable to that with four excitations. Our fast DWI protocol could provide reproducible visibility and ADC value, potentially helping radiologists to efficiently diagnose patients.

Risk Management for BRCA1/BRCA2 mutation carriers without and with breast cancer

Purpose of review

We review the management for unaffected BRCA1/2 mutation carriers and the local management of early stage breast cancer.

Recent findings

For unaffected BRCA1/2 mutation carriers, surveillance includes annual magnetic resonance imaging (MRI) and mammogram (MG). Novel imaging modalities, including abbreviated protocol MRI, ultrafast/accelerated MRI, and contrast-enhanced digital mammography are being investigated. Risk reducing mastectomy (RRM) should be considered, and nipple-areolar sparing mastectomy (NSM) is now an option. Additionally, risk reducing salpingo-oophorectomy (RRSO) is strongly recommended as it reduces mortality.In BRCA1/2 mutation carriers with breast cancer, BCT is an appropriate treatment option but to reduce risk of second primary, mastectomy and contralateral risk-reducing mastectomy should be considered.

A meta-analysis comparing the diagnostic performance of abbreviated MRI and a full diagnostic protocol in breast cancer

AIM

To undertake a meta-analysis of the diagnostic performance of abbreviated (ABB) magnetic resonance imaging (MRI) and full diagnostic protocol MRI (FDP-MRI) in breast cancer.

MATERIALS AND METHODS

This meta-analysis was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Diagnostic Test Accuracy (PRISMA-DTA) guidelines. The PubMed and EMBASE databases were searched through August 2019 for studies comparing the diagnostic performance of ABB-MRI and FDP-MRI in the breast. Studies were reviewed by two authors independently according to eligibility and exclusion criteria and split into two subgroups (screening population studies and studies using cohorts enriched with known cancers) to avoid bias. Quality assessment and bias for diagnostic accuracy was determined with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The diagnostic accuracy for each subgroup was pooled using a bivariate random effects model and summary receiver operating characteristic (sROC) curves produced. Sensitivities and specificities were compared using a paired t-test.

RESULTS

Five screening (62/2,588 cancers/patients) and eight enriched cohort (540/1,432 cancers/patients) studies were included in the meta-analysis. QUADAS-2 assessment showed a low risk of bias in most studies. The pooled sensitivity/specificity/area under the receiver operating characteristic curve (AUC) for screening studies was 0.90/0.92/0.94 for ABB-MRI and 0.92/0.95/0.97 for FDP-MRI. The pooled sensitivity/specificity/AUC for enriched cohort studies was 0.93/0.83/0.94 for ABB-MRI and 0.93/0.84/0.95 for FDP-MRI. There was no significant difference in sensitivity or specificity using ABB-MRI or FDP-MRI (p=0.18 and 0.27, p=0.18 and 0.93, respectively).

CONCLUSION

The diagnostic performances of the ABB-MRI and FDP-MRI protocols used in either screening or enriched cohorts were comparable. There was a large variation in patient population, study methodology, and abbreviated protocols reported.

Abbreviated magnetic resonance imaging in breast cancer: A systematic review of literature

BACKGROUND

: magnetic resonance imaging (MRI) has been increasingly used to study breast cancer for screening high-risk cases, pre-operative staging, and problem-solving because of its high sensitivity. However, its cost-effectiveness is still debated. Thus, the concept of abbreviated MRI (ABB-MRI) protocols was proposed as a possible solution for reducing MRI costs.

PURPOSE

: to investigate the role of the abbreviated MRI protocols in detecting and staging breast cancer.

METHODS

: a systematic search of the literature was carried out in the bibliographic databases: Scopus, PubMed, Medline, and Science Direct.

RESULTS

: forty-one articles were included, which described results of the assessment of fifty-three abbreviated protocols for screening, staging, recurrence assessing, and problem-solving or clarification.

CONCLUSIONS

: the use of ABB-MRI protocols allows reducing the acquisition and reading times, maintaining a high concordance with the final interpretation, in comparison to a complete protocol. However, larger prospective and multicentre trials are necessary to validate the performance in specific clinical environments.

Abbreviated MRI for Hepatocellular Carcinoma Screening and Surveillance

To detect potentially curable hepatocellular carcinoma (HCC), clinical practice guidelines recommend semiannual surveillance US of the liver in adult patients at risk for developing this malignancy, such as those with cirrhosis and some patients with chronic hepatitis B infection. However, cirrhosis and a large body habitus, both of which are increasingly prevalent in the United States and the rest of the world, may impair US visualization of liver lesions and reduce the sensitivity of surveillance with this modality. The low sensitivity of US for detection of early-stage HCC contributes to delayed diagnosis and increased mortality. Abbreviated MRI, a shortened MRI protocol tailored for early-stage detection of HCC, has been proposed as an alternative surveillance option that provides high sensitivity and specificity. Abbreviated MRI protocols include fewer sequences than a complete multiphase MRI examination and are specifically designed to identify small potentially curable HCCs that may be missed at US. Three abbreviated MRI strategies have been studied: (a) nonenhanced, (b) dynamic contrast material-enhanced, and (c) hepatobiliary phase contrast-enhanced abbreviated MRI. Retrospective studies have shown that simulated abbreviated MRI provides high sensitivity and specificity for early-stage HCC, mostly in nonsurveillance cohorts. If it is supported by scientific evidence in surveillance populations, adoption of abbreviated MRI could advance clinical practice by increasing early detection of HCC, allowing effective treatment and potentially prolonging life in the growing number of individuals with this cancer. Online supplemental material is available for this article. ©RSNA, 2020.

The potential utility of abbreviated breast MRI (FAST MRI) as a tool for breast cancer screening: a systematic review and meta-analysis

AIM

To synthesise evidence comparing abbreviated breast magnetic resonance imaging (abMRI) to full-protocol MRI (fpMRI) in breast cancer screening.

MATERIALS AND METHODS

A systematic search was undertaken in multiple databases. Cohort studies without enrichment, presenting accuracy data of abMRI in screening, for any level of risk (population, moderate, high risk) were included. Level of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Meta-analyses (bivariate random effects model) were performed for abMRI, with fpMRI and histology from fpMRI-positive cases as reference standard, and with follow-up to symptomatic detection added to the fpMRI. The review also covers evidence comparing abMRI with mammographic techniques.

RESULTS

The title and abstract review retrieved 23 articles. Five studies (six articles) were included (2,763 women, 3,251 screening rounds). GRADE assessment of the evidence was very low because the reference standard was interpreted with knowledge of the index test and biopsy was not obtained for all abMRI positives. The overall sensitivity for abMRI, with fpMRI (and histology for fpMRI positives) as reference standard, was 94.8% (95% confidence interval [CI] 85.5-98.2) and specificity as 94.6% (95% CI: 91.5-96.6). Three studies (1,450 women, 1,613 screening rounds) presented follow-up data, enabling comparison between abMRI and fpMRI. Sensitivities and specificities for abMRI did not differ significantly from those for fpMRI (p=0.83 and p=0.37, respectively).

CONCLUSION

A very low level of evidence suggests abMRI could be accurate for breast cancer screening. Research is required, with follow-up to interval cancer, to determine the effect its use could have on clinical outcome.

Abbreviated Screening Breast MRI in Women at Higher-than-Average Risk for Breast Cancer with Prior Normal Full Protocol MRI

OBJECTIVE

To compare the performance of abbreviated screening breast MRI (ABMR) versus full protocol MRI (FPMR) in women at higher-than-average risk for breast cancer with a prior normal FPMR.

METHODS

ABMR was performed on higher-than-average-risk women who had a prior normal FPMR. ABMR protocol consisted of short inversion time inversion recovery imaging, precontrast, and two early postcontrast sequences acquired in under 10 minutes. Retrospective review of ABMR examinations performed from July 2016 to July 2018 was compared with a control group who underwent routine screening with FPMR who had a prior normal FPMR performed from July 2014 to June 2016. Screening outcome metrics were calculated and compared, adjusting for differences in patient demographics.

RESULTS

The study cohort included 481 ABMR examinations, while the control group included 440 FPMR studies. There was no significant difference in the abnormal interpretation rate (AIR) or cancer detection rate (CDR) for the ABMR versus the FPMR group (AIR 6.0% vs 6.8% respectively, odds ratio (OR) 0.91, 95% confidence interval (CI): 0.53-1.5, P = 0.73; CDR 8.3 vs 11 cancers detected per 1000 examinations respectively, OR 0.73, 95% CI: 0.20-2.7, P = 0.64). The PPV2 and PPV3 for the ABMR group was 19% and 21% versus 16% and 16% for the FPMR group, with no statistical difference. Sensitivity was 100% in each group with no interval cancers. There was no difference in specificity between the ABMR and FPMR groups, 93% versus 94%, respectively (P = 0.73).

CONCLUSION

ABMR may be used to screen higher-than-average-risk women with a prior normal FPMR as outcome metrics are equivalent to FPMR.

Multiple analyte profiling (MAP) index as a powerful diagnostic and therapeutic monitoring tool

A robust data mining algorithm is presented as a critical solution to the challenge of managing intensive data generated from the recently developed multiplexing techniques, which allow simultaneous detection of up to 500 biomarkers in a few microliters of a single sample. Furthermore, detailed methodology is provided for exploiting the new algorithm along with examples for description of the first application as a powerful diagnostic and therapeutic monitoring tool in the management of breast cancer, as a disease model.

Comparison of Contrast-Enhanced Mammography With Conventional Digital Mammography in Breast Cancer Screening: A Pilot Study

PURPOSE

To perform a pilot evaluation of contrast-enhanced mammography (CEM) for screening to determine whether it can improve accuracy and reader confidence in diagnosis.

METHODS AND MATERIALS

This institutional review board-approved reader study was comprised of 64 de-identified CEM cases acquired from December 1, 2014, to June 7, 2016, including 48 negative, 5 biopsy-proven benign, and 11 biopsy-proven malignancies. Negative cases were followed for at least 2 years without evidence of cancer. Ten breast imagers of varying experience first rated the low-energy (LE) mammogram and then the CEM examination using BI-RADS categories and a 5-point Likert scale for confidence in diagnosis.

RESULTS

There were 635 out a total possible 640 complete reader interpretations included in this analysis. The remaining five incomplete interpretations were excluded. Median sensitivity and specificity improved with the addition of CEM (sensitivity: 0.86 [95% confidence interval {CI}: 0.74-0.95] versus 1 [95% CI: 0.83-1.00], specificity: 0.85 [95% CI: 0.64-0.94] versus 0.88 [95% CI: 0.80-0.92]). Individual receiver operating characteristic curves showed significant improvement with CEM (mean area under the curve increase = 0.056 [95% CI: 0.015-0.097], P = .002). The addition of CEM significantly improved average confidence in 5 of 10 readers when compared with LE (P < .0001) and improved pooled confidence across all tissue density categories, except the almost entirely fatty category. There was a trend toward improved confidence with increasing tissue density with CEM. Degree of background parenchymal enhancement did not affect readers' level of improvement in confidence when interpreting CEM.

SUMMARY

CEM improved reader performance and confidence compared with viewing only LE, suggesting a role for CEM in breast cancer screening for which larger trials are warranted.