Ritse M. Mann

Breast MRI: guidelines from the European Society of Breast Imaging.

The aim of breast MRI is to obtain a reliable evaluation of any lesion within the breast. It is currently always used as an adjunct to the standard diagnostic procedures of the breast, i.e., clinical examination, mammography and ultrasound. Whereas the sensitivity of breast MRI is usually very high, specificity—as in all breast imaging modalities—depends on many factors such as reader expertise, use of adequate techniques and composition of the patient cohorts. Since breast MRI will always yield MR-only visible questionable lesions that require an MR-guided intervention for clarification, MRI should only be offered by institutions that can also offer a MRI-guided breast biopsy or that are in close contact with a site that can perform this type of biopsy for them. Radiologists involved in breast imaging should ensure that they have a thorough knowledge of the MRI techniques that are necessary for breast imaging, that they know how to evaluate a breast MRI using the ACR BI-RADS MRI lexicon, and most important, when to perform breast MRI. This manuscript provides guidelines on the current best practice for the use of breast MRI, and the methods to be used, from the European Society of Breast Imaging (EUSOBI).

MRI compared to conventional diagnostic work-up in the detection and evaluation of invasive lobular carcinoma of the breast: a review of existing literature

PurposeThe clinical diagnosis and management of invasive lobular carcinoma (ILC) of the breast presents difficulties. Magnetic resonance imaging (MRI) has been proposed as the imaging modality of choice for the evaluation of ILC. Small studies addressing different aspects of MRI in ILC have been presented but no large series to date. To address the usefulness of MRI in the work-up of ILC, we performed a review of the currently published literature.Materials and methodsWe performed a literature search using the query “lobular AND (MRI OR MR OR MRT OR magnetic)” in the Cochrane library, PubMed and scholar.google.com, to retrieve all articles that dealt with the use of MRI in patients with ILC. We addressed sensitivity, morphologic appearance, correlation with pathology, detection of additional lesions, and impact of MRI on surgery as different endpoints. Whenever possible we performed meta-analysis of the pooled data.ResultsSensitivity is 93.3% and equal to overall sensitivity of MRI for malignancy in the breast. Morphologic appearance is highly heterogeneous and probably heavily influenced by interreader variability. Correlation with pathology ranges from 0.81 to 0.97; overestimation of lesion size occurs but is rare. In 32% of patients, additional ipsilateral lesions are detected and in 7% contralateral lesions are only detected by MRI. Consequently, MRI induces change in surgical management in 28.3% of cases.ConclusionThis analysis indicates MRI to be valuable in the work-up of ILC. It provides additional knowledge that cannot be obtained by conventional imaging modalities which can be helpful in patient treatment.

Large scale deep learning for computer aided detection of mammographic lesions

&NA; Recent advances in machine learning yielded new techniques to train deep neural networks, which resulted in highly successful applications in many pattern recognition tasks such as object detection and speech recognition. In this paper we provide a head‐to‐head comparison between a state‐of‐the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently. Both systems are trained on a large data set of around 45,000 images and results show the CNN outperforms the traditional CAD system at low sensitivity and performs comparable at high sensitivity. We subsequently investigate to what extent features such as location and patient information and commonly used manual features can still complement the network and see improvements at high specificity over the CNN especially with location and context features, which contain information not available to the CNN. Additionally, a reader study was performed, where the network was compared to certified screening radiologists on a patch level and we found no significant difference between the network and the readers. HighlightsA system based on deep learning is shown to outperform a state‐of‐the art CAD system.Adding complementary handcrafted features to the CNN is shown to increase performance.The system based on deep learning is shown to perform at the level of a radiologist. Graphical abstract Figure. No caption available.

Volumetric breast density estimation from Full-Field Digital Mammograms: A validation study

A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.

Breast MRI: EUSOBI recommendations for women's information.

AbstractThis paper summarizes information about breast MRI to be provided to women and referring physicians. After listing contraindications, procedure details are described, stressing the need for correct scheduling and not moving during the examination. The structured report including BI-RADS® categories and further actions after a breast MRI examination are discussed. Breast MRI is a very sensitive modality, significantly improving screening in high-risk women. It also has a role in clinical diagnosis, problem solving, and staging, impacting on patient management. However, it is not a perfect test, and occasionally breast cancers can be missed. Therefore, clinical and other imaging findings (from mammography/ultrasound) should also be considered. Conversely, MRI may detect lesions not visible on other imaging modalities turning out to be benign (false positives). These risks should be discussed with women before a breast MRI is requested/performed. Because breast MRI drawbacks depend upon the indication for the examination, basic information for the most important breast MRI indications is presented. Seventeen notes and five frequently asked questions formulated for use as direct communication to women are provided. The text was reviewed by Europa Donna–The European Breast Cancer Coalition to ensure that it can be easily understood by women undergoing MRI.Key Points• Information on breast MRI concerns advantages/disadvantages and preparation to the examination • Claustrophobia, implantable devices, allergic predisposition, and renal function should be checked • Before menopause, scheduling on day 7–14 of the cycle is preferred • During the examination, it is highly important that the patient keeps still • Availability of prior examinations improves accuracy of breast MRI interpretation

A Novel Approach to Contrast-enhanced Breast Magnetic Resonance Imaging for Screening: High-resolution Ultrafast Dynamic Imaging

ObjectivesThe use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether dynamic information from ultrafast breast MRI can be used to replace standard dynamic information to preserve accuracy. Materials and MethodsWe interleaved 20 ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (0.9 × 1 × 2.5 mm, temporal resolution, 4.3 seconds) during contrast inflow in a regular high-resolution dynamic MRI protocol. A total of 160 consecutive patients with 199 enhancing abnormalities (95 benign and 104 malignant) were included. The maximum slope of the relative enhancement versus time curve (MS) obtained from the TWIST and curve type obtained from the regular dynamic sequence as defined in the breast imaging reporting and data system (BIRADS) lexicon were recorded. Diagnostic performance was compared using receiver operating characteristic analysis. ResultsAll lesions were visible on both the TWIST and standard series. Maximum slope allows discrimination between benign and malignant disease with high accuracy (area under the curve, 0.829). Types of MS were defined in analogy to BIRADS curve types: MS type 3 implies a high risk of malignancy (MS >13.3%/s; specificity, 85%), MS type 2 yields intermediate risk (MS <13.3%/s and >6.4%/s), and MS type 1 implies a low risk (MS <6.4%/s; sensitivity, 90%). This simplification provides a much higher accuracy than the much lengthier BIRADS curve type analysis does (area under the curve, 0.812 vs 0.692; P = 0.0061). ConclusionsUltrafast dynamic breast MRI allows detection of breast lesions and classification with high accuracy using MS. This allows substantial shortening of scan protocols and hence reduces imaging costs, which is beneficial especially for screening.

Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions

The value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in characterizing breast lesions on magnetic resonance imaging (MRI) was evaluated. Sixty-eight malignant and 34 benign lesions were included. In the scanning protocol, high temporal resolution imaging was combined with high spatial resolution imaging. The high temporal resolution images were recorded every 4.1 s during initial enhancement (fast dynamic analysis). The high spatial resolution images were recorded at a temporal resolution of 86 s (slow dynamic analysis). In the fast dynamic evaluation pharmacokinetic parameters (Ktrans, Ve and kep) were evaluated. In the slow dynamic analysis, each lesion was scored according to the BI-RADS classification. Two readers evaluated all data prospectively. ROC and multivariate analysis were performed. The slow dynamic analysis resulted in an AUC of 0.85 and 0.83, respectively. The fast dynamic analysis resulted in an AUC of 0.83 in both readers. The combination of both the slow and fast dynamic analyses resulted in a significant improvement of diagnostic performance with an AUC of 0.93 and 0.90 (P = 0.02). The increased diagnostic performance found when combining both methods demonstrates the additional value of our method in further improving the diagnostic performance of breast MRI.

Automated localization of breast cancer in DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used for the detection and diagnosis of breast cancer. Compared to mammography, DCE-MRI provides higher sensitivity, however its specificity is variable. Moreover, DCE-MRI data analysis is time consuming and depends on reader expertise. The aim of this work is to propose a novel automated breast cancer localization system for DCE-MRI. Such a system can be used to support radiologists in DCE-MRI analysis by marking suspicious areas. The proposed method initially corrects for motion artifacts and segments the breast. Subsequently, blob and relative enhancement voxel features are used to locate lesion candidates. Finally, a malignancy score for each lesion candidate is obtained using region-based morphological and kinetic features computed on the segmented lesion candidate. We performed experiments to compare the use of different classifiers in the region classification stage and to study the effect of motion correction in the presented system. The performance of the algorithm was assessed using free-response operating characteristic (FROC) analysis. For this purpose, a dataset of 209 DCE-MRI studies was collected. It is composed of 95 DCE-MRI studies with 105 breast cancers (55 mass-like and 50 non-mass-like malignant lesions) and 114 DCE-MRI studies from women participating in a screening program which were diagnosed to be normal. At 4 false positives per normal case, 89% of the breast cancers (91% and 86% for mass-like and non-mass-like malignant lesions, respectively) were correctly detected.

Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework

Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image processing techniques to segment breast and fibroglandular tissue. Intra- and interpatient signal intensity variability is initially corrected. The breast is segmented by automatically detecting body-breast and air-breast surfaces. Subsequently, fibroglandular tissue is segmented in the breast area using expectation-maximization. A dataset of 50 cases with manual segmentations was used for evaluation. Dice similarity coefficient (DSC), total overlap, false negative fraction (FNF), and false positive fraction (FPF) are used to report similarity between automatic and manual segmentations. For breast segmentation, the proposed approach obtained DSC, total overlap, FNF, and FPF values of 0.94, 0.96, 0.04, and 0.07, respectively. For fibroglandular tissue segmentation, we obtained DSC, total overlap, FNF, and FPF values of 0.80, 0.85, 0.15, and 0.22, respectively. The method is relevant for researchers investigating breast density as a risk factor for breast cancer and all the described steps can be also applied in computer aided diagnosis systems.

Computer-Aided Detection of Cancer in Automated 3-D Breast Ultrasound

Automated 3-D breast ultrasound (ABUS) has gained a lot of interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and prevent errors. In the multi-stage system we propose, segmentations of the breast, the nipple and the chestwall are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and depth are extracted. Using an ensemble of neural-network classifiers, a likelihood map indicating potential abnormality is computed. Local maxima in the likelihood map are determined and form a set of candidates in each image. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. On region level, classification experiments were performed using different classifiers including an ensemble of neural networks, a support vector machine, a k-nearest neighbors, a linear discriminant, and a gentle boost classifier. Performance was determined using a dataset of 238 patients with 348 images (views), including 169 malignant and 154 benign lesions. Using free response receiver operating characteristic (FROC) analysis, the system obtains a view-based sensitivity of 64% at 1 false positives per image using an ensemble of neural-network classifiers.