Breast cancer imaging with MRI

A near-infrared fluorescent probe with assembly/aggregation-induced retention effect for specific diagnosis of metastasis and image-guided surgery in breast cancer

Image-guided surgery is crucial for achieving complete tumor resection, reducing postoperative recurrence and improving patient survival. However, current clinical near-infrared fluorescent probes, such as indocyanine green (ICG), face two main limitations: 1) lack of active tumor targeting, and 2) short retention time in tumors, which restricts real-time imaging during surgery. To address these issues, we developed a near-infrared fluorescent probe capable of in situ nanofiber formation within tumor lesions. This probe actively targets the integrin αvβ3 receptors overexpressed on breast cancer cells and exhibits assembly/aggregation-induced retention effects at the tumor site, significantly extending the imaging time window. Additionally, we found that the probe's fluorescence intensity can be enhanced under receptor induction. Due to its excellent tumor specificity and sensitivity, 1FCG-FFGRGD not only identifies primary breast cancer but also precisely locates smaller lymph node metastases and detects sub-millimeter peritoneal metastases. In summary, this near-infrared probe, leveraging assembly/aggregation-induced retention effects, holds substantial potential for various biomedical applications.

An investigation on protection properties of Tantalum (V) oxide reinforced glass screens on unexposed breast tissue for mammography examinations

INTRODUCTION

The utilization of radiation shielding material positioned between the both breasts are crucial for the reduction of glandular dose and the safeguarding of the contralateral breast during mammographic procedures. This study proposes an alternative substance for shielding the contralateral breast from radiation exposure during mammography screening.

METHODS

In this study, we present an analysis of the shielding effectiveness of transparent glass that has been doped with Tantalum (V) oxide encoded as BTZT6. The evaluation of this shielding material was conducted using the MCNPX code, specifically for the ipsilateral and contralateral breasts. The design of the left and right breast phantoms involved the creation of three-layer heterogeneous breast phantoms, consisting of varying proportions of glandular tissue (25%, 50%, and 75%). The design of BTZT6 and lead-acrylic shielding screens is implemented using the MCNPX code. The comparative analysis of dose outcomes is conducted to assess the protective efficacy of BTZT6 and lead-acrylic shielding screens.

RESULTS

The utilization of BTZT6 shielding material resulted in a reduction in both breast dose and skin dose exposure when compared to the lead-acrylic shield.

CONCLUSION

Based on the findings acquired, the utilization of BTZT6 shielding material screens during mammography procedures involving X-rays with energy levels ranging from 26 to 30 keV is associated with a decrease in radiation dose.

IMPLICATIONS FOR PRACTICE

It can be inferred that the utilization of BTZT6 demonstrates potential efficacy in mitigating excessive radiation exposure to the breasts and facilitating the quantification of glandular doses in mammography procedures.

Harnessing the power of Microscale AcoustoFluidics: A perspective based on BAW cancer diagnostics

Cancer directly affects one in every three people, and mortality rates strongly correlate with the stage at which diagnosis occurs. Each of the multitude of methods used in cancer diagnostics has its own set of advantages and disadvantages. Two common drawbacks are a limited information value of image based diagnostic methods and high invasiveness when opting for methods that provide greater insight. Microfluidics offers a promising avenue for isolating circulating tumor cells from blood samples, offering high informational value at predetermined time intervals while being minimally invasive. Microscale AcoustoFluidics, an active method capable of manipulating objects within a fluid, has shown its potential use for the isolation and measurement of circulating tumor cells, but its full potential has yet to be harnessed. Extensive research has focused on isolating single cells, although the significance of clusters should not be overlooked and requires attention within the field. Moreover, there is room for improvement by designing smaller and automated devices to enhance user-friendliness and efficiency as illustrated by the use of bulk acoustic wave devices in cancer diagnostics. This next generation of setups and devices could minimize streaming forces and thereby enable the manipulation of smaller objects, thus aiding in the implementation of personalized oncology for the next generation of cancer treatments.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Exploring the Radioprotective Indium (III) Oxide Screens for Mammography Scans Using a Three-Layer Heterogeneous Breast Phantom and MCNPX: A Comparative Study Using Clinical Findings

Background: During mammography, a lead-acrylic protective screen is recommended to reduce radiation exposure to the unexposed breast. Objectives: This research study aimed to construct an Indium-(III)-oxide-rich tellurite-glass screen (TZI8) and compare its performance to that of lead acrylic. Materials and Methods: A three-layer heterogeneous-breast phantom was developed, using the MCNPX (version 2.7.0) Monte Carlo code. An MCNPX-simulation geometry was designed and implemented, using the lead-acrylic and TZI8 shielding screens between the right and left breast. Next, the reliability of the phantom and the variations in absorption between the lead-acrylic and TZI8 glass were investigated. Results: The findings show that the TZI8-protective-glass screen offers significantly greater radioprotection than the lead-acrylic material. The quantity of total dose absorbed in the unexposed breast was much lower for TZI8 than for lead-based acrylic. The TZI8-glass screen gives about 60% more radioprotection than the lead-acrylic screen. Conclusion: Considering the toxic lead in the structure that may be hazardous to the human tissues, the TZI8-glass screen may be used in mammography examination to provide greater radioprotection than the lead-acrylic screen, in order to greatly reduce the dose to the unexposed breast.

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.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning

Background and Objective. Breast cancer is a common malignant tumor that seriously threatens the health of women in my country and even around the world. The proliferation marker Ki-67 has been utilized to distinguish luminal B from luminal A tumors and is a reliable indicator of more aggressive breast cancer growth. If a reliable prediction method for breast cancer patients to avoid invasive damage can be found to predict Ki-67 before pathological examination, it will be very beneficial for doctors to formulate later treatment plans and provide more useful treatment options. Methodology. This paper proposes a tumor segmentation and prediction framework based on the combination of improved attention U-Net and SVM. The framework first improves on attention U-Net by introducing coefficients for learning multidimensional attention. Make the attention mechanism more aware of the main situation in the segmentation process. At the same time, the segmented breast MRI results and corresponding labels were input into the SVM classifier to accurately predict the expression of Ki-67. Results. The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion. Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.

Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning

Background

Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today's clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network's labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation.

Results

We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors.

Conclusion

Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

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.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor can be categorized into two main groups—in situ and infiltrative, with the latter being the most common malignant lesions. The current use of magnetic resonance imaging (MRI) was shown to provide the highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a computer-aided detection/diagnosis (CAD) system that could provide valuable assistance to radiologists for discrimination between in situ and infiltrating tumors. The system consists of two main processing levels—(1) localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter), followed by a deep-feature extraction and classification method for false-positive rejection; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE–MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.

Correcting time-intensity curves in dynamic contrast-enhanced breast MRI for inhomogeneous excitation fields at 7T

Purpose

Inhomogeneous excitation at ultrahigh field strengths (7T and above) compromises the reliability of quantified dynamic contrast‐enhanced breast MRI. This can hamper the introduction of ultrahigh field MRI into the clinic. Compensation for this non‐uniformity effect can consist of both hardware improvements and post‐acquisition corrections. This paper investigated the correctable radiofrequency transmit (B1+) range post‐acquisition in both simulations and patient data for 7T MRI.

Methods

Simulations were conducted to determine the minimum B1+ level at which corrections were still beneficial because of noise amplification. Two correction strategies leading to differences in noise amplification were tested. The effect of the corrections on a 7T patient data set (N = 38) with a wide range of B1+ levels was investigated in terms of time‐intensity curve types as well as washin, washout and peak enhancement values.

Results

In simulations assuming a common amount of T₁ saturation, the lowest B1+ level at which the SNR of the corrected images was at least that of the original precontrast image was 43% of the nominal angle. After correction, time‐intensity curve types changed in 24% of included patients, and the distribution of curve types corresponded better to the distribution found in literature. Additionally, the overlap between the distributions of washin, washout, and peak enhancement values for grade 1 and grade 2 tumors was slightly reduced.

Conclusion

Although the correctable range varies with the amount of T₁ saturation, post‐acquisition correction for inhomogeneous excitation was feasible down to B1+ levels of 43% of the nominal angle in vivo.

Estimating B1+ in the breast at 7 T using a generic template

Dynamic contrast-enhanced MRI is the workhorse of breast MRI, where the diagnosis of lesions is largely based on the enhancement curve shape. However, this curve shape is biased by RF transmit (B₁⁺ ) field inhomogeneities. B₁⁺ field information is required in order to correct these. The use of a generic, coil-specific B₁⁺ template is proposed and tested. Finite-difference time-domain simulations for B₁⁺ were performed for healthy female volunteers with a wide range of breast anatomies. A generic B₁⁺ template was constructed by averaging simulations based on four volunteers. Three-dimensional B₁⁺ maps were acquired in 15 other volunteers. Root mean square error (RMSE) metrics were calculated between individual simulations and the template, and between individual measurements and the template. The agreement between the proposed template approach and a B₁⁺ mapping method was compared against the agreement between acquisition and reacquisition using the same mapping protocol. RMSE values (% of nominal flip angle) comparing individual simulations with the template were in the range 2.00-4.01%, with mean 2.68%. RMSE values comparing individual measurements with the template were in the range8.1-16%, with mean 11.7%. The agreement between the proposed template approach and a B₁⁺ mapping method was only slightly worse than the agreement between two consecutive acquisitions using the same mapping protocol in one volunteer: the range of agreement increased from ±16% of the nominal angle for repeated measurement to ±22% for the B₁⁺ template. With local RF transmit coils, intersubject differences in B₁⁺ fields of the breast are comparable to the accuracy of B₁⁺ mapping methods, even at 7 T. Consequently, a single generic B₁⁺ template suits subjects over a wide range of breast anatomies, eliminating the need for a time-consuming B₁⁺ mapping protocol.

Experimental mammary carcinogenesis - Rat models

Mammary cancer is one of the most common cancers, victimizing more than half a million of women worldwide every year. Despite all the studies in this field, the current therapeutic approaches are not effective and have several devastating effects for patients. In this way, the need to better understand the mammary cancer biopathology and find effective therapies led to the development of several rodent models over years. With this review, the authors intended to provide the readers with an overview of the rat models used to study mammary carcinogenesis, with a special emphasis on chemically-induced models.

Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment

OBJECTIVE

The purposes of this study were to evaluate diagnostic parameters measured with ultrafast MRI acquisition and with standard acquisition and to compare diagnostic utility for differentiating benign from malignant lesions.

MATERIALS AND METHODS

Ultrafast acquisition is a high-temporal-resolution (7 seconds) imaging technique for obtaining 3D whole-breast images. The dynamic contrast-enhanced 3-T MRI protocol consists of an unenhanced standard and an ultrafast acquisition that includes eight contrast-enhanced ultrafast images and four standard images. Retrospective assessment was performed for 60 patients with 33 malignant and 29 benign lesions. A computer-aided detection system was used to obtain initial enhancement rate and signal enhancement ratio (SER) by means of identification of a voxel showing the highest signal intensity in the first phase of standard imaging. From the same voxel, the enhancement rate at each time point of the ultrafast acquisition and the AUC of the kinetic curve from zero to each time point of ultrafast imaging were obtained.

RESULTS

There was a statistically significant difference between benign and malignant lesions in enhancement rate and kinetic AUC for ultrafast imaging and also in initial enhancement rate and SER for standard imaging. ROC analysis showed no significant differences between enhancement rate in ultrafast imaging and SER or initial enhancement rate in standard imaging.

CONCLUSION

Ultrafast imaging is useful for discriminating benign from malignant lesions. The differential utility of ultrafast imaging is comparable to that of standard kinetic assessment in a shorter study time.

Analysis of factors influencing the degree of detectability on diffusion-weighted MRI and diffusion background signals in patients with invasive breast cancer

To determine the factors influencing the degree of detectability of lesions and diffusion background signals on magnetic resonance diffusion-weighted imaging (DWI) in invasive breast cancer.Institutional review board approval was obtained and patient consent was waived. Patients with newly diagnosed invasive ductal carcinoma, who underwent preoperative breast magnetic resonance imaging with DWI were included in this study (n = 167). Lesion detectability on DWI and contrast-enhanced subtracted T1-weighted images, the degree of background parenchymal enhancement (BPE), and diffusion background signal were qualitatively rated. Detectability of lesions on DWI was compared with clinicopathological findings including menopausal status, mammographic density, and molecular subtype of breast cancer. Multivariate linear regression analysis was performed to determine variables independently associated with detectability of lesions on DWI and diffusion background signals.Univariate analysis showed that the detectability of lesions on DWI was significantly associated with lesion size (P = 0.001), diffuse background signal (P < 0.0001), and higher detectability scores for contrast-enhanced T1-weighted subtraction images (P = 0.000). The degree of diffusion background signal was significantly affected by age (P < 0.0001), BPE (P < 0.0001), mammographic density (P = 0.002), and menopausal status (P < 0.0001). On multivariate analysis, the diffusion background signal (P < 0.0001) and histologic grade (P < 0.0001) were correlated with the detectability on DWI of invasive breast cancer. Only BPE was correlated with the amount of diffusion background signal on DWI (P < 0.0001).For invasive breast cancers, detectability on DWI was significantly affected by the diffusion background signal. BPE, menopausal status, menstrual cycle, or mammographic density did not show statistically significant correlation with the diffusion detectability of lesions on DWI.

Paget's disease of the male breast with underlying ductal carcinoma in situ ('DCIS')

Male breast cancer accounts for 1% of all breast malignancies and 0.1% of all male cancer death. Like Paget's disease, DCIS is a rare form of male breast malignancy. We report a 69-year-male presenting with 3 years history of subtle of nipple symptoms and normal breast imaging. Punch biopsy of nipple established the diagnosis of Paget's disease and subsequent histology of mastectomy specimen revealed underlying DCIS.

Contribution of kinetic characteristics of axillary lymph nodes to the diagnosis in breast magnetic resonance imaging

OBJECTIVE

To assess the contribution of kinetic characteristics in the discrimination of malignant-benign axillary lymph nodes.

MATERIAL AND METHODS

One hundred fifty-five female patients were included in the study. Following magnetic resonance imaging (MRI) examinations postprocessing applications were applied, dynamic curves were obtained from subtracted images. Types of dynamic curves were correlated with histopathological results in malignant cases or final clinical results in patients with no evidence of malignancy. Sensitivity, specificity, positive likehood ratio (+LHR), negative (-LHR) of dynamic curves characterizing the axillary lymph nodes were calculated.

RESULTS

A total of 178 lymph nodes greater than 8 mm were evaluated in 113 patients. Forty-six lymph nodes in 24 cases had malignant axillary involvement. 132 lymph nodes in 89 patients with benign diagnosis were included in the study. The sensitivity of type 3 curve as an indicator of malignancy was calculated as 89%. However the specificity, +LHR, -LHR were calculated as 14%, 1.04, 0.76 respectively.

CONCLUSION

Since kinetic analysis of both benign and malignant axillary lymph nodes, rapid enhancement and washout (type 3) they cannot be used as a discriminator, unlike breast lesions. MRI, depending on the kinetic features of the axillary lymph nodes, is not high enough to be used in the clinical management of breast cancer patients.

Perception of care and experience of examination in women at risk of breast cancer undergoing intensive surveillance by standard imaging with or without MRI

OBJECTIVE

Intensive surveillance in women at breast cancer risk is currently investigated in a French prospective, non-randomized, multicenter study, in which standard imaging--mammography±ultrasound ('Mx') and standard imaging combined with magnetic resonance imaging ('MRI') are compared with regard to perception of care and examination experience.

METHODS

1561 women were invited to complete the STAI-State Anxiety Inventory and breast cancer risk perception items at baseline (T0), and MGQ (MammoGraphy Questionnaire) and MRI discomfort items within 2 days after examinations (T1).

RESULTS

Baseline compliance was high (>91%). Women from the 'MRI' group were significantly younger and displayed higher education level and risk perception. MRI discomfort related to the duration, immobility, prone position or noise was experienced by more than 20% of women. In multivariate analyses, 'MRI' was associated with more favorable examination psychological experience (p≤.001), especially in women younger than 50; baseline STAI-State anxiety was associated with lower MGQ scores (p≤.001) and higher MRI discomfort (p≤.001).

CONCLUSION

In spite of the discomfort experienced with MRI, perception of care and experience with this surveillance procedure was more positive than with standard imaging.

PRACTICE IMPLICATIONS

Information and support may assuage some of the adverse effects of an uncomfortable examination technique.

The Great Mimicker: Zona Zoster at the Mastectomy Site Causing Contralateral Intramammary Lymph Node Enlargement

Zona zoster is rarely observed in patients with malignancy; when present, it follows a dermatomal fashion. Involvement of widely separated regions is very rare. Hereby, zona zoster causing enlarged intramammary lymph nodes (IMLN) in the opposite breast is reported for the first time in literature. The masses were hypoechoic on US with no hilum and hypervascular on color Doppler US. MRI showed hypointense masses with type 3 time-intensity curve and adjacent vessel sign. The complete regression of the nodes after the antiviral therapy confirmed the diagnosis. In breast cancer patients, IMLN enlargements may mimic breast cancer metastasis, and zona zoster infection of the mastectomy site may present with contralateral IMLN enlargement due to altered lymphatic drainage. When breast US is not sufficient for the differential diagnosis, breast MRI may warrant proper diagnosis, and prevent unnecessary biopsies. Antiviral treatment with followup would be sufficient for management.

Application of Benchtop-magnetic resonance imaging in a nude mouse tumor model

Background

MRI plays a key role in the preclinical development of new drugs, diagnostics and their delivery systems. However, very high installation and running costs of existing superconducting MRI machines limit the spread of MRI. The new method of Benchtop-MRI (BT-MRI) has the potential to overcome this limitation due to much lower installation and almost no running costs. However, due to the low field strength and decreased magnet homogeneity it is questionable, whether BT-MRI can achieve sufficient image quality to provide useful information for preclinical in vivo studies. It was the aim of the current study to explore the potential of BT-MRI on tumor models in mice.

Methods

We used a prototype of an in vivo BT-MRI apparatus to visualise organs and tumors and to analyse tumor progression in nude mouse xenograft models of human testicular germ cell tumor and colon carcinoma.

Results

Subcutaneous xenografts were easily identified as relative hypointense areas in transaxial slices of NMR images. Monitoring of tumor progression evaluated by pixel extension analyses based on NMR images correlated with increasing tumor volume calculated by calliper measurement. Gd-BOPTA contrast agent injection resulted in a better differentiation between parts of the urinary tissues and organs due to fast elimination of the agent via kidneys. In addition, interior structuring of tumors could be observed. A strong contrast enhancement within a tumor was associated with a central necrotic/fibrotic area.

Conclusions

BT-MRI provides satisfactory image quality to visualize organs and tumors and to monitor tumor progression and structure in mouse models.

Time for a re-evaluation of mammography in the young? Results of an audit of mammography in women younger than 40 in a resource restricted environment

Mammography in younger women is considered to be of limited value. In a resource restricted environment without access to magnetic resonance imaging (MRI) and with a high incidence of breast cancer in the young, mammography remains an important diagnostic tool. Recent technical advances and better regulation of mammography make a reassessment of its value in these conditions necessary. Data of all the mammograms performed at a tertiary hospital and private breast clinic between January 2003 and July 2009 in women less than 40 years of age were collected. Indications were the presence of a mass, follow-up after primary cancer therapy, and screening for patients perceived at high risk due to a family history or the presence of atypical hyperplasia. Data acquired were as follows: Demographics, prior breast surgery, indication for mammography, outcome of mammography, diagnostic procedures, and their results. Of 2,167 mammograms, 393 were performed for a palpable mass, diagnostic mammography. In these, the overall cancer detection rate was 40%. If the mammography was reported as breast imaging reporting and data system (BIRADS(®)) 5 versus BIRADS(®) 3 and 4 versus BIRADS(®) 1 and 2, a final diagnosis of malignancy was established in 96, 48, and 5%, respectively. Of 367 mammograms done for the follow-up after primary treatment of breast cancer, seven cancers were diagnosed for a detection rate of 1.9%. Of 1,312 mammograms performed for screening, the recall rate was 4%; the biopsy rate 2%, and the cancer diagnosis rate 3/1,000 examinations. In contrast to past series, this series has shown that recent advances in mammography have made it a useful tool in the management of breast problems in young women, notably in a resource-restricted environment. Women for screening should be selected carefully.

Combined optical and X-ray tomosynthesis breast imaging

PURPOSE

To explore the optical and physiologic properties of normal and lesion-bearing breasts by using a combined optical and digital breast tomosynthesis (DBT) imaging system.

MATERIALS AND METHODS

Institutional review board approval and patient informed consent were obtained for this HIPAA-compliant study. Combined optical and tomosynthesis imaging analysis was performed in 189 breasts from 125 subjects (mean age, 56 years ± 13 [standard deviation]), including 138 breasts with negative findings and 51 breasts with lesions. Three-dimensional (3D) maps of total hemoglobin concentration (Hb(T)), oxygen saturation (So(2)), and tissue reduced scattering coefficients were interpreted by using the coregistered DBT images. Paired and unpaired t tests were performed between various tissue types to identify significant differences.

RESULTS

The estimated average bulk Hb(T) from 138 normal breasts was 19.2 μmol/L. The corresponding mean So(2) was 0.73, within the range of values in the literature. A linear correlation (R = 0.57, P < .0001) was found between Hb(T) and the fibroglandular volume fraction derived from the 3D DBT scans. Optical reconstructions of normal breasts revealed structures corresponding to chest-wall muscle, fibroglandular, and adipose tissues in the Hb(T), So(2), and scattering images. In 26 malignant tumors of 0.6-2.5 cm in size, Hb(T) was significantly greater than that in the fibroglandular tissue of the same breast (P = .0062). Solid benign lesions (n = 17) and cysts (n = 8) had significantly lower Hb(T) contrast than did the malignant lesions (P = .025 and P = .0033, respectively).

CONCLUSION

The optical and DBT images were structurally consistent. The malignant tumors and benign lesions demonstrated different Hb(T) and scattering contrasts, which can potentially be exploited to reduce the false-positive rate of conventional mammography and unnecessary biopsies.

Color Doppler ultrasonography for treatment response prediction and evaluation in breast cancer

Primary systemic therapy is a well-established modality of treatment in locally advanced breast cancer. Assessment of tumor response to chemotherapy not only helps in assessing the efficacy of the regimen used but also predicts the overall outcome of the patient. The tumor vascularity is a surrogate marker of tumor burden and this can be readily assessed by color Doppler ultrasound using various indices (resistivity index, pulsatility index and maximum flow velocity). The pre- and post-chemotherapy indices can be compared with in order assess the response to chemotherapy. Among various imaging modalities, MRI and PET have the highest sensitivity in detecting the tumor response, but they are not cost effective. Color Doppler ultrasound is a promising alternative for tumor response assessment owing to its availability, reproducibility and cost–effectiveness.

Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers

PURPOSE

To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions.

MATERIALS AND METHODS

In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis.

RESULTS

Differentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 +/- 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions.

CONCLUSION

Computer-aided diagnosis of breast DCE MR imaging-depicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09090838/-/DC1.

A high spatial resolution in vivo 1H magnetic resonance spectroscopic imaging technique for the human breast at 3 T

PURPOSE

The technical challenges that have prevented routine proton magnetic resonance spectroscopic imaging (1H MRSI) examinations of the breast include insufficient spatial resolution, increased difficulties in shimming compared to the brain, and strong lipid contamination at short echo time (TE) at 1.5 T. The authors investigated the feasibility of high spatial resolution 1H MRSI of human breast cancer in a clinical setting at 3 T.

METHODS

Ten patient studies (eight cancers and two benign lesions) were performed in a 3 T whole-body clinical imager using a pulse sequence consisting of optional outer volume presaturation, optional CHESS pulse for lipid suppression, CHESS pulse for water suppression, and standard 2D/3D PRESS pulse sequence with an elliptical weighted k-space sampling scheme.

RESULTS

All ten studies were technically successful. The spectral quality was acceptable for all cases even the one with a 65 Hz width of water peak at half height. Choline (Cho) signals were clearly visible in malignant lesion areas, while there was no detectable Cho in normal appearing breast or in benign lesions. It was also observed that the distribution of Cho signal can be nonuniform across MRI demonstrated lesions.

CONCLUSIONS

To the author's knowledge, this is the first 2D/3D MRSI study of human breast cancer with short TE (less than 135 ms) at 3 T and the highest spatial resolution (up to 0.25 cm3) to date. In conclusion, the authors have presented a robust technique for high spatial resolution in vivo 1H MRSI of human breast cancer that uses the combined advantages of high field, short TE, multivoxel, and high spatial resolution itself to overcome the major technical challenges and illustrated its potential for routine clinical examination as well as advantages over single-voxel techniques in studying metabolite heterogeneity.

Can breast MRI computer-aided detection (CAD) improve radiologist accuracy for lesions detected at MRI screening and recommended for biopsy in a high-risk population?

AIM

To evaluate the sensitivity and specificity of magnetic resonance imaging (MRI) computer-aided detection (CAD) for breast MRI screen-detected lesions recommended for biopsy in a high-risk population.

MATERIAL AND METHODS

Fifty-six consecutive Breast Imaging Reporting and Data System (BI-RADS) 3-5 lesions with histopathological correlation [nine invasive cancers, 13 ductal carcinoma in situ (DCIS) and 34 benign] were retrospectively evaluated using a breast MRI CAD prototype (CAD-Gaea). CAD evaluation was performed separately and in consensus by two radiologists specializing in breast imaging, blinded to the histopathology. Thresholds of 50, 80, and 100% and delayed enhancement were independently assessed with CAD. Lesions were rated as malignant or benign according to threshold and delayed enhancement only and in combination. Sensitivities, specificities, and negative predictive values (NPV) were determined for CAD assessments versus pathology. Initial MRI BI-RADS interpretation without CAD versus CAD assessments were compared using paired binary diagnostic tests.

RESULTS

Threshold levels for lesion enhancement were: 50% to include all malignant (and all benign) lesions; and 100% for all invasive cancer and high-grade DCIS. Combined use of threshold and enhancement patterns for CAD assessment was best (73% sensitivity, 56% specificity and 76% NPV for all cancer). Sensitivities and NPV were better for invasive cancer (100%/100%) than for all malignancies (54%/76%). Radiologists' MRI interpretation was more sensitive than CAD (p=0.05), but less specific (p=0.001) for cancer detection.

CONCLUSION

The breast MRI CAD system used could not improve the radiologists' accuracy for distinguishing all malignant from benign lesions, due to the poor sensitivity for DCIS detection.

Diagnostic value of MR elastography in addition to contrast-enhanced MR imaging of the breast-initial clinical results

OBJECTIVE

The purpose of the study was to assess the additional value of magnetic resonance (MR) elastography (MRE) to contrast-enhanced (ce) MR imaging (MRI) for breast lesion characterisation.

METHODS

Fifty-seven suspected breast lesions in 57 patients (mean age 52.4 years) were examined by ce MRI and MRE. All lesions were classified into BI-RADS categories. Viscoelastic parameters, e.g. alpha0 as an indicator of tissue stiffness, were calculated. Histology of the lesions was correlated with BI-RADS and viscoelastic properties. The positive predictive value (PPV) for malignancy, and the sensitivity and specificity of ce MRI were calculated. Receiver-operating characteristics (ROC) curves were separately calculated for both ce MRI and viscoelastic properties and conjoined to analyse the accuracy of diagnostic performance.

RESULTS

The lesions (mean size 27.6 mm) were malignant in 64.9% (n = 37) of cases. The PPV for malignancy was significantly (p < 0.0001) dependent on BI-RADS classification. The sensitivity of ce MRI for breast cancer detection was 97.3% (36/37), whereas specificity was 55% (11/20). If ce MRI was combined with alpha0, the diagnostic accuracy could be significantly increased (p < 0.05; AUC(ce MRI) = 0.93, AUC(combined) = 0.96).

CONCLUSIONS

In this study, the combination of MRE and ce MRI could increase the diagnostic performance of breast MRI. Further investigations of larger cohorts and smaller lesions (in particular those only visible on MRI) are necessary to validate these results.

Breast MRI: State of the Art

Magnetic resonance imaging (MRI) of the breast has emerged as a useful adjunct in evaluation of breast disease. For the past 25 years its use has been explored extensively in the literature and specific clinical indications have been developed. This review will address the current state of the art of breast MRI, including image acquisition, interpretation, limitations, and current applications. We also will discuss briefly emerging techniques that may further advance the practice of breast MRI evaluation.

[Quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography]

PURPOSE

The aim of the study was an evaluation of the quantitative parametric analysis of contrast-enhanced lesions in dynamic MR mammography.

MATERIAL AND METHODS

In 137 patients, 183 contrast-enhanced lesions were identified in dynamic MR mammography. In 82 lesions histopathology was performed and in 101 lesions follow-up MR mammography was carried out. The contrast kinetics of lesions was analyzed quantitatively, on a pixel-by-pixel basis. The initial signal enhancement was coded by color intensity (bright, medium, dark), the post-initial signal enhancement was coded by color hue (blue, green, red). ROC analysis and logistic regression were performed.

RESULTS

Malignant lesions showed a significantly higher number of bright red, medium red and dark red, bright green and medium green pixels than benign lesions. Benign lesions showed a significantly higher number of bright blue, medium blue and dark blue pixels than malignant lesions. The highest areas under the ROC curves (AUC) were found for medium red (AUC = 0.782) and medium green pixels (AUC = 0.733). A regression model with medium red and medium green pixels allows diagnosis of malignant lesions with a sensitivity of 60.7% and a specificity of 83.6%.

CONCLUSIONS

The quantification of contrast-enhanced lesions allows objective analysis of the signal intensities in malignant and benign lesions. Therefore, this method might increase the specificity of MR mammography. Further developments are necessary before this method can be used for routine analysis of contrast-enhancing lesions in MR mammography.

Sensitivity of imaging for multifocal-multicentric breast carcinoma

Background

This retrospective study aims to determine: 1) the sensitivity of preoperative mammography (Mx) and ultrasound (US), and re-reviewed Mx to detect multifocal multicentric breast carcinoma (MMBC), defined by pathology on surgical specimens, and 2) to analyze the characteristics of both detected and undetected foci on Mx and US.

Methods

Three experienced breast radiologists re-reviewed, independently, digital mammography of 97 women with MMBC pathologically diagnosed on surgical specimens. The radiologists were informed of all neoplastic foci, and blinded to the original mammograms and US reports. With regards to Mx, they considered the breast density, number of foci, the Mx characteristics of the lesions and their BI-RADS classification. For US, they considered size of the lesions, BI-RADS classification and US pattern and lesion characteristics. According to the histological size, the lesions were classified as: index cancer, 2nd lesion, 3rd lesion, and 4th lesion. Any pathologically identified malignant foci not previously described in the original imaging reports, were defined as undetected or missed lesions. Sensitivity was calculated for Mx, US and re-reviewed Mx for detecting the presence of the index cancer as well as additional satellite lesions.

Results

Pathological examination revealed 13 multifocal and 84 multicentric cancers with a total of 303 malignant foci (282 invasive and 21 non invasive). Original Mx and US reports had an overall sensitivity of 45.5% and 52.9%, respectively. Mx detected 83/97 index cancers with a sensitivity of 85.6%. The number of lesions undetected by original Mx was 165/303. The Mx pattern of breasts with undetected lesions were: fatty in 3 (1.8%); scattered fibroglandular density in 40 (24.3%), heterogeneously dense in 91 (55.1%) and dense in 31 (18.8%) cases. In breasts with an almost entirely fatty pattern, Mx sensitivity was 100%, while in fibroglandular or dense pattern it was reduced to 45.5%. Re-reviewed Mx detected only 3 additional lesions. The sensitivity of Mx was affected by the presence of dense breast tissue which obscured lesions or by an incorrect interpretation of suspicious findings.

US detected 73/80 index cancers (sensitivity of 91.2%), US missed 117 malignant foci with a mean tumor diameter of 6.5 mm; the sensitivity was 52.9%

Undetected lesions by US were those smallest in size and present in fatty breast or in the presence of microcalcifications without a visible mass.

US sensitivity was affected by the presence of fatty tissue or by the extent of calcification.

Conclusion

Mx missed MMBC malignant foci more often in dense or fibroglandular breasts. US missed small lesions in mainly fatty breasts or when there were only microcalcifications. The combined sensitivity of both techniques to assess MMBC was 58%. We suggest larger studies on multimodality imaging.

Recognizing and interpreting artifacts and pitfalls in MR imaging of the breast

Magnetic resonance (MR) imaging of the breast has evolved into an important adjunctive tool in breast imaging with multiple and ever-increasing indications for its use. As with other types of MR imaging, there are a number of technical artifacts and pitfalls that can potentially limit interpretation of the images by masking or simulating disease. Because of the coils and computer-aided detection software specific to breast MR imaging, there are additional technical considerations that are unique to this type of MR imaging. Motion and misregistration artifacts, wraparound artifact, susceptibility artifact, poor fat saturation, lack of contrast material, and poor timing of the contrast material bolus are some of the artifacts and pitfalls that can make interpretation of breast MR images challenging and lead to misdiagnosis. Other important considerations in proper interpretation of breast MR images include acquisition of a sufficient medical history, knowledge of benign and abnormal lesion enhancement, morphologic versus kinetic assessment, evaluation of areas outside the breast, and positioning. By using the recommended strategies, one can reduce or eliminate common artifacts and pitfalls in breast MR imaging that prevent proper interpretation of the results of this important diagnostic tool.

Basics of Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy

In this chapter, the basic principles of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) (Sects. 2.2, 2.3, and 2.4), the technical components of the MRI scanner (Sect. 2.5), and the basics of contrast agents and the application thereof (Sect. 2.6) are described. Furthermore, flow phenomena and MR angiography (Sect. 2.7) as well as diffusion and tensor imaging (Sect. 2.7) are elucidated.