Ultrasound Tomography.

Ultrasound tomography (USCT) is a promising imaging modality, mainly aiming at early diagnosis of breast cancer. It provides three-dimensional, reproducible images of higher quality than conventional ultrasound methods and additionally offers quantitative information on tissue properties. This chapter provides an introduction to the background and history of USCT, followed by an overview of image reconstruction algorithms and system design. It concludes with a discussion of current and future applications as well as limitations and their potential solutions.

Simulation-to-real generalization for deep-learning-based refraction-corrected ultrasound tomography image reconstruction.

Objective. The image reconstruction of ultrasound computed tomography is computationally expensive with conventional iterative methods. The fully learned direct deep learning reconstruction is promising to speed up image reconstruction significantly. However, for direct reconstruction from measurement data, due to the lack of real labeled data, the neural network is usually trained on a simulation dataset and shows poor performance on real data because of the simulation-to-real gap.Approach. To improve the simulation-to-real generalization of neural networks, a series of strategies are developed including a Fourier-transform-integrated neural network, measurement-domain data augmentation methods, and a self-supervised-learning-based patch-wise preprocessing neural network. Our strategies are evaluated on both the simulation dataset and real measurement datasets from two different prototype machines.Main results. The experimental results show that our deep learning methods help to improve the neural networks' robustness against noise and the generalizability to real measurement data.Significance. Our methods prove that it is possible for neural networks to achieve superior performance to traditional iterative reconstruction algorithms in imaging quality and allow for real-time 2D-image reconstruction. This study helps pave the path for the application of deep learning methods to practical ultrasound tomography image reconstruction based on simulation datasets.

Development and Validation of Biosafety Climate Scale for Biological and Biomedical Science Laboratories in the United States.

Introduction: Industry-specific safety climate scales that measure safety status have been published, however, nothing specific to biological laboratories has ever been established. Objective: This study aimed to develop and validate a biosafety climate (BSCL) scale unique for research professionals (RPs) and biosafety professionals (BPs) at teaching and research biological laboratories affiliated to public universities in the United States. Methods: BSCL scale was developed from literature review. In study 1, 15-item biosafety climate (BSCL-15) scale with 15 items and 5 factors was pretested with n = 9 RPs and n = 7 BPs to perform reliability, content, and face validity analyses. In study 2, revised 17-item biosafety climate (BSCL-17) scale with 17 items and 5 factors was pilot tested with n = 91 RPs and n = 88 BPs. Correlation tests, Kaiser-Mayer-Olkin, Bartlett's test of sphericity, Cronbach's alpha, and exploratory factor analysis (EFA) were conducted to validate the BSCL-17 scale. Results: EFA resulted in a 3-factor 17-item BSCL scale for both RPs and BPs. Internal consistency of the scale was > 0.8 for the BSCL scale and the underlying three factors, indicating high reliability. The factors identified for RPs are 1) management priority, communication and participation, 2) group norms, and 3) supervisor commitment. The factors identified for BPs are 1) management priority and communication, 2) group norms and participation, and 3) supervisor commitment. Discussion: A valid and reliable BSCL scale to measure safety climate and quantify safety culture in biological laboratories has been presented. It can be used as a key performance indicator and aid in targeted interventions as part of process improvement of biological safety programs.

Ultrasound transmission tomography image reconstruction with a fully convolutional neural network.

Image reconstruction of ultrasound computed tomography based on the wave equation is able to show much more structural details than simpler ray-based image reconstruction methods. However, to invert the wave-based forward model is computationally demanding. To address this problem, we develop an efficient fully learned image reconstruction method based on a convolutional neural network. The image is reconstructed via one forward propagation of the network given input sensor data, which is much faster than the reconstruction using conventional iterative optimization methods. To transform the ultrasound measured data in the sensor domain into the reconstructed image in the image domain, we apply multiple down-scaling and up-scaling convolutional units to efficiently increase the number of hidden layers with a large receptive and projective field that can cover all elements in inputs and outputs, respectively. For dataset generation, a paraxial approximation forward model is used to simulate ultrasound measurement data. The neural network is trained with a dataset derived from natural images in ImageNet and tested with a dataset derived from medical images in OA-Breast Phantom dataset. Test results show the superior efficiency of the proposed neural network to other reconstruction algorithms including popular neural networks. When compared with conventional iterative optimization algorithms, our neural network can reconstruct a 110 × 86 image more than 20 times faster on a CPU and 1000 times faster on a GPU with comparable image quality and is also more robust to noise.

4D co-registration of X-ray and MR-mammograms: initial clinical results and potential incremental diagnostic value.

PURPOSE: 4D co-registration of X-ray- and MR-mammograms (XM and MM) is a new method of image fusion. The present study aims to evaluate its clinical feasibility, radiological accuracy, and potential clinical value. METHODS: XM and MM of 25 patients were co-registered. Results were evaluated by a blinded reader. RESULTS: Precision of the 4D co-registration was "very good" (mean-score [ms]=7), and lesions were "easier to delineate" (ms=5). In 88.8%, "relevant additional diagnostic information" was present, accounting for a more "confident diagnosis" in 76% (ms=5). CONCLUSION: 4D co-registration is feasible, accurate, and of potential clinical value.

3D ultrasound computer tomography of the breast: a new era?

A promising candidate for imaging of breast cancer is ultrasound computer tomography (USCT). The main advantages of a USCT system are simultaneous recording of reproducible reflection, attenuation and speed of sound volumes, high image quality, and fast data acquisition. The here presented 3D USCT prototype realizes for the first time the full potential of such a device. It is ready for a clinical study. Full volumes of a breast can be acquired in four minutes. In this paper images acquired with a clinical breast phantom are presented. The resolution and imaged details of the reflectivity reconstruction are comparable to a 3 tesla MRI volume of the phantom. Image quality and resolution is isotropic in all three dimensions, confirming the successful implementation experimentally.

2D/3D image fusion of X-ray mammograms with breast MRI: visualizing dynamic contrast enhancement in mammograms.

PURPOSE: Breast cancer is the most common cancer among women. The established screening method to detect breast cancer is X-ray mammography. Additionally, MRI is used for diagnosis in clinical routine. Due to complementary diagnostic information, both modalities are often read in combination. Yet, the correlation is challenging due to different dimensionality of images and different patient positioning. In this paper, we describe a method to fuse X-ray mammograms with DCE-MRI. The present study was conducted to evaluate the feasibility of the approach. METHODS: For the combination of information from both modalities, the images have to be registered using a compression simulation based on a patient-specific biomechanical model. The registered images can be compared directly. The contrast enhancement in the DCE-MRI volume is evaluated using parametric enhancement maps. A projection image of the contrast enhancement is created. The image fusion combines it with X-ray mammograms for intuitive multimodal diagnosis. RESULTS: The image fusion was evaluated using 11 clinical datasets. For 10 of 11 datasets, a good accuracy of the image registration was achieved. The overlap of contrast-enhanced regions with marked lesions in the mammogram is 61%. Lesions are clearly differentiable from surrounding tissue by the DCE-MRI projection in 10 of 11 cases. CONCLUSION: The described preliminary results are promising, thus we expect the visualization of quantitative information from dynamic MRI together with mammograms to be beneficial for multimodal diagnosis. Because of the use of clinical standard modalities, no additional image acquisition is needed.

Fusion of dynamic contrast-enhanced magnetic resonance mammography at 3.0T with X-ray mammograms: pilot study evaluation using dedicated semi-automatic registration software.

RATIONALE AND OBJECTIVES: To evaluate the semi-automatic image registration accuracy of X-ray-mammography (XR-M) with high-resolution high-field (3.0T) MR-mammography (MR-M) in an initial pilot study. MATERIAL AND METHODS: MR-M was acquired on a high-field clinical scanner at 3.0T (T1-weighted 3D VIBE ± Gd). XR-M was obtained with state-of-the-art full-field digital systems. Seven patients with clearly delineable mass lesions >10mm both in XR-M and MR-M were enrolled (exclusion criteria: previous breast surgery; surgical intervention between XR-M and MR-M). XR-M and MR-M were matched using a dedicated image-registration algorithm allowing semi-automatic non-linear deformation of MR-M based on finite-element modeling. To identify registration errors (RE) a virtual craniocaudal 2D mammogram was calculated by the software from MR-M (with and w/o Gadodiamide/Gd) and matched with corresponding XR-M. To quantify REs the geometric center of the lesions in the virtual vs. conventional mammogram were subtracted. The robustness of registration was quantified by registration of X-MRs to both MR-Ms with and w/o Gadodiamide. RESULTS: Image registration was performed successfully for all patients. Overall RE was 8.2mm (1 min after Gd; confidence interval/CI: 2.0-14.4mm, standard deviation/SD: 6.7 mm) vs. 8.9 mm (no Gd; CI: 4.0-13.9 mm, SD: 5.4mm). The mean difference between pre- vs. post-contrast was 0.7 mm (SD: 1.9 mm). CONCLUSION: Image registration of high-field 3.0T MR-mammography with X-ray-mammography is feasible. For this study applying a high-resolution protocol at 3.0T, the registration was robust and the overall registration error was sufficient for clinical application.

A hypersensitive estrogen receptor alpha mutation that alters dynamic protein interactions.

Estrogen receptor alpha (ERalpha) is highly regulated through multiple mechanisms including cell signaling, posttranslational modifications, and protein-protein interactions. We have previously identified a K303R ERalpha mutation within the hinge region of ERalpha. This mutation results in an altered posttranslational regulation and increased in vitro growth in the presence of low estrogen concentrations. We sought to determine if cells expressing this mutant ERalpha would display hypersensitive tumor growth in in vivo athymic ovariectomized nude mice. MCF-7 cells, stably expressing the K303R ERalpha, formed tumors in nude mice faster than cells expressing wild-type ERalpha in the presence of low levels of estrogen. When estrogen was withdrawn, all tumors regressed but half of the K303R ERalpha-expressing tumors became estrogen-independent and regrew. We evaluated potential mechanisms for the observed hypersensitivity. The mutant ERalpha did not demonstrate increased estrogen binding affinity, but did exhibit increased interactions with members of the SRC family of coactivators. The mutant ERalpha demonstrated increased levels and occupancy time on the pS2 promoter. In the presence of the K303R ERalpha, the SRC-3 and p300 coactivators also displayed increased levels and time on the pS2 promoter. The K303R ERalpha has, in part, lost critical negative regulation by the F domain. Collectively, these data demonstrate an important role for the K303R ERalpha mutation in hormonal regulation of tumor growth and estrogen-regulated promoter dynamics in human breast cancer.

Breast tumors that overexpress nuclear metastasis-associated 1 (MTA1) protein have high recurrence risks but enhanced responses to systemic therapies.

Nuclear metastasis-associated 1(MTA1) protein is an estrogen receptor co-repressor that regulates transcription via chromatin remodeling, and MTA1 messenger ribonucleic acid (mRNA) levels are elevated in several kinds of locally advanced and metastatic tumors relative to non-metastatic tumors. Previous studies in our laboratory mapped MTA1 into a region showing significantly lower LOH (loss of heterozygosity) in primary breast cancers with metastases compared to node-negative tumors, suggesting that epigenetic alterations of MTA1 affect metastatic potential. The present study examined immunohistochemical expression of the MTA1 protein in treated and untreated primary human breast cancers to study the relationship between MTA1 expression and clinical outcome. Node-negative tumors that overexpress MTA1 protein had recurrence risks similar to node-positive tumors. In multivariate analysis of untreated node-negative tumors, highest expression of MTA1 was associated with increased relapse risk (hazard ratio (HR)=2.72, p=0.0003 for multivariate analysis). Tamoxifen and/or anthracylcene-based chemotherapies eliminated all MTA1 associations with clinical outcome, suggesting MTA1 overexpression predicts early disease relapse, but sensitizes breast tumors to systemic therapies.

Low levels of estrogen receptor beta protein predict resistance to tamoxifen therapy in breast cancer.

PURPOSE: Breast cancer is a hormone-dependent cancer, and the presence of estrogen receptor alpha (ER-alpha) in tumors is used clinically to predict the likelihood of response to hormonal therapies. The clinical value of the second recently identified ER isoform, called ER-beta, is less clear, and there is currently conflicting data concerning its potential role as a prognostic or predictive factor. EXPERIMENTAL DESIGN: To assess whether ER-beta expression is associated with clinical outcome, protein levels were measured by immunoblot analysis of a retrospective bank of tumor cell lysates from 305 axillary node-positive patients. A total of 119 received no adjuvant therapy, and 186 were treated with tamoxifen only. The median follow-up time was 65 months. Univariate and multivariate Cox regression modeling was done to assess the prognostic and predictive significance of ER-beta expression. RESULTS: Expression of ER-beta protein did not correlate significantly with any other clinical variables, including ER and progesterone levels (as measured ligand binding assay), tumor size, age, or axillary nodal status. In the untreated population, those patients whose tumors who expressed both receptor isoforms exhibited the most favorable outcome as compared with those patients who had lost ER-alpha expression. However, there was no association between ER-beta levels alone and either disease-free or overall survival in the untreated patient population. In contrast, in both univariate and multivariate analyses, high levels of ER-beta predicted an improved disease-free and overall survival in patients treated with adjuvant tamoxifen therapy. CONCLUSIONS: These findings provide evidence that ER-beta may be an independent predictor of response to tamoxifen in breast cancer. Furthermore, these results suggest that ER-beta may influence tumor progression in ways different from those mediated by the ER-alpha isoform.

Breast cancer patients with progesterone receptor PR-A-rich tumors have poorer disease-free survival rates.

PURPOSE: No study has yet analyzed whether changes in relative expression levels of progesterone receptor (PR) isoforms A and B in human breast tumors have significance in predicting clinical outcome. Human PRs are ligand-activated nuclear transcription factors that mediate progesterone action. Their presence in breast tumors is used to predict functional estrogen receptors (ERs) and, therefore, also to predict the likelihood of response to endocrine therapies and disease prognosis. The two PR isoforms, PR-A and PR-B, possess different in vitro and in vivo activities, suggesting that in tumors, the ratio of their expression may control hormone responsiveness. In general, PR-B are strong transcriptional activators, whereas PR-A can act as dominant repressors of PR-B and ER. Thus their balance may affect tamoxifen response in breast cancers. EXPERIMENTAL DESIGN: To determine whether differential expression of the PR isoforms is associated with clinical outcome and hormonal responsiveness, PR-A and PR-B were measured by immunoblot analysis of cell lysates from 297 axillary node-positive breast tumors. RESULTS: Expression of the two isoforms correlated with each other, as well as with ER. Additional analyses revealed that patients with PR-positive tumors but high PR-A:PR-B ratios, which were often caused by high PR-A levels, were 2.76 times more likely to relapse than patients with lower ratios, indicating resistance to tamoxifen. CONCLUSIONS: This study suggests that knowledge of the PR-A:PR-B ratio may identify a subgroup of ER-positive/PR-positive patients with node-positive breast cancer that benefit poorly from endocrine therapy.

Regulation of the estrogen receptor alpha minimal promoter by Sp1, USF-1 and ERalpha.

The exact molecular mechanisms regulating estrogen receptor alpha (ERalpha) expression in breast tumors are unclear, but studies suggest that they are partly at the level of transcription. We have focused on the transcription factors that regulate the ERalpha minimal promoter, which we have previously shown to reside within the first 245 bp of the 5'-flanking region of the gene. Within this region are several elements essential for full ERalpha promoter transcriptional activity, including a GC box and an imperfect E box. In earlier studies we demonstrated an essential function for the Sp1 family of transcription factors in the regulation of ERalpha expression. We have now identified both USF-1 and ERalpha itself as components of a multi-protein complex of transcription factors that interacts at the ERalpha minimal promoter and is essential for its full transcriptional activity. Electrophoretic mobility shift assays demonstrated that Sp1 and USF-1, but not ERalpha, bind directly to the ERalpha minimal promoter. We showed by GST pull-down assays that ERalpha is able to interact in vitro with USF-1, suggesting, in addition to a possible interaction between ERalpha and Sp1, a mechanism whereby ERalpha is able to interact with the protein complex. Combined exogenous expression of the components of the complex in MCF-7 breast cancer cells resulted in a synergistic effect on transactivation of the ERalpha minimal promoter, suggesting that the importance of the protein complex is in the interactions among the components. Based upon these findings, we propose a possible model for transcription from the ERalpha minimal promoter.

Progesterone crosstalks with insulin-like growth factor signaling in breast cancer cells via induction of insulin receptor substrate-2.

Both progesterone and the insulin-like growth factors (IGFs) are critically involved in mammary gland development and also in breast cancer progression. However, how the progesterone and IGF signaling pathways interact with each other to regulate breast cancer cell growth remains unresolved. In this study, we investigated progesterone regulation of IGF signaling components in breast cancer cells. We found that insulin receptor substrate-2 (IRS-2) levels were markedly induced by progesterone and the synthetic progestin R5020 in MCF-7 and other progesterone receptor (PR) positive breast cancer cell lines, whereas IRS-1 and the IGF-I receptor were not induced. The antiprogestin RU486 blocked the R5020 effect on IRS-2 expression. Ectopic expression of either PR-A or PR-B in C4-12 breast cancer cells (estrogen receptor and PR negative) showed that progestin upregulation of IRS-2 was mediated specifically by PR-B. The IRS-2 induction by R5020 occurred via an increase of IRS-2 mRNA levels. Furthermore, progestin treatment prior to IGF-I stimulation resulted in higher tyrosine-phosphorylated IRS-2 levels, increased binding of IRS-2 to Grb-2 and the PI3K regulatory subunit p85, and correspondingly enhanced ERK and Akt activation, as compared with IGF-I-only conditions. Taken together, our data suggest that IRS-2 may play an important role in crosstalk between progesterone and the IGFs in breast cancer cells.

Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer.

BACKGROUND: AIB1 (SRC-3) is an estrogen receptor (ER) coactivator that, when overexpressed in cultured cells, can reduce the antagonist activity of tamoxifen-bound ERs. Signaling through the HER-2 receptor pathway activates AIB1 by phosphorylation. To determine whether high AIB1 expression alone or together with HER-2 reduces the effectiveness of tamoxifen in breast cancer patients, we quantified expression of AIB1 and HER-2 in tumors from breast cancer patients with long-term clinical follow-up who received either no adjuvant therapy or adjuvant tamoxifen therapy after breast cancer surgery. METHODS: AIB1 and HER-2 protein levels in tumors from 316 breast cancer patients were determined using western blot analysis. Molecular variables (e.g., expression of AIB1, ER, progesterone receptor, p53, Bcl-2), tumor characteristics, and patient outcome were assessed using Spearman rank correlation. Disease-free survival (DFS) curves were derived from Kaplan-Meier estimates, and the curves were compared by log-rank tests. The effect of AIB1 on DFS adjusted for other prognostic factors was assessed by multivariable analysis using the Cox proportional hazards model. All statistical tests were two-sided. RESULTS: High AIB1 expression in patients not receiving adjuvant tamoxifen therapy was associated with better prognosis and longer DFS (P =.018, log-rank test). In contrast, for patients who did receive tamoxifen therapy, high AIB1 expression was associated with worse DFS (P =.049, log-rank test), which is indicative of tamoxifen resistance. The test for interaction between AIB1 expression and tamoxifen therapy was statistically significant (P =.004). When expression of AIB1 and HER-2 were considered together, patients whose tumors expressed high levels of both AIB1 and HER-2 had worse outcomes with tamoxifen therapy than all other patients combined (P =.002, log-rank test). CONCLUSIONS: The antitumor activity of tamoxifen in patients with breast cancer may be determined, in part, by tumor levels of AIB1 and HER-2. Thus, AIB1 may be an important diagnostic and therapeutic target.

Androgen receptor acetylation governs trans activation and MEKK1-induced apoptosis without affecting in vitro sumoylation and trans-repression function.

The androgen receptor (AR) is a nuclear hormone receptor superfamily member that conveys both trans repression and ligand-dependent trans-activation function. Activation of the AR by dihydrotestosterone (DHT) regulates diverse physiological functions including secondary sexual differentiation in the male and the induction of apoptosis by the JNK kinase, MEKK1. The AR is posttranslationally modified on lysine residues by acetylation and sumoylation. The histone acetylases p300 and P/CAF directly acetylate the AR in vitro at a conserved KLKK motif. To determine the functional properties governed by AR acetylation, point mutations of the KLKK motif that abrogated acetylation were engineered and examined in vitro and in vivo. The AR acetylation site point mutants showed wild-type trans repression of NF-kappa B, AP-1, and Sp1 activity; wild-type sumoylation in vitro; wild-type ligand binding; and ligand-induced conformational changes. However, acetylation-deficient AR mutants were selectively defective in DHT-induced trans activation of androgen-responsive reporter genes and coactivation by SRC1, Ubc9, TIP60, and p300. The AR acetylation site mutant showed 10-fold increased binding of the N-CoR corepressor compared with the AR wild type in the presence of ligand. Furthermore, histone deacetylase 1 (HDAC1) bound the AR both in vivo and in cultured cells and HDAC1 binding to the AR was disengaged in a DHT-dependent manner. MEKK1 induced AR-dependent apoptosis in prostate cancer cells. The AR acetylation mutant was defective in MEKK1-induced apoptosis, suggesting that the conserved AR acetylation site contributes to a pathway governing prostate cancer cellular survival. As AR lysine residue mutations that abrogate acetylation correlate with enhanced binding of the N-CoR repressor in cultured cells, the conserved AR motif may directly or indirectly regulate ligand-dependent corepressor disengagement and, thereby, ligand-dependent trans activation.