Screening for breast cancer has predominantly been done using mammography. Unfortunately, mammograms miss 50% cancers in women with dense breast tissue. Multi-modal screenings offer the best chance of enhancing breast cancer screening effectiveness. We evaluated the use of TAB004, an antibody that recognizes the tumor form of the glycoprotein MUC1 (tMUC1), to aid early detection of breast cancer. Our experimental approach was to follow tMUC1 from the tissue into circulation. We found that 95% of human breast cancer tissues across all subtypes stained positive for TAB004. In breast cancer cell lines, we showed that the amount of tMUC1 released from tumor cells is proportional to the cell's tMUC1 expression level. Finally, we showed that TAB004 can be used to assess circulating tMUC1 levels, which when monitored in the context of cancer immunoediting, can aid earlier diagnosis of breast cancer regardless of breast tissue density. In a blinded pilot study with banked serial samples, tMUC1 levels increased significantly up to 2 years before diagnosis. Inclusion of tMUC1 monitoring as part of a multi-modal screening strategy may lead to earlier stage diagnosis of women whose cancers are missed by mammography.
We develop and implement a compressive reconstruction method for tomographic recovery of refractive index distribution for weakly attenuating objects in a microfocus X-ray system. This is achieved through the development of a discretized operator modeling both the transport of intensity equation and X-ray transform that is suitable for iterative reconstruction techniques.Traditional tomography with hard X-rays recovers the attenuation of an object. Attenuation does not always provide good contrast when imaging objects made of materials with low electron density, e.g. soft tissues. In these cases, richer information is often contained in the phase, i.e. the optical thickness of the sample [1][2][3]. Propagation based techniques are particularly suitable for X-ray phase imaging because they allow phase to be recovered from intensity images taken at multiple propagation distances without the need for optical elements [4,5]. Here we adopt the transport of intensity equation (TIE) which relates the measured intensity to the Laplacian of the phase under a weakly-attenuating sample approximation. Implementing TIE at many angles while rotating the object allows tomographic reconstruction of the refractive index distribution.TIE tomographic reconstruction first requires a suitable forward model, consisting of 1) a projection of refractive index through the sample and 2) modeling diffraction after the sample with the TIE. Recovering the phase then amounts to inverting these operations on the measured data. A straightforward method of reconstruction is to invert the forward model in two steps [7]. For the first step, the TIE can be solved by a Poisson equation solver. The TIE is ill-posed because the transfer function relating the intensity measurement to the phase tends to zero as the spatial frequency decreases. As a result, reconstructions are often corrupted by significant low-frequency noise, requiring regularization. Tikhonov regularization is most commonly employed to reduce these artifacts [7,8]. For the second step, a standard tomographic reconstruction is carried out, e.g. using the filtered back-projection (FBP) method. In order for FBP to yield a result free from high-frequency "streaking" artifacts, projections must be taken at many angles. It is often desirable to use fewer projections in order to reduce dose or acquisition time, in which case the tomographic inversion problem is underdetermined. Rather than solve these two inverse problems independently, the forward and inverse models may be adapted into a single-step op- Fig. 1: Imaging geometry for TIE tomography eration combining TIE and tomography [6,[9][10][11]. However, a single step inversion still requires many projection angles in order to avoid artifacts. Iterative solvers have recently been proposed to reduce these artifacts when attempting a reconstruction from a small number of projections. Myers et. al. propose inversion of TIE tomography measurements to obtain a sample distribution using prior knowledge that the sample consists of a single mat...
In the United States, legislative actions in over 28 states require radiologists to notify women who undergo breast screening mammography of their breast density. This has led to increased public interest in supplemental screening, but radiologists have not come to a consensus on a supplemental screening modality. In choosing between the most common options, whole-breast ultrasonography (US) and magnetic resonance (MR) imaging, one must weigh the benefits and drawbacks of each modality, as increased cancer detection may be accompanied by increased examination costs and biopsy rates. There has been recent interest in molecular breast imaging (MBI) for supplemental screening because of its high sensitivity, as well as its high specificity. This article describes how MBI fits into clinical practice alongside digital breast tomosynthesis (DBT), targeted US, and MR imaging. The authors describe their approach to breast cancer screening, which uses DBT as the primary imaging modality. DBT is complemented by automated density calculations and supplemented with functional imaging techniques, including MR imaging or MBI, for women with dense breasts. An algorithm based on the patient's breast cancer risk is used to determine if either MR imaging or MBI for supplemental screening is appropriate. MBI is also used as a problem-solving tool for the evaluation of clinical indications following complex mammography or US, or for unexplained physical findings. This article describes aspects related to implementing MBI in clinical practice, including the clinical workflow, patient management, radioactive tracer administration, and procedure reimbursement. RSNA, 2017.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.