The Experts below are selected from a list of 5469 Experts worldwide ranked by ideXlab platform
Mehdi Moradi - One of the best experts on this subject based on the ideXlab platform.
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ultrasound rf time series for classification of breast lesions
IEEE Transactions on Medical Imaging, 2015Co-Authors: Nishant Uniyal, Purang Abolmaesumi, Hani Eskandari, Samira Sojoudi, Paula B Gordon, Linda Warren, Robert Rohling, Septimiu E Salcudean, Mehdi MoradiAbstract:This work reports the use of ultrasound radio frequency (RF) time series analysis as a method for ultrasound-based classification of malignant breast lesions. The RF time series method is versatile and requires only a few seconds of raw ultrasound data with no need for additional instrumentation. Using the RF time series features, and a machine learning framework, we have generated malignancy maps, from the estimated cancer likelihood, for decision support in biopsy recommendation. These maps depict the likelihood of malignancy for regions of size $1~{\hbox {mm}}^{2}$ within the suspicious lesions. We report an area under receiver operating characteristics curve of 0.86 (95% confidence interval [CI]: 0.84%–0.90%) using support vector machines and 0.81 (95% CI: 0.78–0.85) using Random Forests classification algorithms, on 22 subjects with leave-one-subject-out cross-validation. Changing the classification method yielded consistent results which indicates the robustness of this Tissue Typing method. The findings of this report suggest that ultrasound RF time series, along with the developed machine learning framework, can help in differentiating malignant from benign breast lesions, subsequently reducing the number of unnecessary biopsies after mammography screening.
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Tissue Typing using ultrasound rf time series experiments with animal Tissue samples
Medical Physics, 2010Co-Authors: Mehdi Moradi, Purang Abolmaesumi, Parvin MousaviAbstract:Purpose: This article provides experimental evidence to show that the time series of radiofrequency (RF) ultrasound data can be used for Tissue Typing. It also explores the Tissue Typing information in RF time series. Clinical and high-frequency ultrasound are studied. Methods: Bovine liver, pig liver, bovine muscle, and chicken breast were used in the experiments as the animal Tissue types. In the proposed approach, the authors record RF echo signals backscattered from Tissue, while the imaging probe and the Tissue are stationary. This sequence of recorded RF data generates a time series of RF echoes for each spatial sample of the RF signal. The authors use spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with feedforward neural networks for Tissue Typing. The experiments are repeated at ultrasound frequency of 6.6 and also 55 MHz. The effects of increasing power and frame rate are studied. Results: The methodology yielded an average two-class classification accuracy of 95.1% when ultrasound data were acquired at 6.6 MHz and 98.1% when data were collected with a high-frequency probe operating at 55 MHz. In four-class classification experiments, the recorded accuracies were 78.6% and 86.5% for low and high-frequency ultrasound data, respectively. A set of 12 texture features extracted from the B-mode image equivalents of the RF data yields an accuracy of only 77.5% in Typing the analyzed Tissues. An increase in acoustic power and the frame rate of ultrasound results in an improvement in classification results. Conclusions: The results of this study demonstrate that RF time series can be used for ultrasound-based Tissue Typing. Further investigation of the underlying physical mechanisms is necessary.
Parvin Mousavi - One of the best experts on this subject based on the ideXlab platform.
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Tissue Typing using ultrasound rf time series experiments with animal Tissue samples
Medical Physics, 2010Co-Authors: Mehdi Moradi, Purang Abolmaesumi, Parvin MousaviAbstract:Purpose: This article provides experimental evidence to show that the time series of radiofrequency (RF) ultrasound data can be used for Tissue Typing. It also explores the Tissue Typing information in RF time series. Clinical and high-frequency ultrasound are studied. Methods: Bovine liver, pig liver, bovine muscle, and chicken breast were used in the experiments as the animal Tissue types. In the proposed approach, the authors record RF echo signals backscattered from Tissue, while the imaging probe and the Tissue are stationary. This sequence of recorded RF data generates a time series of RF echoes for each spatial sample of the RF signal. The authors use spectral and fractal features of ultrasound RF time series averaged over a region of interest, along with feedforward neural networks for Tissue Typing. The experiments are repeated at ultrasound frequency of 6.6 and also 55 MHz. The effects of increasing power and frame rate are studied. Results: The methodology yielded an average two-class classification accuracy of 95.1% when ultrasound data were acquired at 6.6 MHz and 98.1% when data were collected with a high-frequency probe operating at 55 MHz. In four-class classification experiments, the recorded accuracies were 78.6% and 86.5% for low and high-frequency ultrasound data, respectively. A set of 12 texture features extracted from the B-mode image equivalents of the RF data yields an accuracy of only 77.5% in Typing the analyzed Tissues. An increase in acoustic power and the frame rate of ultrasound results in an improvement in classification results. Conclusions: The results of this study demonstrate that RF time series can be used for ultrasound-based Tissue Typing. Further investigation of the underlying physical mechanisms is necessary.
J A Hansen - One of the best experts on this subject based on the ideXlab platform.
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Tissue Typing in support of unrelated hematopoietic cell transplantation
Tissue Antigens, 2003Co-Authors: Effie W Petersdorf, Claudio Anasetti, Paul J Martin, J A HansenAbstract:The success of unrelated hematopoietic cell transplantation (HCT) for the treatment of hematologic malignancies has closely paralleled development of robust Typing methods for comprehensive and precise donor-recipient matching. The application of molecular methods in clinical research has led to a more complete understanding of the immunogenetic barriers involving host-vs-graft (HVG) and graft-vs-host (GVH) reactions. Along with the development of less toxic transplant regimens, advances in the prevention and treatment of graft-vs-host disease (GVHD) and in the supportive care of the transplant recipient, improved HLA matching of potential unrelated donors has led to clinical results that begin to compare favorably with that of HLA-identical sibling transplants.
Paul J Hauptman - One of the best experts on this subject based on the ideXlab platform.
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panel reactive antibody screening practices prior to heart transplantation
Journal of Heart and Lung Transplantation, 2001Co-Authors: Adam S Betkowski, Ralph J Graff, John J Chen, Paul J HauptmanAbstract:Abstract Background Evaluation of humoral sensitization, commonly determined by the panel-reactive antibody (PRA) screen, is accepted as an important part of pre-transplant assessment. A variety of definitions and approaches to sensitization have been described in the literature but no analyses of actual practice have been reported. Methods We sent surveys to 108 adult heart transplant program directors and 20 Tissue-Typing laboratories to obtain information about their approaches to PRA and crossmatch determination and management of sensitized patients. Results Among 65 responding directors (60%), 63.1% were cardiologists and 36.9% surgeons. The most common threshold to consider PRA as positive is ≥10%. Fifty-five of the respondents consider reactivity with T or B lymphocytes to be significant, whereas 34% consider only T-lymphocyte reactivity. Timing of PRA determination varies considerably among programs. Conversion to positive PRA results in more frequent PRA assessments and often therapy aimed to decrease the degree of sensitization. The most commonly utilized approaches are administration of immunoglobulin and plasmapheresis. The complement-dependent cytotoxicity (CDC) assay is the most commonly used method for PRA determination, but other techniques including flow cytometry and enzyme-linked immunosorbent assay (ELISA) are also used. Crossmatches are performed utilizing CDC and flow cytometry methods. Many laboratories employ more than one technique. Conclusions PRA screening, crossmatch determinations and management of sensitized patients vary considerably from center to center. Uncertainty exists about the importance of PRA values, threshold for treatment and clinical implications of sensitization. Important questions about the impact of sensitization on outcomes following heart transplantation may not be resolved until the measurement and management of sensitization becomes more uniform.
Matthew P Sypek - One of the best experts on this subject based on the ideXlab platform.
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hla epitope matching in kidney transplantation an overview for the general nephrologist
American Journal of Kidney Diseases, 2017Co-Authors: Matthew P Sypek, Joshua Kausman, Steve Holt, Peter HughesAbstract:Rapid changes in Tissue-Typing technology, including the widespread availability of highly specific molecular Typing methods and solid-phase assays for the detection of allele-specific anti-HLA antibodies, make it increasingly challenging to remain up to date with developments in organ matching. Terms such as epitopes and eplets abound in the transplantation literature, but often it can be difficult to see what they might mean for the patient awaiting transplantation. In this review, we provide the historical context for current practice in Tissue Typing and explore the potential role of HLA epitopes in kidney transplantation. Despite impressive gains in preventing and managing T-cell–mediated rejection and the associated improvements in graft survival, the challenge of the humoral alloresponse remains largely unmet and is the major cause of late graft loss. Describing HLA antigens as a series of antibody targets, or epitopes, rather than based on broad seroreactivity patterns or precise amino acid sequences may provide a more practical and clinically relevant system to help avoid antibody-mediated rejection, reduce sensitization, and select the most appropriate organs in the setting of pre-existing alloantibodies. We explain the systems proposed to define HLA epitopes, summarize the evidence to date for their role in transplantation, and explore the potential benefits of incorporating HLA epitopes into clinical practice as this field continues to evolve toward everyday practice.