1,721,016 research outputs found
Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma
No full textPancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone, is limited due to high correlation among features and the multiple testing problem. Deep learning architectures, such as Convolutional Neural networks (CNNs) have been shown to outperform traditional feature-based approaches in computer vision tasks such as object detection. Nonetheless, they require large sample sizes for training which limits their application in medical imaging. As an alternative solution, CNN-based transfer learning has shown potential for achieving reasonable performance using datasets with small sample sizes. In this work, we developed a CNN-based deep radiomics pipeline based on transfer learning, which outperforms the traditional radiomics model in resectable PDAC prognostication.M.Sc
Multi-parametric Magnetic Resonance Imaging (MRI) in Prostate Cancer
Full text linkProstate cancer is extremely prevalent, with shifting patient demographics leading to an increasing number of men balancing treatment efficacy with associated side-effects. Non-invasive characterization of disease – useful for guiding biopsy, to monitor disease progression during active surveillance, or for treatment planning of focal therapies – could have a significant impact on patient management. Through its excellent anatomic imaging capabilities and its ability to characterize physiologic properties, magnetic resonance imaging (MRI) has the potential to fulfill clinical goals; however, further improvements are necessary to maximize accuracy and impact. Thus, this thesis presents: 1) the development of a multi-parametric model to combine parameters derived from measurement of T2 relaxation, diffusion weighted imaging, and dynamic contrast-enhanced MRI to improve the discrimination between normal and malignant peripheral zone tissue; 2) determination of the impact that the presence of normal tissue within regions of tumour has on the measurement of apparent diffusion coefficient (ADC) and T2 relaxation in the peripheral zone; and 3) relationships between MRI measurement and underlying prostate tissue composition. A common patient cohort was used for all studies, with prostate cancer patients having in vivo MRI prior to prostatectomy followed by whole-mount histologic sectioning of the surgical specimens, facilitating the use of pathology as a gold-standard for all analyses. In the first study, the optimal multi-parametric model combines ADC, T2, and volume transfer constant (Ktrans) to yield the probability of malignancy for each voxel. Performance of the model is better than each single parameter, but not significantly so compared to ADC. The second study demonstrates that there is no difference in ADC and T2 between tumours containing significant portions of normal tissue and the surrounding normal tissue itself, indicating that full characterization of prostate cancer with MRI may be limited. Finally, by determining relationships between MRI parameters and tissue characteristics, the third study suggests mechanisms driving MR image appearance in the prostate, including the visualization of cancer. Taken together, this thesis presents potential improvements to prostate cancer imaging, and provides further insight into the interplay between the underlying histology and MRI.Ph
Multi-parametric Magnetic Resonance Imaging (MRI) in Prostate Cancer
No full textProstate cancer is extremely prevalent, with shifting patient demographics leading to an increasing number of men balancing treatment efficacy with associated side-effects. Non-invasive characterization of disease – useful for guiding biopsy, to monitor disease progression during active surveillance, or for treatment planning of focal therapies – could have a significant impact on patient management. Through its excellent anatomic imaging capabilities and its ability to characterize physiologic properties, magnetic resonance imaging (MRI) has the potential to fulfill clinical goals; however, further improvements are necessary to maximize accuracy and impact. Thus, this thesis presents: 1) the development of a multi-parametric model to combine parameters derived from measurement of T2 relaxation, diffusion weighted imaging, and dynamic contrast-enhanced MRI to improve the discrimination between normal and malignant peripheral zone tissue; 2) determination of the impact that the presence of normal tissue within regions of tumour has on the measurement of apparent diffusion coefficient (ADC) and T2 relaxation in the peripheral zone; and 3) relationships between MRI measurement and underlying prostate tissue composition. A common patient cohort was used for all studies, with prostate cancer patients having in vivo MRI prior to prostatectomy followed by whole-mount histologic sectioning of the surgical specimens, facilitating the use of pathology as a gold-standard for all analyses. In the first study, the optimal multi-parametric model combines ADC, T2, and volume transfer constant (Ktrans) to yield the probability of malignancy for each voxel. Performance of the model is better than each single parameter, but not significantly so compared to ADC. The second study demonstrates that there is no difference in ADC and T2 between tumours containing significant portions of normal tissue and the surrounding normal tissue itself, indicating that full characterization of prostate cancer with MRI may be limited. Finally, by determining relationships between MRI parameters and tissue characteristics, the third study suggests mechanisms driving MR image appearance in the prostate, including the visualization of cancer. Taken together, this thesis presents potential improvements to prostate cancer imaging, and provides further insight into the interplay between the underlying histology and MRI.Ph
Deep Radiomics Analytics Pipeline for Prognosis of Pancreatic Ductal Adenocarcinoma
Full text linkPancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone, is limited due to high correlation among features and the multiple testing problem. Deep learning architectures, such as Convolutional Neural networks (CNNs) have been shown to outperform traditional feature-based approaches in computer vision tasks such as object detection. Nonetheless, they require large sample sizes for training which limits their application in medical imaging. As an alternative solution, CNN-based transfer learning has shown potential for achieving reasonable performance using datasets with small sample sizes. In this work, we developed a CNN-based deep radiomics pipeline based on transfer learning, which outperforms the traditional radiomics model in resectable PDAC prognostication.M.Sc
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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