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Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods.The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with...
Symptom Cluster Research is a major topic in Cancer Symptom Science. In spite of the several statistical and clinical approaches in this domain, there is not a consensus on which method performs better. Identifying a generally accepted analytical method is important in order to be able to utilize and process all the available data. In this paper we report a secondary analysis on cancer symptom data,...
Automatic pulmonary nodule detection in Computer Tomographic (CT) images is a challenge task for the Computer Aided Diagnosis (CAD) systems. This paper proposes a novel nodule enhancement filter (Homocentric Squares Filter) for automatic nodule detection based on CT characteristic and shape feature. First, the bright regions in the image are enhanced by calculating the CT value variation between an...
Nodule detection is a vital step in computer-aided diagnosis (CAD) system, for its accuracy can greatly influence the reliability of the system. Many researchers have proposed lots of effective filters to detect nodules. However, these filters have limitations, such as changing the nodules shapes and sizes. This paper proposes a modified Difference of Gaussian (DoG) detector for pulmonary nodule location...
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