Abstract
We quantify a recent five-category CT histogram based classification of ground glass opacities using a dynamic mathematical model for the spatial-temporal evolution of malignant nodules. Our mathematical model takes the form of a spatially structured partial differential equation with a logistic crowding term. We present the results of extensive simulations and validate our model using patient data obtained from clinical CT images from patients with benign and malignant lesions.
Keywords:
CT scan; Medical imaging; logistic partial differential equation.
Publication types
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Case Reports
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
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Validation Study
MeSH terms
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Adenocarcinoma of Lung / diagnostic imaging
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Adenocarcinoma of Lung / pathology
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Carcinoma, Non-Small-Cell Lung / diagnostic imaging
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Carcinoma, Non-Small-Cell Lung / pathology
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Computer Simulation
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Humans
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Imaging, Three-Dimensional / statistics & numerical data
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Logistic Models
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Longitudinal Studies
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Lung Neoplasms / diagnostic imaging
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Lung Neoplasms / pathology
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Mathematical Concepts
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Models, Biological*
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Models, Statistical
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Multiple Pulmonary Nodules / diagnostic imaging*
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Multiple Pulmonary Nodules / pathology
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Solitary Pulmonary Nodule / diagnostic imaging*
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Solitary Pulmonary Nodule / pathology
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Spatio-Temporal Analysis
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Time Factors
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Tomography, X-Ray Computed / statistics & numerical data*