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Lung Image Databases with Pathologic Correlates
Geoffrey McLennan, M.D.
University of Iowa
Grant Number: U01CA091085
This application is in response to a specific request to establish a generalized CT-derived database representing ground truth in lung cancer and is not hypothesis-driven. Our broad goal is to help in the building of this database, and through that effort assist with the methodical development of appropriate lung cancer screening tools and protocols. Our group, with recognized experience in cooperative national projects, and with a broad perspective, will provide for the consortium: a well characterized group of study subjects with lung cancer, and with common lung cancer mimics such as histoplasmosis, supported by excellent radiologists and pathologists; expertise in the development of CT imaging protocols; a functional electronic transfer system for CT data sets from multiple sites, analysis and archiving of such data sets; expertise in DICOM standards, and in the issuing of web-based reports; methods for temporal matching of CT data points, important in the longitudinal follow-up of patients, and in matching excised inflated lobe data and histopathological data to the original patient CT; expertise in computational morphology, (i.e., the mathematical description of complex structures, their visualization, and their derived CT images).
We intend to apply this to a subset of resected lung tumors to help define pathological and CT ground truth. Image reconstruction algorithms. This is critically important for the identification and implementation of needed improvements in CT methods to maximize the chance of detection of subtle early lesions within the lung parenchyma and airways. Data from two different CT manufacturers multi-slice helical CT scanners. With mathematically derived virtual lung models, including early lung cancer development, for use in design of scanning and reconstruction methods.
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