Document Type


Date of Degree


Degree Name

PhD (Doctor of Philosophy)

Degree In

Biomedical Engineering

First Advisor

McLennan, Geoffrey

First Committee Member

Reinhardt, Joseph M

Second Committee Member

Hoffman, Eric A

Third Committee Member

Sonka, Milan

Fourth Committee Member

DeYoung, Barry

Fifth Committee Member

Cohen, Michael B


Over 190,000 Americans die every year from lung cancer, making it the number one cause of death from cancer in America. Lung cancer has maintained the same low five year survival rate, of 13-15%, over the last thirty years. There is therefore desperate need for improvement in diagnostic and therapeutic techniques for lung cancer. Multidetector computed tomography (MDCT) is being increasingly used for lung cancer detection and characterization. While national lung cancer screening trials have shown MDCT to be effective in detecting even very small lung nodules, the characterization achievable through this modality is poor. The majority of non-small cell lung cancer tumors are histologically heterogeneous and consist of malignant tumor cells, necrotic tumor cells, fibroblastic stromal tissue, and inflammation, however the extent of this heterogeneity is unknown. Geometric and tissue density heterogeneity are under utilized in MDCT representations of lung tumors for distinguishing between malignant and benign nodules because there has been no thorough investigation into the correlation between radiographic heterogeneity and corresponding histological content in 3D. To understand and to make more effective this lung cancer characterization by MDCT, two vital steps must be taken. Firstly, an understanding of the 3D structure and content of tissue types that constitute a lung nodule must be established. Secondly, this knowledge must then be used to assess how nodule tissue content corresponds to the heterogeneity apparent in MDCT data, impacting diagnosis, planning biopsy procedures and nodule change analysis.

In this study we have developed a process model for establishing a direct correlation between histopathology and non-destructive radiological imaging. We provide the 3D structural and pathological detail of lung cancer nodules and surrounding tissues using a purpose built Large Image Microscope Array (LIMA). This information served as the basis for registration of MDCT images of the human nodule before and after resection, computed micro-tomography (micro-CT) detail and histopathology.


biomass, classification, computed tomography, heterogeneity, immunohistochemistry, lung cancer


xi, 140 pages


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Copyright © 2008 Jessica Corinne De Ryk