Exploration of the volumetric composition of human lung cancer nodules in correlated histopathology and computed tomography

Abstract

Gaining a complete and comprehensive understanding of lung cancer nodule histological compositions and how these tissues are represented in radiological data is important not only for expanding the current knowledge base of cancer growth and development but also has potential implications for classification standards, radiological diagnosis methods and for the evaluation of treatment response. In this study we generate large scale histological segmentations of the cancerous and non-cancerous tissues within resected lung nodules. We have implemented a processing pipeline which allows for the direct correlation between histological data and spatially corresponding computed tomography data. Utilizing these correlated datasets we evaluated the statistical separation between Hounsfield Unit (HU) histogram values for each tissue type. The findings of this study revealed that lung cancer nodules contain a complex intermixing of cellular tissue types and that trends exist in the relationship between these tissue types. It was found that the mean Hounsfield Unit values for isolated lung cancer nodules imaged with computed tomography, had statistically significantly different values for non-solid bronchoalveolar carcinoma, solid cancerous tumor, blood, and inactive fibrotic stromal tissue.

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“…f ). Interestingly, this pattern is similar to the fibrosis areas previously described in human lung cancer …”

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“…For example, in a study looking at lung cancer, the investigators were able to create regional histopathologic maps using CT features (HU histogram) to identify things like inactive fibrosis, active fibrosis, necrosis, red blood cells and neoplastic cells. In a liver that has a combination of processes such as inflammation and fibrosis, such maps may be useful in determining the predominant process, targeting biopsy, and prioritizing treatment [39].…”

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