Computed tomography (CT) imagery is an important weapon in the fight against lung cancer; various forms of lung cancer are routinely diagnosed from CT imagery. The growth of the suspect nodule is known to be a prognostic factor in the diagnosis of pulmonary cancer, but the change in other aspects of the nodule, such as its aspect ratio, density, spiculation, or other features usable for machine learning, may also provide prognostic information.
We hypothesized that adding combined feature information from multiple CT image sets separated in time could provide a more accurate determination of nodule malignancy. To this end, we combined data from multiple CT images for individual patients taken from the National Lung Screening Trial. The resulting dataset was compared to equivalent datasets featuring single CT images for each patient. Feature reduction and normalization was performed as is standard.
The highest accuracy achieved was 83.71% on a subset of features chosen by a combination of manual feature stability testing and the Correlation-based Feature Selection algorithm and classified by the Random Forests algorithm. The highest accuracy achieved with individual CT images was 81.00%, on a feature set consisting solely of the volume of the nodule in cubic centimeters.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-8702 |
| Date | 07 December 2018 |
| Creators | Geiger, Benjamin |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
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