Medicine and Health Sciences Commons^™

Predicting Malignant Nodules From Screening Ct Scans, Samuel Hawkins, Hua Wang, Ying Liu, Alberto Garcia, Olya Stringfield, Henry Krewer, Qiang Li, Dmitry Cherezov, Matthew Schabath, Lawrence O. Hall, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Objectives

The aim of this study was to determine whether quantitative analyses (“radiomics”) of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.

Methods

Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.

Results

The best models used 23 stable features in a random forests classifier and could …

Differences In Patient Outcomes Of Prevalence, Interval, And Screen-Detected Lung Cancers In The Ct Arm Of The National Lung Screening Trial, Matthew B. Schabath, Pierre P. Massion, Zachary J. Thompson, Steven A. Eschrich, Yoganand Balagurunathan, Dmitry Goldof, Denise R. Aberle, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Lung cancer screening identifies cancers with heterogeneous behaviors. Some lung cancers will be identified among patients who had prior negative CT screens and upon follow-up scans develop a de novo nodule that was determined to be cancerous. Other lung cancers will be identified among patients who had one or more prior stable positive scans that were not determined to be lung cancer (indeterminate pulmonary nodules), but in follow-up scans was diagnosed with an incidence lung cancer. Using data from the CT arm of the National Lung Screening Trial, this analysis investigated differences in patient characteristics and survival endpoints between prevalence-, …