Predicting Malignant Nodules from Screening CT Scans. Issue 12 (December 2016)

• Record Type: Journal Article
• Authors: Hawkins, Samuel; Wang, Hua; Liu, Ying; Garcia, Alberto; Stringfield, Olya; Krewer, Henry; Li, Qian; Cherezov, Dmitry; Gatenby, Robert A.; Balagurunathan, Yoganand; Goldgof, Dmitry; Schabath, Matthew B.; Hall, Lawrence; Gillies, Robert J.
• Is Part Of: Journal of thoracic oncology. Volume 11:Issue 12(2016)
• Publication Date: 2016-12
• Subjects: Radiomics -- Computed tomography -- Lung cancer -- Screening -- Prediction -- Machine learning; Chest -- Cancer -- Periodicals; Thoracic Neoplasms -- Periodicals; 616.99494005
• DOI: 10.1016/j.jtho.2016.07.002
• Languages: English
• ISSNs: 1556-0864

Abstract

ABSTRACT : 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 predict nodules that would become cancerous 1 and 2 years hence with accuracies of 80% (area under the curve 0.83) and 79% (area under the curve 0.75), respectively. Radiomics outperformed the Lung Imaging Reporting and Data System and volume‐only approaches. The performance of the McWilliams risk assessment model was commensurate. Conclusions: : The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.