title: Predicting Malignant Nodules from Screening CT Scans.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.
authors.affiliation: Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Samuel
authors.initials: S
authors.lastname: Hawkins
authors.affiliation: Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Hua
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Alberto
authors.initials: A
authors.lastname: Garcia
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Olya
authors.initials: O
authors.lastname: Stringfield
authors.affiliation: Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Henry
authors.initials: H
authors.lastname: Krewer
authors.affiliation: Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Qian
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Dmitry
authors.initials: D
authors.lastname: Cherezov
authors.affiliation: Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Robert A
authors.initials: RA
authors.lastname: Gatenby
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Yoganand
authors.initials: Y
authors.lastname: Balagurunathan
authors.affiliation: Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Dmitry
authors.initials: D
authors.lastname: Goldgof
authors.affiliation: Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Matthew B
authors.initials: MB
authors.lastname: Schabath
authors.affiliation: Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Hall
authors.affiliation: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. Electronic address: Robert.Gillies@Moffitt.org.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Gillies
mesh_terms: D000368:Aged; D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computed tomography; Lung cancer; Machine learning; Prediction; Radiomics; Screening