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J Thorac Oncol. 2016 Dec;11(12):2120-2128. doi: 10.1016/j.jtho.2016.07.002. Epub 2016 Jul 13.

Predicting Malignant Nodules from Screening CT Scans.

Author information

1
Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.
2
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.
3
Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
4
Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
5
Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
6
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.

Erratum in

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.

KEYWORDS:

Computed tomography; Lung cancer; Machine learning; Prediction; Radiomics; Screening

Comment in

PMID:
27422797
PMCID:
PMC5545995
DOI:
10.1016/j.jtho.2016.07.002
[Indexed for MEDLINE]
Free PMC Article

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