Document Type

Article

Publication Date

12-2016

Keywords

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

Digital Object Identifier (DOI)

Radiomics Computed tomography Lung cancer Screening Prediction Machine learning

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.

Rights Information

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Journal of Thoracic Oncology, v. 11, issue 12, p. 2120-2128

This article is the post-print author version.

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