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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 1/2016

01-01-2016 | Original Article

Automated pulmonary nodule CT image characterization in lung cancer screening

Authors: Anthony P. Reeves, Yiting Xie, Artit Jirapatnakul

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 1/2016

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Abstract

Purpose

In lung cancer screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed.

Methods

A computer system that incorporates novel 3D image features to determine the malignancy status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and ELCAP lung cancer studies. The system is evaluated with different data subsets to determine the impact of class size distribution imbalance in datasets and to evaluate different training and testing strategies.

Results

Results show a modest improvement in malignancy prediction compared to prediction by size alone for a traditional size-unbalanced dataset. Further, the advantage of size binning for classifier design and the advantages of a size-balanced dataset for both training and testing are demonstrated.

Conclusion

Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.
Appendix
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Metadata
Title
Automated pulmonary nodule CT image characterization in lung cancer screening
Authors
Anthony P. Reeves
Yiting Xie
Artit Jirapatnakul
Publication date
01-01-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 1/2016
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-015-1245-7

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