BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.

RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.

CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers.

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Published in BMC Bioinformatics, v. 15, article 310, p. 1-12.

© Wang et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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This research is funded by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number 2 P20 GM103436-14. The project is also partially supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR000117 (or TL1 TR000115 or KL2 TR000116).