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Novel image markers for non-small cell lung cancer classification and survival prediction
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 plannin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287550/ https://www.ncbi.nlm.nih.gov/pubmed/25240495 http://dx.doi.org/10.1186/1471-2105-15-310 |
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author | Wang, Hongyuan Xing, Fuyong Su, Hai Stromberg, Arnold Yang, Lin |
author_facet | Wang, Hongyuan Xing, Fuyong Su, Hai Stromberg, Arnold Yang, Lin |
author_sort | Wang, Hongyuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4287550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42875502015-01-10 Novel image markers for non-small cell lung cancer classification and survival prediction Wang, Hongyuan Xing, Fuyong Su, Hai Stromberg, Arnold Yang, Lin BMC Bioinformatics Research Article 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. BioMed Central 2014-09-19 /pmc/articles/PMC4287550/ /pubmed/25240495 http://dx.doi.org/10.1186/1471-2105-15-310 Text en © 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. |
spellingShingle | Research Article Wang, Hongyuan Xing, Fuyong Su, Hai Stromberg, Arnold Yang, Lin Novel image markers for non-small cell lung cancer classification and survival prediction |
title | Novel image markers for non-small cell lung cancer classification and survival prediction |
title_full | Novel image markers for non-small cell lung cancer classification and survival prediction |
title_fullStr | Novel image markers for non-small cell lung cancer classification and survival prediction |
title_full_unstemmed | Novel image markers for non-small cell lung cancer classification and survival prediction |
title_short | Novel image markers for non-small cell lung cancer classification and survival prediction |
title_sort | novel image markers for non-small cell lung cancer classification and survival prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287550/ https://www.ncbi.nlm.nih.gov/pubmed/25240495 http://dx.doi.org/10.1186/1471-2105-15-310 |
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