Novel image markers for non-small cell lung cancer classification and survival prediction

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• 作者：Hongyuan Wang (1)
  Fuyong Xing (2) (3)
  Hai Su (4)
  Arnold Stromberg (1)
  Lin Yang (2)
  
  1. Department of Statistics ; University of Kentucky ; 725 Rose Street ; 40536 ; Lexington ; KY ; USA
  2. J. Crayton Pruitt Family Department of Biomedical Engineering ; University of Florida ; 1275 Center Drive ; 32611 ; Gainesville ; FL ; USA
  3. Department of Electrical and Computer Engineering ; University of Florida ; 233 Larsen Hall ; 32611 ; Gainesville ; FL ; USA
  4. Department of Computer Science ; University of Kentucky ; 329 Rose Street ; 40536 ; Lexington ; KY ; USA
  
• 关键词：Lung cancer ; Segmentation ; Classification ; Image informatics ; Survival analysis
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：1,108 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

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.