Lung cancer survival prediction from pathological images and genetic data - An integration study

Xinliang Zhu, Jiawen Yao, Xin Luo, Guanghua Xiao, Yang Xie, Adi Gazdar, Junzhou Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

In this paper, we have proposed a framework for lung cancer survival prediction by integrating genetic data and pathological images. Since molecular profiles and pathological images reveal complementary information on tumor characteristics, the integration will benefit the survival analysis. The gene expression signatures are processed using Model-Based Background Correction method. A robust cell detection and segmentation method is applied to segment each individual cell from pathological images to extract the image features. Based on the cell detection results, a set of extensive features are extracted using efficient geometry and texture descriptors. The supervised principal component regression model is fitted to evaluate the proposed framework. Experimental results demonstrate strong prediction power of the statistical model built from the integration of genetic data and pathological images compared with using only one of the two types of data alone.

Original languageEnglish (US)
Title of host publication2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE Computer Society
Pages1173-1176
Number of pages4
Volume2016-June
ISBN (Electronic)9781479923502
DOIs
StatePublished - Jun 15 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: Apr 13 2016Apr 16 2016

Other

Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
CountryCzech Republic
CityPrague
Period4/13/164/16/16

Keywords

  • Genetic Data
  • Integration Framework
  • Lung cancer
  • Pathological Image
  • Survival prediction

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Zhu, X., Yao, J., Luo, X., Xiao, G., Xie, Y., Gazdar, A., & Huang, J. (2016). Lung cancer survival prediction from pathological images and genetic data - An integration study. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (Vol. 2016-June, pp. 1173-1176). [7493475] IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493475