Imaging Biomarker Discovery for Lung Cancer Survival Prediction View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Jiawen Yao , Sheng Wang , Xinliang Zhu , Junzhou Huang

ABSTRACT

Solid tumors are heterogeneous tissues composed of a mixture of cells and have special tissue architectures. However, cellular heterogeneity, the differences in cell types are generally not reflected in molecular profilers or in recent histopathological image-based analysis of lung cancer, rendering such information underused. This paper presents the development of a computational approach in H&E stained pathological images to quantitatively describe cellular heterogeneity from different types of cells. In our work, a deep learning approach was first used for cell subtype classification. Then we introduced a set of quantitative features to describe cellular information. Several feature selection methods were used to discover significant imaging biomarkers for survival prediction. These discovered imaging biomarkers are consistent with pathological and biological evidence. Experimental results on two lung cancer data sets demonstrated that survival models bsuilt from the clinical imaging biomarkers have better prediction power than state-of-the-art methods using molecular profiling data and traditional imaging biomarkers. More... »

PAGES

649-657

References to SciGraph publications

Book

TITLE

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

ISBN

978-3-319-46722-1
978-3-319-46723-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46723-8_75

DOI

http://dx.doi.org/10.1007/978-3-319-46723-8_75

DIMENSIONS

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