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Proceedings Paper

Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection
Author(s): Y. Kawata; N. Niki; H. Ohmatsu; M. Satake; M. Kusumoto; T. Tsuchida; K. Aokage; K. Eguchi; M. Kaneko; N. Moriyama
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Paper Abstract

In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

Paper Details

Date Published: 18 March 2014
PDF: 6 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352N (18 March 2014); doi: 10.1117/12.2043390
Show Author Affiliations
Y. Kawata, Univ. of Tokushima (Japan)
N. Niki, Univ. of Tokushima (Japan)
H. Ohmatsu, National Cancer Ctr. Hospital East (Japan)
M. Satake, National Cancer Ctr. Hospital East (Japan)
M. Kusumoto, National Cancer Ctr. Hospital (Japan)
T. Tsuchida, National Cancer Ctr. Hospital (Japan)
K. Aokage, National Cancer Ctr. Hospital East (Japan)
K. Eguchi, Teikyo Univ. School of Medicine (Japan)
M. Kaneko, Tokyo Health Service Association (Japan)
N. Moriyama, Tokyo Midtown Medical Ctr. (Japan)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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