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Proceedings Paper

Image-based computer-aided prognosis of lung cancer: predicting patient recurrent-free survival via a variational Bayesian mixture modeling framework for cluster analysis of CT histograms
Author(s): Y. Kawata; N. Niki; H. Ohamatsu; M. Kusumoto; T. Tsuchida; K. Eguchi; M. Kaneko; N. Moriyama
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Paper Abstract

In this paper, we present a computer-aided prognosis (CAP) scheme that utilizes quantitatively derived image information to predict patient recurrent-free survival for lung cancers. Our scheme involves analyzing CT histograms to evaluate the volumetric distribution of CT values within pulmonary nodules. A variational Bayesian mixture modeling framework translates the image-derived features into an image-based risk score for predicting the patient recurrence-free survival. Using our dataset of 454 patients with NSCLC, we demonstrate the potential usefulness of the CAP scheme which can provide a quantitative risk score that is strongly correlated with prognostic factors.

Paper Details

Date Published: 23 February 2012
PDF: 8 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150C (23 February 2012); doi: 10.1117/12.911229
Show Author Affiliations
Y. Kawata, Univ. of Tokushima (Japan)
N. Niki, Univ. of Tokushima (Japan)
H. Ohamatsu, National Cancer Ctr. Hospital East (Japan)
M. Kusumoto, National Cancer Ctr. Hospital (Japan)
T. Tsuchida, National Cancer Ctr. Hospital (Japan)
K. Eguchi, Teikyo Univ. School of Medicine (Japan)
M. Kaneko, Tokyo Health Service Association (Japan)
N. Moriyama, National Cancer Ctr. Hospital East (Japan)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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