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Proceedings Paper

Nonlinear dimensionality reduction of CT histogram based feature space for predicting recurrence-free survival in non-small-cell lung cancer
Author(s): Y. Kawata; N. Niki; H. Ohmatsu; K. Aokage; M. Kusumoto; T. Tsuchida; K. Eguchi; M. Kaneko
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Paper Abstract

Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. In the recent release of positive results from the National Lung Screening Trial (NLST) in the US showing that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of RFS for patients with NSCLC.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141N (20 March 2015); doi: 10.1117/12.2081719
Show Author Affiliations
Y. Kawata, The Univ. of Tokushima (Japan)
N. Niki, The Univ. of Tokushima (Japan)
H. Ohmatsu, National Cancer Ctr. Hospital East (Japan)
K. Aokage, National Cancer Ctr. Hospital East (Japan)
M. Kusumoto, National Cancer Ctr. Hospital East (Japan)
T. Tsuchida, National Cancer Ctr. Hospital (Japan)
K. Eguchi, Teikyo Univ. School of Medicine (Japan)
M. Kaneko, Tokyo Health Service Association (Japan)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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