Name
Title | ||
|---|---|---|
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 |
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911229 | Proceedings of SPIE |
Keywords | Field | DocType |
image-based computer-aided prognosis (CAP),lung cancer,recurrent-free survival,variational Bayesian mixture modeling framework,CT histogram | Lung cancer,Histogram,Computer-aided,Image based,Artificial intelligence,Framingham Risk Score,Computer vision,Pattern recognition,Mixture modeling,Bioinformatics,Computing systems,Bayesian probability,Physics | Conference |
Volume | ISSN | Citations |
8315 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiki Kawata | 1 | 192 | 54.44 |
| Noboru Niki | 2 | 188 | 66.10 |
| Hironobu Ohmatsu | 3 | 138 | 45.23 |
| masahiko kusumoto | 4 | 46 | 16.28 |
| takaaki tsuchida | 5 | 0 | 4.39 |
| Kenji Eguchi | 6 | 129 | 42.78 |
| Makoto Kaneko | 7 | 535 | 97.23 |
| Noriyuki Moriyama | 8 | 148 | 50.47 |