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

Y. Kawata; N. Niki; H. Ohamatsu; M. Kusumoto; T. Tsuchida; K. Eguchi; M. Kaneko; N. Moriyama

doi:10.1117/12.911229

23 February 2012 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

Y. Kawata, N. Niki, H. Ohamatsu, M. Kusumoto, T. Tsuchida, K. Eguchi, M. Kaneko, N. Moriyama

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83150C (2012) https://doi.org/10.1117/12.911229
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

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Y. Kawata, N. Niki, H. Ohamatsu, M. Kusumoto, T. Tsuchida, K. Eguchi, M. Kaneko, and N. Moriyama "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", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150C (23 February 2012); https://doi.org/10.1117/12.911229

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