Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

Y. Kawata; N. Niki; H. Ohmatsu; M. Satake; M. Kusumoto; T. Tsuchida; K. Aokage; K. Eguchi; M. Kaneko; N. Moriyama

doi:10.1117/12.2043390

18 March 2014 Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

Y. Kawata, N. Niki, H. Ohmatsu, M. Satake, M. Kusumoto, T. Tsuchida, K. Aokage, K. Eguchi, M. Kaneko, N. Moriyama

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90352N (2014) https://doi.org/10.1117/12.2043390
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

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Y. Kawata, N. Niki, H. Ohmatsu, M. Satake, M. Kusumoto, T. Tsuchida, K. Aokage, K. Eguchi, M. Kaneko, and N. Moriyama "Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352N (18 March 2014); https://doi.org/10.1117/12.2043390

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