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Proceedings Paper

Differential diagnosis of pulmonary nodules using 3D CT images
Author(s): Takeru Kageyama; Yoshiki Kawata; Noboru Niki; Masahiko Kusumoto; Yoshiki Aokage; Genichirou Ishii; Hironobu Ohmatsu; Takaaki Tsuchida; Yuji Matsumoto; Kenji Eguchi; Masahiro Kaneko
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Paper Abstract

Lung cancer CT screening has been carried out. Unnecessary biopsy is performed in 20-55% of cancer candidate cases. Several malignant risk models have been published to reduce the false positive rate of lung cancer. In this study, we develop a high-performance malignant risk model. This risk model consists of Generalized Additive Model (GAM) using diameter, pleural attachment area rate, CT kurtosis, GLCM_Inertia, GLCM_IDM and GLCM_Energy_in_marginal_region. This model shows effectiveness by showing AUC 0.918 compared to the current Pancan model.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142J (16 March 2020);
Show Author Affiliations
Takeru Kageyama, Tokushima Univ. (Japan)
Yoshiki Kawata, Tokushima Univ. (Japan)
Noboru Niki, Tokushima Univ. (Japan)
Masahiko Kusumoto, National Cancer Ctr. Hospital (Japan)
Yoshiki Aokage, National Cancer Ctr. Hospital (Japan)
Genichirou Ishii, National Cancer Ctr. Hospital (Japan)
Hironobu Ohmatsu, Abashiri Prison (Japan)
Takaaki Tsuchida, National Cancer Ctr. Hospital (Japan)
Yuji Matsumoto, National Cancer Ctr. Hospital (Japan)
Kenji Eguchi, Teikyo Univ. (Japan)
Masahiro Kaneko, Tokyo Health Service Association (Japan)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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