Differential diagnosis of pulmonary nodules using 3D CT images

Takeru Kageyama; Yoshiki Kawata; Noboru Niki; Masahiko Kusumoto; Yoshiki Aokage; Genichirou Ishii; Hironobu Ohmatsu; Takaaki Tsuchida; Yuji Matsumoto; Kenji Eguchi; Masahiro Kaneko

doi:10.1117/12.2551139

16 March 2020 Differential diagnosis of pulmonary nodules using 3D CT images

Takeru Kageyama, Yoshiki Kawata, Noboru Niki, Masahiko Kusumoto, Yoshiki Aokage, Genichirou Ishii, Hironobu Ohmatsu, Takaaki Tsuchida, Yuji Matsumoto, Kenji Eguchi, Masahiro Kaneko

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142J (2020) https://doi.org/10.1117/12.2551139
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Takeru Kageyama, Yoshiki Kawata, Noboru Niki, Masahiko Kusumoto, Yoshiki Aokage, Genichirou Ishii, Hironobu Ohmatsu, Takaaki Tsuchida, Yuji Matsumoto, Kenji Eguchi, and Masahiro Kaneko "Differential diagnosis of pulmonary nodules using 3D CT images", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142J (16 March 2020); https://doi.org/10.1117/12.2551139

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