Koichiro Yasaka1, Hiroyuki Akai1, Masanori Nojima2, Aya Shinozaki-Ushiku3, Masashi Fukayama3, Jun Nakajima4, Kuni Ohtomo5, Shigeru Kiryu6. 1. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. 2. Center for Translational Research, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. 3. Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 4. Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyok, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. 5. Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. 6. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. Electronic address: kiryu-tky@umin.ac.jp.
Abstract
OBJECTIVES: To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis. MATERIALS AND METHODS: The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale. RESULTS: The mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p≤0.018). CONCLUSION: Using CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.
OBJECTIVES: To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis. MATERIALS AND METHODS: The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale. RESULTS: The mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p≤0.018). CONCLUSION: Using CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.
Authors: Jose Arimateia Batista Araujo-Filho; Maria Mayoral; Junting Zheng; Kay See Tan; Peter Gibbs; Annemarie Fernandes Shepherd; Andreas Rimner; Charles B Simone; Gregory Riely; James Huang; Michelle S Ginsberg Journal: Ann Thorac Surg Date: 2021-04-09 Impact factor: 5.102
Authors: K Martini; B Baessler; M Bogowicz; C Blüthgen; M Mannil; S Tanadini-Lang; J Schniering; B Maurer; T Frauenfelder Journal: Eur Radiol Date: 2020-10-06 Impact factor: 5.315