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Your search keyword '"Mimura, Hidefumi"' showing total 441 results
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441 results on '"Mimura, Hidefumi"'

3. Percutaneous ultrasound-guided cryoablation for early-stage primary breast cancer: a follow-up study in Japan

Author

Kawamoto, Hisanori, Tsugawa, Koichiro, Furuya, Yuko, Sakamaki, Kaori, Kakimoto, Sayoko, Kitajima, Mina, Takishita, Mariko Nagai, Tazo, Mizuho, Nakano, Mari Hara, Kuroda, Takako, Shimo, Ayaka, Shimo, Arata, Kojima, Yasuyuki, Tsuzuki, Makiko, Motoyoshi, Ai, Haku, Ei, Nishikawa, Toru, Kanemaki, Yoshihide, Mimura, Hidefumi, and Fukuda, Mamoru

Published
2024
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13. Impact of Work Environment on Job Satisfaction among Interventional Radiologists in Japan: A Cross-sectional Study

Author

Sone, Miyuki, primary, Yasunaga, Hideo, additional, Osawa, Marie, additional, Takeguchi, Yuko, additional, Han, Alisa, additional, Akiyama, Naoko, additional, Kamiya, Mika, additional, Woodhams, Reiko, additional, Yoshimatsu, Rika, additional, Nakatsuka, Atsuhiro, additional, Kakeda, Shingo, additional, Takase, Kei, additional, Mimura, Hidefumi, additional, and Yamakado, Koichiro, additional

Published
2024
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16. Japanese clinical practice guidelines for vascular anomalies 2017

Author

Mimura, Hidefumi, Akita, Sadanori, Fujino, Akihiro, Jinnin, Masatoshi, Ozaki, Mine, Osuga, Keigo, Nakaoka, Hiroki, Morii, Eiichi, Kuramochi, Akira, Aoki, Yoko, Arai, Yasunori, Aramaki, Noriko, Inoue, Masanori, Iwashina, Yuki, Iwanaka, Tadashi, Ueno, Shigeru, Umezawa, Akihiro, Ozeki, Michio, Ochi, Junko, Kinoshita, Yoshiaki, Kurita, Masakazu, Seike, Shien, Takakura, Nobuyuki, Takahashi, Masataka, Tachibana, Takao, Chuman, Kumiko, Nagata, Shuji, Narushima, Mitsunaga, Niimi, Yasunari, Nosaka, Shunsuke, Nozaki, Taiki, Hashimoto, Kazuki, Hayashi, Ayato, Hirakawa, Satoshi, Fujikawa, Atsuko, Hori, Yumiko, Matsuoka, Kentaro, Mori, Hideki, Yamamoto, Yuki, Yuzuriha, Shunsuke, Rikihisa, Naoaki, Watanabe, Shoji, Watanabe, Shinichi, Kuroda, Tatsuo, Sugawara, Shunsuke, Ishikawa, Kosuke, and Sasaki, Satoru

Published
2020
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26. Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography

Author

Tomita, Hayato, Yamashiro, Tsuneo, Iida, Gyo, Tsubakimoto, Maho, Mimura, Hidefumi, and Murayama, Sadayuki

Subjects
machine learning, malignant lymphoma, cervical lymphadenopathy, texture, cancer of unknown primary
Abstract

To investigate the usefulness of texture analysis to discriminate between cervical lymph node (LN) metastasis from cancer of unknown primary (CUP) and cervical LN involvement of malignant lymphoma (ML) on unenhanced computed tomography (CT). Cervical LN metastases in 17 patients with CUP and cervical LN involvement in 17 patients with ML were assessed by 18F-FDG PET/CT. The texture features were obtained in the total cross-sectional area (CSA) of the targeted LN, following the contour of the largest cervical LN on unenhanced CT. Values for the max standardized uptake value (SUVmax) and the mean SUV value (SUVmean), and 34 texture features were compared using a Mann-Whitney U test. The diagnostic accuracy and area under the curve (AUC) of the combination of the texture features were evaluated by support vector machine (SVM) with nested cross-validation. The SUVmax and SUVmean did not differ significantly between cervical LN metastases from CUP and cervical LN involvement from ML. However, significant differences of 9 texture features of the total CSA were observed (p = 0.001 – 0.05). The best AUC value of 0.851 for the texture feature of the total CSA were obtained from the correlation in the gray-level co-occurrence matrix features. SVM had the best AUC and diagnostic accuracy of 0.930 and 84.8%. Radiomics analysis appears to be useful for differentiating cervical LN metastasis from CUP and cervical LN involvement of ML on unenhanced CT.

Published
2022

31. Embolization Tools

Author

Golzarian, Jafar, Siskin, Gary P., Sharafuddin, Melhem, Mimura, Hidefumi, Coldwell, Douglas M., Baert, A. L., editor, Sartor, K., editor, Golzarian, Jafar, editor, Sun, Shiliang, editor, and Sharafuddin, Melhelm J., editor

Published
2006
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36. Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma

Author

Tomita, Hayato, Yamashiro, Tsuneo, Iida, Gyo, Tsubakimoto, Maho, Mimura, Hidefumi, and Murayama, Sadayuki

Subjects
machine learning, PET, Texture analysis, nasopharyngeal cancer, malignant lymphoma
Abstract

Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating between nasopharyngeal cancer and nasopharyngeal ML. Thirty patients with nasopharyngeal tumors, including 17 nasopharyngeal cancers and 13 nasopharyngeal MLs, were underwent 18F-FDG PET/CT. All nasopharyngeal cancers and 7 of 13 nasopharyngeal MLs were confirmed by endoscopic biopsy. On unenhanced CT, 34 texture features were analyzed following lesion segmentation in the maximum area of the target lesion. The Mann-Whitney U test and areas under the curve (AUCs) were used for analysis and to compare the maximum standardized uptake values (SUV)max, SUVmean, and 34 texture features. A support vector machine (SVM) was constructed to evaluate the diagnostic accuracy and AUCs of combinations of texture features, with 50 repetitions of 5-fold cross-validation. Differences between the SUVmax and SUVmean for nasopharyngeal cancers and nasopharyngeal MLs were not significant. Significant differences of texture features were seen, as follows: 1 histogram feature (p = 0.038), 3 gray-level co-occurrence matrix features (p < 0.05), and 1 neighborhood gray-level different matrix feature (NGLDM) (p = 0.003). Coarseness in NGLDM provided the highest diagnostic accuracy and largest AUC of 76.7% and 0.82, respectively. SVM evaluation of the combined texture features obtained the highest accuracy of 81.3%, with an AUC of 0.80. Combined texture features can provide useful information for discriminating between nasopharyngeal cancer and nasopharyngeal ML on unenhanced CT., This is an Open Access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view the details of this license, please visit (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Published
2021

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