Rchr
J-GLOBAL ID：200901068534073978   Update date: Aug. 19, 2024

Jinguji Megumi

ジングウジ メグミ | Jinguji Megumi
Affiliation and department：
Kagoshima University Graduate School of Medical and Dental Sciences, Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences

About Kagoshima University Graduate School of Medical and Dental Sciences, Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences


Job title： -
Research field  (1)： Radiology
Research keywords  (8)： MRI ,  CT ,  RI ,  放射線診断 ,  MRI ,  CT ,  RI ,  radiological diagnosis
Research theme for competitive and other funds  (7)：
  • 2022 - 2025 FDG-PET代謝速度定数画像による免疫チェックポイント阻害剤効果判定法の確立
  • 2022 - 2025 定量肺血流SPECT/CTと機械学習による肺高血圧症診断法の開発
  • 2019 - 2023 18F-FDGの速度定数画像に基づく心サルコイド-シスの活動性評価法の開発
  • 2017 - 2019 99mTc-MIBIイメージングによるミトコンドリア病の全身骨格筋評価法の開発
  • 2008 - 2010 The roles of unsaturated fatty acids in breast cancer risk and the responsiveness to treatment
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Papers (72)：
  • Masatoyo Nakajo, Daisuke Hirahara, Megumi Jinguji, Satoko Ojima, Mitsuho Hirahara, Atsushi Tani, Koji Takumi, Kiyohisa Kamimura, Mitsuru Ohishi, Takashi Yoshiura. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Japanese journal of radiology. 2024
  • Masatoyo Nakajo, Daisuke Hirahara, Megumi Jinguji, Satoko Ojima, Mitsuho Hirahara, Atsushi Tani, Koji Takumi, Kiyohisa Kamimura, Mitsuru Ohishi, Takashi Yoshiura. Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Japanese Journal of Radiology. 2024
  • Kodai Kawaji, Masatoyo Nakajo, Yoshiaki Shinden, Megumi Jinguji, Atsushi Tani, Daisuke Hirahara, Ikumi Kitazono, Takao Ohtsuka, Takashi Yoshiura. Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer. Molecular imaging and biology. 2023. 25. 5. 923-934
  • Masatoyo Nakajo, Megumi Jinguji, Soichiro Ito, Atushi Tani, Mitsuho Hirahara, Takashi Yoshiura. Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Japanese journal of radiology. 2023
  • Masatoyo Nakajo, Hiromi Nagano, Megumi Jinguji, Yoshiki Kamimura, Keiko Masuda, Koji Takumi, Atsushi Tani, Daisuke Hirahara, Keisuke Kariya, Masaru Yamashita, et al. The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer. The British journal of radiology. 2023. 96. 1149. 20220772-20220772
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MISC (25)：
  • 萱野 大樹, 若林 大志, 稲木 杏吏, 村山 敏典, 神宮司 メグミ, 樋口 徹也, 志賀 哲, 絹谷 清剛. 先進医療Bとして実施した難治性褐色細胞腫に対するI-131 MIBG治療の第1相試験. 日本内分泌学会雑誌. 2019. 95. 1. 405-405
  • 神宮司 メグミ, 中條 正豊, 中條 政敬, 谷 淳至, 吉浦 敬. 【副腎の画像診断・IVR:診断に必要な基礎から臨床まで】副腎疾患の核医学診断. 臨床画像. 2018. 34. 3. 306-316
  • 加治屋 より子, 谷 淳至, 南立 亮, 仮屋 圭佑, 神宮司 メグミ, 中條 正豊, 西俣 寛人, 福永 秀敏, 中條 政敬, 吉浦 敬. PET検診がん発見率と年代や性別、受診回数との関連性 どのような人がFDG-PET検診を受けるとよいか. 臨床放射線. 2017. 62. 9. 1157-1163
  • Masatoyo Nakajo, Megumi Jinguji, Atsushi Tani, Hiroaki Nagano, Yoshiaki Nakabeppul, Masayuki Nakajo, Takashi Yoshiura. Texture analysis of F-18-FDG PET/CT for predicting the malignant nature in thymic epithelial tumors. JOURNAL OF NUCLEAR MEDICINE. 2017. 58
  • M. Jinguji, M. Nakajo, M. Nakajo, Y. Nakabeppu, C. Koriyama, T. Yoshiura. Thymic involution after radioiodine therapy for Graves' disease. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING. 2015. 42. S727-S728
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Professional career (1)：
  • 博士(医学) (鹿児島大学)
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