2020 - 2022 The decision support tool for T1-4N0M0 NSCLC patients treated with SBRT analyzing our accumulated database set using machine learning methods
論文 (9件):
Eride Mutu, Takeshi Akiba, Yoshitsugu Matsumoto, Etsuo Kunieda, Ryuta Nagao, Tsuyoshi Fukuzawa, Tomomi Katsumata, Toshihisa Kuroki, Tatsuya Mikami, Yoji Nakano, et al. Effect on Heart and Lung Doses Reduction of Abdominal and Thoracic Deep Inspiratory Breath-hold Assuming Involved-field Radiation Therapy in Patients with Simulated Esophageal Cancer. Tokai J Exp Clin Med. 2023. 48. 1. 32-37
Takahisa Eriguchi, Atsuya Takeda, Takafumi Nemoto, Yuichiro Tsurugai, Naoko Sanuki, Yudai Tateishi, Yuichi Kibe, Takeshi Akiba, Mari Inoue, Kengo Nagashima, et al. Relationship between Dose Prescription Methods and Local Control Rate in Stereotactic Body Radiotherapy for Early Stage Non-Small-Cell Lung Cancer: Systematic Review and Meta-Analysis. Cancers. 2022. 14. 15. 3815-3815
Takafumi Nemoto, Atsuya Takeda, Yukinori Matsuo, Noriko Kishi, Takahisa Eriguchi, Etsuo Kunieda, Ryusei Kimura, Naoko Sanuki, Yuichiro Tsurugai, Masamichi Yagi, et al. Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy. JCO Clinical Cancer Informatics. 2022. 6. 6. e2100176
Takafumi Nemoto, Natsumi Futakami, Etsuo Kunieda, Masamichi Yagi, Atsuya Takeda, Takeshi Akiba, Eride Mutu, Naoyuki Shigematsu. Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs. Radiological Physics and Technology. 2021. 14. 3. 318-327
Takafumi Nemoto, Natsumi Futakami, Etsuo Kunieda, Masamichi Yagi, Atsuya Takeda, Takeshi Akiba, Eride Mutu, Naoyuki Shigematsu. [Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs]. Igaku butsuri : Nihon Igaku Butsuri Gakkai kikanshi = Japanese journal of medical physics : an official journal of Japan Society of Medical Physics. 2023. 43. 1. 19-19
Effects of Sample Size and Data Augmentation on U-Net-based Automatic Segmentation of Various Organs
(第22回アジア・オセアニア医学物理学会(AOCMP) 2022)
放射線治療とAI
(Society of Advanced Medical Imaging (SAMI) 2022)
Efficacy evaluation of 2-D, 3-D U-Net semantic segmentation of normal lungs
(第39回欧州放射線腫瘍学会(ESTRO) 2020)
The effects of sample size and data augmentation on the efficacy of semantic segmentation for prostate cancer using deep learning: A report of more than 500 cases.
(第62回米国放射線腫瘍学会(ASTRO) 2020)