Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Kodai SATO"'
Autor:
Kodai Sato, Shinichi Sakamoto, Shinpei Saito, Hiroki Shibata, Yasutaka Yamada, Nobuyoshi Takeuchi, Yusuke Goto, Sazuka Tomokazu, Yusuke Imamura, Tomohiko Ichikawa, Eiryo Kawakami
Publikováno v:
BMC Cancer, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background For biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and
Externí odkaz:
https://doaj.org/article/0acbb301de4c43c2896d13286fe6b29c
Autor:
Kodai Sato, Tomokazu Sazuka, Takayuki Arai, Hiroaki Sato, Manato Kanesaka, Keisuke Ando, Shinpei Saito, Sangjon Pae, Yasutaka Yamada, Yusuke Imamura, Shinichi Sakamoto, Tomohiko Ichikawa
Publikováno v:
BJUI Compass, Vol 5, Iss 10, Pp 950-956 (2024)
Abstract Objectives Renal cell carcinoma (RCC) is shown to have a tendency for late recurrence, occurring 5 or more years after curative surgery. Imaging diagnosis is required for follow‐up, and there is no definitive answer as to how long this sho
Externí odkaz:
https://doaj.org/article/b275577542f14d42916a6fbdce0eb5a3
Autor:
Shinichi Sakamoto, Kodai Sato, Takahiro Kimura, Yoshiyuki Matsui, Yusuke Shiraishi, Kohei Hashimoto, Hideaki Miyake, Shintaro Narita, Jun Miki, Ryuji Matsumoto, Takuma Kato, Toshihiro Saito, Ryotaro Tomida, Masaki Shiota, Akira Joraku, Naoki Terada, Shigetaka Suekane, Tomoyuki Kaneko, Shuichi Tatarano, Yuko Yoshio, Takayuki Yoshino, Naotaka Nishiyama, Eiryo Kawakami, Tomohiko Ichikawa, Hiroshi Kitamura
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract A multicenter study of nonmetastatic castration-resistant prostate cancer (nmCRPC) was conducted to identify the optimal cut-off value of prostate-specific antigen (PSA) doubling time (PSADT) that correlated with the prognosis in Japanese nm
Externí odkaz:
https://doaj.org/article/916b6526270845538ce6a993714a45e5
Autor:
Koichiro Kurokawa, Yasutaka Yamada, Shinichi Sakamoto, Takuro Horikoshi, Kodai Sato, Sakie Nanba, Yoshihiro Kubota, Manato Kanesaka, Ayumi Fujimoto, Nobuyoshi Takeuchi, Hiroki Shibata, Tomokazu Sazuka, Yusuke Imamura, Toyonori Tsuzuki, Takashi Uno, Tomohiko Ichikawa
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract The prognostic significance of unconventional histology (UH) subtypes including intraductal carcinoma of the prostate (IDC-P), ductal adenocarcinoma, and cribriform pattern has been investigated for prostate cancer (PCa). However, little is
Externí odkaz:
https://doaj.org/article/47fcf6bc250646e0b3e811cb36bf9cdf
Publikováno v:
Technologies, Vol 12, Iss 10, p 195 (2024)
Thin sound-absorbing materials are particularly desired in space-constrained applications, such as in the automotive industry. In this study, we theoretically analyzed the structure of relatively thin glass wool or polyester wool laminated with a non
Externí odkaz:
https://doaj.org/article/f658552f429d43528cef06cc4488e76a
Autor:
Shinpei Saito, Shinichi Sakamoto, Kosuke Higuchi, Kodai Sato, Xue Zhao, Ken Wakai, Manato Kanesaka, Shuhei Kamada, Nobuyoshi Takeuchi, Tomokazu Sazuka, Yusuke Imamura, Naohiko Anzai, Tomohiko Ichikawa, Eiryo Kawakami
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract Machine learning technology is expected to support diagnosis and prognosis prediction in medicine. We used machine learning to construct a new prognostic prediction model for prostate cancer patients based on longitudinal data obtained from
Externí odkaz:
https://doaj.org/article/3674b0fec0144b9eac2d2cee5dfc7ffc
Autor:
Hirokazu Madokoro, Kodai Sato, Stephanie Nix, Shun Chiyonobu, Takeshi Nagayoshi, Kazuhito Sato
Publikováno v:
Sensors, Vol 23, Iss 21, p 8809 (2023)
The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is
Externí odkaz:
https://doaj.org/article/210af288e5f34cc3895efacc7b660693
Autor:
Kazuki Iwahana, Tatsuya Takemura, Ju Chien Cheng, Nami Ashizawa, Naoki Umeda, Kodai Sato, Ryota Kawakami, Rei Shimizu, Yuichiro Chinen, Naoto Yanai
Publikováno v:
IEEE Access, Vol 9, Pp 78293-78314 (2021)
Fast and accurate malicious domain detection is an essential research theme to prevent cybercrime, and machine learning is an attractive approach for detecting unseen malicious domains in the past decade. In this paper, we present MADMAX (MAchine lea
Externí odkaz:
https://doaj.org/article/7687dcec26e7486692e49b3cc31523c0
Publikováno v:
Journal of Photopolymer Science and Technology. 35:213-217
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8031641fb19390521f4ac5e041353e09
https://doi.org/10.2494/photopolymer.35.213
https://doi.org/10.2494/photopolymer.35.213
Publikováno v:
Noise Control Engineering Journal; Jan2024, Vol. 72 Issue 1, p37-50, 14p