Katsuta Yoshiyuki 勝田義之

Researcher Number	90848326
Other IDs
Affiliation (Current)	2025: 東北大学, 大学病院, 助教
Affiliation (based on the past Project Information) *help	2020 – 2024: 東北大学, 大学病院, 助教
Review Section/Research Field	Principal Investigator Basic Section 52040:Radiological sciences-related
Keywords	Principal Investigator 人工知能 / 肺炎 / 放射線治療 / 放射線肺臓炎 / 肺がん / 放射線誘発性肺炎 / 機械学習

Research Projects
(2 results)
Research Products
(8 results)

Displaying lung tissues that introduces radiation pneumonitis by irradiationPrincipal Investigator
- Principal Investigator
  
  勝田義之
- Project Period (FY)
  2024 – 2027
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 52040:Radiological sciences-related
- Research Institution
  Tohoku University
Prediction of radiation pneumonitis with machine learning using dose-volume and dose-function featuresPrincipal Investigator
- Principal Investigator
  
  Katsuta Yoshiyuki
- Project Period (FY)
  2020 – 2023
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 52040:Radiological sciences-related
- Research Institution
  Tohoku University

All 2023 2022 2021 2020

All Journal Article Presentation Patent

[Journal Article] Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images2023
- Author(s)
  Yoshiyuki Katsuta et al.
- Journal Title
  
  Physica Medica
  
  Volume: 105 Pages: 102505-102505
- DOI
  10.1016/j.ejmp.2022.11.009
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K16815, KAKENHI-PROJECT-20H03624
[Journal Article] Feasibility of Differential Dose Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence2022
- Author(s)
  Yoshiyuki Katsuta et al.
- Journal Title
  
  Diagnostics
  
  Volume: 12 Issue: 6 Pages: 1354-1354
- DOI
  10.3390/diagnostics12061354
- Peer Reviewed / Open Access
- Data Source
  KAKENHI-PROJECT-20K16815
[Journal Article] Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features2021
- Author(s)
  Katsuta Yoshiyuki、Kadoya Noriyuki、Mouri Shina、Tanaka Shohei、Kanai Takayuki、Takeda Kazuya、Yamamoto Takaya、Ito Kengo、Kajikawa Tomohiro、Nakajima Yujiro、Jingu Keiichi
- Journal Title
  
  Journal of Radiation Research
  
  Volume: 63 Issue: 1 Pages: 71-79
- DOI
  10.1093/jrr/rrab097
- Peer Reviewed
- Data Source
  KAKENHI-PROJECT-20K16815
[Patent] 放射線誘発性肺炎を誘発する肺組織を抽出する技術2023
- Inventor(s)
  勝田義之
- Industrial Property Rights Holder
  東北大学
- Industrial Property Rights Type
  特許
- Filing Date
  2023
- Data Source
  KAKENHI-PROJECT-20K16815
[Presentation] 複数の放射線生物影響数理モデルによる放射線肺臓炎の予測2021
- Author(s)
  勝田義之他
- Organizer
  日本放射線腫瘍学会第34回学術大会
- Data Source
  KAKENHI-PROJECT-20K16815
[Presentation] Evaluation of machine learning-based prediction model with combination of conventional and functional dosimetric parameters for radiation pneumonitis in NSCLC patients2021
- Author(s)
  Mouri S, Kadoya N, Katsuta Y et al.
- Organizer
  日本医学物理学会第121回学術大会
- Data Source
  KAKENHI-PROJECT-20K16815
[Presentation] Development of machine learning-based radiation pneumonitis prediction model with combination of conventional, functional dosimetric parameters and clinical factors in NSCLC patients2021
- Author(s)
  Mouri S, Kadoya N, Katsuta Y et al.
- Organizer
  日本医学物理学会第122回学術大会
- Data Source
  KAKENHI-PROJECT-20K16815
[Presentation] Evaluation of machine learning-based prediction model for radiation pneumonitis in NSCLC patients2020
- Author(s)
  Mouri S, Kadoya N, Katsuta Y, Kanai T, Nakajima Y, Tanabe S, Sugai Y, Umeda M, Dobashi S, Takeda K, Jingu K
- Organizer
  20th Asia-Oceania Congress of Medical Physics
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-20K16815

Katsuta Yoshiyuki 勝田 義之

Research Projects

Research Products

Displaying lung tissues that introduces radiation pneumonitis by irradiationPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

Prediction of radiation pneumonitis with machine learning using dose-volume and dose-function featuresPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Feasibility of Differential Dose Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features2021

Author(s)

Journal Title

DOI

Data Source

[Patent] 放射線誘発性肺炎を誘発する肺組織を抽出する技術2023

Inventor(s)

Industrial Property Rights Holder

Industrial Property Rights Type

Filing Date

Data Source

[Presentation] 複数の放射線生物影響数理モデルによる放射線肺臓炎の予測2021

Author(s)

Organizer

Data Source

[Presentation] Evaluation of machine learning-based prediction model with combination of conventional and functional dosimetric parameters for radiation pneumonitis in NSCLC patients2021

Author(s)

Organizer

Data Source

[Presentation] Development of machine learning-based radiation pneumonitis prediction model with combination of conventional, functional dosimetric parameters and clinical factors in NSCLC patients2021

Author(s)

Organizer

Data Source

[Presentation] Evaluation of machine learning-based prediction model for radiation pneumonitis in NSCLC patients2020

Author(s)

Organizer

Data Source

Katsuta Yoshiyuki 勝田義之