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Prediction of treatment response for head-and-neck cancer using IIMU

Research Project

Project/Area Number 16K19798
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeMulti-year Fund
Research Field Radiation science
Research InstitutionHokkaido University

Principal Investigator

Hirata Kenji  北海道大学, 医学研究科, 助教 (30431365)

Project Period (FY) 2016-04-01 – 2018-03-31
Project Status Completed (Fiscal Year 2017)
Budget Amount *help
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Keywords核医学 / 頭頚部癌 / 化学療法 / IIMU / 甲状腺癌 / metabolic tumor volume / texture analysis / thymidine phosphorylase / FDG / 頭頸部癌 / 腫瘍 / イメージング
Outline of Final Research Achievements

In this study, we first aimed to evaluate clinical usefulness of 123I-IIMU, a SPECT tracer we developed for visualizing thymidine phosphorylase expression, in patients with head-and-neck cancer. However, we were not able to administer IIMU to patients during the research period because the composition of 123I-NaI solution (purchased from the manufacturer to use for synthesis of IIMU) was changed by the manufacturer, and thus the synthesis yield was significantly reduced. Therefore, during this period, we optimized the protocol of IIMU radiosynthesis and purification conditions according to the new solution composition. We finally confirmed that IIMU was produced at a yield of 50% or more. We also administered IIMU to mice to confirm that thymidine phosphorylase was imaged. In parallel, we investigate FDG PET clinical images of thyroid cancer retrospectively and demonstrated clinical usefulness of metabolic tumor volume and heterogeneity indices (i.e., texture features).

Report

(3 results)
  • 2017 Annual Research Report   Final Research Report ( PDF )
  • 2016 Research-status Report
  • Research Products

    (7 results)

All 2018 2017

All Presentation (7 results) (of which Int'l Joint Research: 2 results,  Invited: 2 results)

  • [Presentation] 核医学における最近のコンピューター支援診断 ~ Texture 解析と Deep learning を中心に2018

    • Author(s)
      平田健司
    • Organizer
      第 88 回日本核医学会関東甲信越地方会
    • Related Report
      2017 Annual Research Report
    • Invited
  • [Presentation] 核医学画像のRadiomics2018

    • Author(s)
      平田健司
    • Organizer
      第74会日本放射線技術学会総会学術大会
    • Related Report
      2017 Annual Research Report
    • Invited
  • [Presentation] Radiomics approach with texture analysis to overcome inter-scanner image variability - a simulation study targeting multicenter clinical trials2018

    • Author(s)
      Kenji Hirata, Osamu Manabe, Kentaro Kobayashi, Shiro Watanabe, Takuya Toyonaga, Sho Furuya, Keiichi Magota, Nagara Tamaki, Tohru Shiga
    • Organizer
      Society of Nuclear Medicine and Molecular Imaging 2018 Annual Meeting
    • Related Report
      2017 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Site-based Metabolic Tumor Volume may be a Prognostic Factor in Patients with Differentiated Thyroid Carcinoma2017

    • Author(s)
      Kenji Hirata, Yuko Uchiyama, Shiro Watanabe, Takuya Toyonaga, Osamu Manabe, Kentaro Kobayashi, Hisaya Kikuchi, Tohru Shiga, Eriko Suzuki, Keiichi Magota, Satoshi Takeuchi, Nagara Tamaki
    • Organizer
      第76回日本医学放射線学会
    • Related Report
      2017 Annual Research Report 2016 Research-status Report
  • [Presentation] Semi-automated whole-body texture analysis may improve predictive performance of FDG PET-CT for patients with differentiated thyroid carcinoma2017

    • Author(s)
      Kenji Hirata, Tohru Shiga, Yuko Uchiyama, Shiro Watanabe, Takuya Toyonaga, Osamu Manabe, Kentaro Kobayashi, Hisaya Kikuchi, Keiichi Magota, Nagara Tamaki
    • Organizer
      Society of Nuclear Medicine and Molecular Imaging 2017 Annual Meeting
    • Related Report
      2017 Annual Research Report 2016 Research-status Report
    • Int'l Joint Research
  • [Presentation] オープンソースソフトウェアMetavolによる全身FDG PET-CTのテクスチャー解析と甲状腺癌への応用2017

    • Author(s)
      平田健司、真鍋治、内山裕子、小林健太郎、渡邊史郎、豊永拓哉、志賀哲
    • Organizer
      第57回日本核医学会学術総会
    • Related Report
      2017 Annual Research Report
  • [Presentation] Deep learningによる核医学画像診断、病変輪郭抽出の試み2017

    • Author(s)
      平田健司
    • Organizer
      第7回核医学画像解析研究会
    • Related Report
      2017 Annual Research Report

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Published: 2016-04-21   Modified: 2019-03-29  

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