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

• Project/Area Number: 16K19798
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Radiation science
• Research Institution: Hokkaido 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: ¥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).

Research Products

• [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
  • Invited
• [Presentation] 核医学画像のRadiomics (2018)
  • 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
  • Invited
• [Presentation] Radiomics approach with texture analysis to overcome inter-scanner image variability - a simulation study targeting multicenter clinical trials (2018)
  • 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
  • Int'l Joint Research
• [Presentation] Site-based Metabolic Tumor Volume may be a Prognostic Factor in Patients with Differentiated Thyroid Carcinoma (2017)
  • 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
• [Presentation] Semi-automated whole-body texture analysis may improve predictive performance of FDG PET-CT for patients with differentiated thyroid carcinoma (2017)
  • 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
  • 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
• [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