Development of a methodology for predicting radiation therapy prognosis based on a radiomics with variability of patients in the treatment

• Project/Area Number: 18K15625
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: Hokkaido University (2020-2021); The University of Tokyo (2018-2019)
• Principal Investigator: Nakamoto Takahiro 北海道大学, 保健科学研究院, 助教 (10808877)
• Project Period (FY): 2018-04-01 – 2022-03-31
• Project Status: Completed (Fiscal Year 2021)
• Budget Amount: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000); Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000); Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
• Keywords: Radiomics / 予後予測 / 放射線治療 / 患者変動 / 医学物理学 / 食道扁平上皮がん / CBCT画像 / コックス回帰 / リスクスコア / ログランク検定 / DIR / 神経膠腫 / 肺がん転移性脳腫瘍 / 機械学習 / 予後因子予測

Outline of Final Research Achievements

The purpose of the study was to develop a system for predicting radiation therapy prognosis based on a radiomics with patient’s variability in the treatment. Radiomic features with patient’s variability were extracted from multi-modal medical images acquired in and before the radiation therapy. We developed system for predicting radiation therapy prognosis and factors related to the prognosis using the radiomic features with patient’s variability. We have been suggested that the radiomics-based system with the patient’s variability would be feasible for predicting the radiation therapy prognosis.

Academic Significance and Societal Importance of the Research Achievements

本研究において治療時の患者変動を含んだRadiomics特徴量と放射線治療予後との関係を学習させてモデリングすることにより治療時患者変動を考慮した上で予後が予測できる可能性を示した．また，本研究で開発したシステムを用いて予測した予後の結果を治療計画時や治療中にフィードバックすることで患者個々の変動や特徴に最適化されたオーダーメイド放射線治療の実現が期待できる．必要なデータは放射線治療を行なう上で取得される医用画像と予後情報のみであるため，提案したシステムで構築したモデルを用いれば他施設でも簡便に予後を予測することが可能で臨床的な意義が高い．

Research Products

• [Journal Article] Training of deep cross‐modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion (2022)
  • Author(s): Ozaki S, Kaji S, Nawa K, Imae T, Aoki A, Nakamoto T, Ohta T, Nozawa Y, Yamashita H, Haga A, Nakagawa K
  • Journal Title: Medical Physics Volume: - Issue: 6 Pages: 1-14
  • DOI: 10.1002/mp.15626
  • Peer Reviewed / Open Access
• [Journal Article] Improvement in Image Quality of CBCT during Treatment by Cycle Generative Adversarial Network (2020)
  • Author(s): 今江 禄一, 鍛冶 静雄, 木田 智士, 松田 佳奈子, 竹中 重治, 青木 淳, 仲本 宗泰, 尾崎 翔, 名和 要武, 山下 英臣, 中川 恵一, 阿部 修．
  • Journal Title: Japanese Journal of Radiological Technology Volume: 76 Issue: 11 Pages: 1173-1184
  • DOI: 10.6009/jjrt.2020_JSRT_76.11.1173
  • NAID: 130007941322
  • ISSN: 0369-4305, 1881-4883
  • Peer Reviewed / Open Access
• [Journal Article] Visual enhancement of Cone‐beam CT by use of CycleGAN (2020)
  • Author(s): Kida S, Kaji S, Nawa K, Imae T, Nakamoto T, Ozaki S, Ohta T, Nozawa Y, Nakagawa K.
  • Journal Title: Medical Physics Volume: 47(3) Issue: 3 Pages: 998-1010
  • DOI: 10.1002/mp.13963
  • Peer Reviewed / Open Access
• [Journal Article] Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography (2020)
  • Author(s): S. Ozaki, A. Haga, E. Chao, C. Maurer, K. Nawa, T. Ohta, T. Nakamoto, Y. Nozawa, T. Magome, M. Nakano, K. Nakagawa
  • Journal Title: The Journal of Medical Investigation Volume: 67 Issue: 1.2 Pages: 30-39
  • DOI: 10.2152/jmi.67.30
  • NAID: 130007839921
  • ISSN: 1343-1420, 1349-6867
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis (2019)
  • Author(s): Nakamoto Takahiro、Takahashi Wataru、Haga Akihiro、Takahashi Satoshi、Kiryu Shigeru、Nawa Kanabu、Ohta Takeshi、Ozaki Sho、Nozawa Yuki、Tanaka Shota、Mukasa Akitake、Nakagawa Keiichi
  • Journal Title: Scientific Reports Volume: 9 Issue: 1 Pages: 19411-19423
  • DOI: 10.1038/s41598-019-55922-0
  • NAID: 120006875662
  • Peer Reviewed / Open Access
• [Journal Article] Radiomics analysis for glioma malignancy evaluation using diffusion kurtosis and tensor imaging (2019)
  • Author(s): Takahashi S, Takahashi W, Tanaka S, Haga A, Nakamoto T, Suzuki Y, Mukasa A, Takayanagi S, Kitagawa Y, Hana T, Nejo T, Nomura M, Nakagawa K, Saito N
  • Journal Title: International Journal of Radiation Oncology Biology Physics Volume: 105 Issue: 4 Pages: 784-791
  • DOI: 10.1016/j.ijrobp.2019.07.011
  • Peer Reviewed
• [Journal Article] An Introduction to Radiomics: Toward a New Era of Precision Medicine (2018)
  • Author(s): 仲本 宗泰, 芳賀 昭弘, 高橋 渉
  • Journal Title: Japanese Journal of Medical Physics (Igakubutsuri) Volume: 38 Issue: 3 Pages: 129-134
  • DOI: 10.11323/jjmp.38.3_129
  • NAID: 130007550535
  • ISSN: 1345-5354, 2186-9634
  • Year and Date: 2018-12-25
  • Peer Reviewed / Open Access
• [Journal Article] Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network (2018)
  • Author(s): Kida Satoshi、Nakamoto Takahiro、Nakano Masahiro、Nawa Kanabu、Haga Akihiro、Kotoku Jun'ichi、Yamashita Hideomi、Nakagawa Keiichi
  • Journal Title: Cureus Volume: - Pages: 1-15
  • DOI: 10.7759/cureus.2548
  • Peer Reviewed / Open Access
• [Journal Article] Volumetric and dosimetric comparison of organs at risk between the prone and supine positions in postoperative radiotherapy for prostate cancer (2018)
  • Author(s): Sawayanagi S, Yamashita H, Ogita M, Kiritoshi T, Nakamoto T, Abe O, Nakagawa K
  • Journal Title: Radiation Oncology Volume: 13 Issue: 1
  • DOI: 10.1186/s13014-018-1023-0
  • Peer Reviewed
• [Presentation] Training modality conversion models with small data and its application to MVCT to kVCT conversion (2021)
  • Author(s): Ozaki S, Kaji S, Nawa K, Imae T, Aoki A, Nakamoto T, Ohta T, Nozawa Y, Haga A, Nakagawa K
  • Organizer: ESTRO 2021 Annual Meeting
  • Int'l Joint Research
• [Presentation] Imaging biomarker analysis for grading malignant gliomas based on a few conventional magnetic resonance imaging sequences (2020)
  • Author(s): Nakamoto T, Takahashi W, Haga A, Takahashi S, Kiryu S, Nawa K, Ohta T, Ozaki S, Nozawa Y, Tanaka S, Mukasa A, Nakagawa K
  • Organizer: 2020 Joint AAPM COMP Meeting
  • Int'l Joint Research
• [Presentation] Radiomic-based prediction of malignant glioma grades using preoperative contrast-enhanced T1WIs and T2WIs (2020)
  • Author(s): Nakamoto T, Takahashi W, Haga A, Takahashi S, Kiryu S, Nawa K, Ohta T, Ozaki S, Nozawa Y, Nakagawa K.
  • Organizer: 第119回日本医学物理学会学術大会
• [Presentation] Prediction of uterus volume shrinkage for cervical cancer patients during radiotherapy using machine-learning approach with treatment planning-CT radiomic features (2020)
  • Author(s): Nakano M, Nakamoto T, Kumai Y, Koizumi Y, Sumi M, Nawa K, Imae T, Yoshioka Y, Oguchi M
  • Organizer: 2020 Joint AAPM COMP Meeting
  • Int'l Joint Research
• [Presentation] 左乳癌乳房全切除術後照射における3D-CRTとVMATの比較および呼吸性移動の検討 (2020)
  • Author(s): 野沢勇樹, 扇田真美, 名和要武, 太田岳史, 尾崎翔, 仲本宗泰, 山下英臣, 中川恵一
  • Organizer: 第33回日本放射線腫瘍学会学術大会
• [Presentation] Prediction of EGFR mutation status in lung adenocarcinomas through brain metastases using an MRI-based radiomics (2019)
  • Author(s): Nakamoto T, Takahashi W, Amano Y, Nawa K, Ohta T, Ozaki S, Nozawa Y, Yamashita H, Nakagawa K
  • Organizer: 19th Asia-Oceania Congress of Medical Physics
  • Int'l Joint Research
• [Presentation] Trial of dose distribution QA using smartphone and tablet camera with scintillator (2019)
  • Author(s): Ohta T, Nawa K, Ozaki S, Nozawa Y, Nakamoto T, Nakagawa K
  • Organizer: 19th Asia-Oceania Congress of Medical Physics
  • Int'l Joint Research
• [Presentation] Visualization of judgment basis of CNN to grading glioma (2019)
  • Author(s): Takahashi S, Tanaka S, Takahashi M, Yamazawa E, Hana T, Kitagawa Y, Takayanagi S, Takahashi W, Nakamoto T, Haga S, Hamamoto R, Saito N
  • Organizer: 24th Annual meeting of Society for NeuroOncology
  • Int'l Joint Research
• [Presentation] A novel radiomics model differentiating chordoma and chondrosarcoma (2019)
  • Author(s): Yamazawa E, Takahashi S, Tanaka S, Takahashi W, Nakamoto T, Takayanagi S, Kitagawa Y, Hana T, Koike T, Kushihara Y, Shin Masahiro, Saito N
  • Organizer: 24th Annual meeting of Society for NeuroOncology
  • Int'l Joint Research
• [Presentation] Pseudo-CBCT image prediction of head and neck cancer patient using principal component vector fields of early treatment fractions (2019)
  • Author(s): Nakano M, Imae T, Nakamoto T, Nawa K, Haga A, Takahashi W, Nakagawa K, Hashimoto M, Yoshioka Y, Oguchi M
  • Organizer: 第117回医学物理学会学術大会
• [Presentation] 左乳癌乳房全切除術後照射におけるVMATと3D-CRTの治療計画の比較 (2019)
  • Author(s): 野沢勇樹, 扇田真美, 名和要武, 太田岳史, 尾崎翔, 仲本宗泰, 山下英臣, 中川恵一
  • Organizer: 第32回日本放射線腫瘍学会学術大会
• [Presentation] Radiomics-based prediction of malignant glioma grades using T2-weighted magnetic resonance images (2018)
  • Author(s): Nakamoto T, Takahashi W, Haga A, Takahashi S, Nawa K, Ohta T, Ozaki S, Tanaka S, Mukasa A, Nakagawa K
  • Organizer: The American Association of Physicists in Medicine 60th Annual Meeting
  • Int'l Joint Research
• [Presentation] Radiomics analysis for detection of IDH mutation of glioma using diffusion tensor and kurtosis images (2018)
  • Author(s): Takahashi S, Takahashi W, Tanaka S, Haga A, Nakamoto T, Mukasa A, Takayanagi S, Suzuki Y, Koike T, Kitagawa Y, Hana T, Nejo T, Nomura M, Saito N
  • Organizer: Society for Neuro-Oncology 2018 Annual Meeting
  • Int'l Joint Research
• [Presentation] A feasibility study for classifying malignant glioma grades based on a radiomics analysis (2018)
  • Author(s): Nakamoto T, Takahashi W, Haga A, Takahashi S, Nawa K, Ohta T, Ozaki S, Nozawa Y, Tanaka S, Mukasa A, Nakagawa K
  • Organizer: 日本放射線腫瘍学会第31回学術大会
• [Presentation] MRI画像に基づくラディオミクスによる神経膠腫1p/19q共欠失の非侵襲的解析 (2018)
  • Author(s): 高橋渉, 仲本宗泰, 芳賀昭弘, 高橋慧, 田中将太, 山下英臣, 中川恵一
  • Organizer: 日本放射線腫瘍学会第31回学術大会
• [Presentation] 拡散画像のRadiomics解析と機械学習モデルによるIDH-1遺伝子変異の同定 (2018)
  • Author(s): 高橋慧, 高橋渉, 田中将太, 芳賀昭弘, 武笠晃丈, 仲本宗泰, 鈴木雄一, 小池司, 北川陽介, 花大洵, 根城尭英, 野村昌志, 高柳俊作, 斎藤延人
  • Organizer: 第19回日本分子脳神経外科学会
• [Presentation] 拡散画像を使ったconvolutional neural networkによる神経膠腫の悪性度診断 (2018)
  • Author(s): 高橋慧, 田中将太, 高橋渉, 仲本宗泰, 鈴木雄一, 北川陽介, 花大洵, 根城尭英, 野村昌志, 高柳俊作, 斎藤延人
  • Organizer: 日本脳神経外科学会第77回学術総会
• [Presentation] CBCT image quality improvement using a deep convolutional neural network (2018)
  • Author(s): Kida S, Nakamoto T, Nakano M, Nawa K, Haga A, Kotoku J, Yamashita H, Nakagawa K
  • Organizer: 第115回医学物理学会学術大会
• [Book] レディオミクス入門 (2021)
  • Author(s): 有村 秀孝, 角谷 倫之 編 (分担執筆: 仲本宗泰)
  • Total Pages: 338
  • Publisher: オーム社
  • ISBN: 9784274226380
• [Remarks] 東京大学医学部附属病院 放射線科 放射線治療部門
  • URL: http://u-tokyo-rad.jp/index.html