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Gene mutation/expression prediction from clinical images using artificial intelligence technology in pancreatic cancer

Research Project

Project/Area Number 20K17570
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 55010:General surgery and pediatric surgery-related
Research InstitutionChiba Cancer Center (Research Institute)

Principal Investigator

Iwatate Yosuke  千葉県がんセンター(研究所), 肝胆膵外科, 医長 (10815731)

Project Period (FY) 2020-04-01 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2020: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Keywords膵臓癌 / 膵管腺癌 / Radiogenomics / ITGAV / ITGB1 / pancreatic cancer / artificial intelligence / Genomics / Radiomics
Outline of Research at the Start

膵癌においては発癌・進展・薬剤効果などに関連する遺伝子異常についても未解決な領域が多い。今回、我々は難治性の膵癌を対象に、組織検体を用いた網羅的なゲノム変異・RNA発現解析(Genomics解析)を行い、臨床転帰に関連する遺伝子情報を同定する。同時に治療前のCT・MRIから数学的に多数の画像的特徴量(統計量)を抽出するRadiomics解析を行う。深層学習を含めた機械学習を用いた人工知能(AI)技術を応用することにより、抽出された画像的特徴量(Radiomics)から、同定した遺伝学的情報(Genomics)および臨床転帰を予測するRadiogenomicsシステムを構築すること目的とする。

Outline of Final Research Achievements

As a radiogenomics analysis, RNA-seq was performed on 12 pancreatic cancer tissues for an exhaustive search of genes useful for clinical pathology. A gene expression prediction model was constructed by machine learning using immunostaining (IHC) and CT images of 107 cases. ITGB1 and ITGAV were identified and significantly correlated with IHC (r=0.552 P=0.118, r=0.625 P=0.039). The prognosis worsened in the IHC high expression group (P=0.035, 0.009). Recurrence was similar (P=0.028, 0.003). The predictive model for ITGAV showed a certain detectability (AUC=0.697), and the group predicted to have high expression also had a poor prognosis (P=0.048).

Academic Significance and Societal Importance of the Research Achievements

診断時に進行していることが多く、5年生存率が10%程度と予後不良な膵臓癌において、診断のための侵襲的な生検等の検査や外科治療は専門施設やハイボリュームセンターでないと施行できない場合が多い。今回CTによる予後予測や今後治療の一助のなりうる可能性がある遺伝子マーカーの同定およびその発現予測が可能であった。今後症例を重ねることでCT画像等の臨床画像から非侵襲的に予後や治療マーカーの同定が行うことができれば、高額で時間がかかる遺伝子検査でなく簡易で費用対効果が高く、専門機関でなくても比較的短期間で結果が得られる検査となることも考えられる。

Report

(4 results)
  • 2022 Annual Research Report   Final Research Report ( PDF )
  • 2021 Research-status Report
  • 2020 Research-status Report
  • Research Products

    (7 results)

All 2022 2021 2020 Other

All Journal Article (3 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 3 results,  Open Access: 3 results) Presentation (3 results) Remarks (1 results)

  • [Journal Article] Transcriptomic analysis reveals high ITGB1 expression as a predictor for poor prognosis of pancreatic cancer2022

    • Author(s)
      Iwatate Yosuke、Yokota Hajime、Hoshino Isamu、Ishige Fumitaka、Kuwayama Naoki、Itami Makiko、Mori Yasukuni、Chiba Satoshi、Arimitsu Hidehito、Yanagibashi Hiroo、Takayama Wataru、Uno Takashi、Lin Jason、Nakamura Yuki、Tatsumi Yasutoshi、Shimozato Osamu、Nagase Hiroki
    • Journal Title

      PLOS ONE

      Volume: 17 Issue: 6 Pages: e0268630-e0268630

    • DOI

      10.1371/journal.pone.0268630

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer2022

    • Author(s)
      Iwatate Yosuke、Yokota Hajime、Hoshino Isamu、Ishige Fumitaka、Kuwayama Naoki、Itami Makiko、Mori Yasukuni、Chiba Satoshi、Arimitsu Hidehito、Yanagibashi Hiroo、Takayama Wataru、Uno Takashi、Lin Jason、Nakamura Yuki、Tatsumi Yasutoshi、Shimozato Osamu、Nagase Hiroki
    • Journal Title

      International Journal of Oncology

      Volume: 60 Issue: 5 Pages: 1-13

    • DOI

      10.3892/ijo.2022.5350

    • Related Report
      2021 Research-status Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer2020

    • Author(s)
      Iwatate Yosuke、Hoshino Isamu、Yokota Hajime、Ishige Fumitaka、Itami Makiko、Mori Yasukuni、Chiba Satoshi、Arimitsu Hidehito、Yanagibashi Hiroo、Nagase Hiroki、Takayama Wataru
    • Journal Title

      British Journal of Cancer

      Volume: 123 Issue: 8 Pages: 1253-1261

    • DOI

      10.1038/s41416-020-0997-1

    • Related Report
      2020 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] Radiogenomics: Machine learning with imaging feature predict ITGB1, ITGAV expression which are poor prognostic factors derived from transcriptome analysis in pancreatic cancer.2021

    • Author(s)
      岩立陽祐, 星野敢, 横田元, 石毛文隆, 伊丹真紀子, 千葉聡,有光秀仁, 柳橋浩男, 高山亘
    • Organizer
      第80回 日本癌学会学術総会
    • Related Report
      2021 Research-status Report
  • [Presentation] Radiogenomics解析を用いた 膵癌におけるトランスクリプトームおよび予後の予測2020

    • Author(s)
      岩立陽祐, 星野敢, 横田元, 石毛文隆, 伊丹真紀子, 千葉聡,有光秀仁, 柳橋浩男, 高山亘
    • Organizer
      第120回日本外科学会定期学術集会
    • Related Report
      2020 Research-status Report
  • [Presentation] Radiogenomics analysis:prediction of transcriptome and prognosis in pancreatic cancer.2020

    • Author(s)
      岩立陽祐, 星野敢, 横田元, 石毛文隆, 伊丹真紀子, 千葉聡,有光秀仁, 柳橋浩男, 高山亘
    • Organizer
      第75回日本消化器外科学会総会
    • Related Report
      2020 Research-status Report
  • [Remarks] 難治性消化器癌を対象とした治療効果予測、予後予測のためのRadiogenomics理論の創出

    • URL

      https://www.pref.chiba.lg.jp/gan/center/gaiyo/rinsyoukenkyuuosirase.html

    • Related Report
      2021 Research-status Report

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Published: 2020-04-28   Modified: 2024-01-30  

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