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Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using 18F-FDG PET Images

Research Project

Project/Area Number 19K08153
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 52040:Radiological sciences-related
Research InstitutionHiroshima University

Principal Investigator

Murakami Yuji  広島大学, 医系科学研究科(医), 准教授 (10403528)

Co-Investigator(Kenkyū-buntansha) 河原 大輔  広島大学, 医系科学研究科(医), 助教 (20630461)
西淵 いくの  広島大学, 病院(医), 講師 (70595834)
三木 健太朗  杏林大学, 保健学部, 准教授 (90732818)
Project Period (FY) 2019-04-01 – 2022-03-31
Project Status Completed (Fiscal Year 2021)
Budget Amount *help
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2020: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Keywords食道扁平上皮癌 / 化学放射線療法 / 治療効果予測 / Radiomics / 機械学習 / ニューラルネットワーク / 食道癌 / 病理学的奏功 / 予測モデル / 人工知能 / 術前化学放射線治療 / 化学放射線治療 / 効果予測
Outline of Research at the Start

切除可能局所進行癌に対する術前化学放射線治療による原発腫瘍完全消失率は高率で、これらの症例では手術を施行しなくとも完治が得られていた可能性がある。しかし、現時点でこれら完全消失が得られる症例を予測するマーカは存在しない。本研究では、術前化学放射線療法+食道切除術を施行した局所進行食道癌症例を対象とする。Radiomicsの手法を用いて導いた治療前のCT、PET、内視鏡画像データの特徴量、臨床データ、放射線治療後の治療効果データを人工知能を用いて機械学習させ、治療前画像・臨床データから食道癌に対する化学放射線治療効果を高精度に予測するシステムの開発を行う。

Outline of Final Research Achievements

Using Radiomics and machine learning, we developed a model to predict pathological findings after preoperative chemoradiotherapy in patients with resectable locally advanced esophageal squamous cell carcinoma. A neural network was used as the machine learning method, and a five-fold cross validation analysis was performed to improve the accuracy. The mean predictive accuracy, specificity, sensitivity, and AUC of the model were 91.2%, 92.0%, 89.5%, and 0.97, respectively, which are very good results. The contents of this study were presented at the 33rd Annual Meeting of the Japanese Society for High Precision Torso Irradiation and the 62nd Annual Meeting of the American Society for Radiation Oncology. The study was also completed and accepted for publication in an international journal.

Academic Significance and Societal Importance of the Research Achievements

切除可能局所進行食道扁平上皮癌に対する術前化学放射線療法による病理学的完全奏効率は比較的高く、治療前の情報から病理所見を予測できれば、臓器温存希望患者の治療選択に貢献できる。本研究では術前画像情報(PET画像)から高精度に術前治療(化学放射線療法)の病理学的完全腫瘍消失を予測するモデルが構築できた。さらに精度を高めることで、病理学的腫瘍完全消失が高率に予測される食道扁平上皮癌患者における臓器温存治療の選択根拠を与えうるツールとなりえる。今後、多施設によるさらに多くの症例数を用いたより汎用的なモデルの開発が望まれるが、本研究はその礎となると考える。

Report

(4 results)
  • 2021 Annual Research Report   Final Research Report ( PDF )
  • 2020 Research-status Report
  • 2019 Research-status Report
  • Research Products

    (6 results)

All 2021 2020

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (5 results) (of which Int'l Joint Research: 2 results)

  • [Journal Article] Predicting the local response of esophageal squamous cell carcinoma to neoadjuvant chemoradiotherapy by radiomics with a machine learning method using<sup>18</sup>f-fdg pet images2021

    • Author(s)
      Murakami Yuji、Kawahara Daisuke、Tani Shigeyuki、Kubo Katsumaro、Katsuta Tsuyoshi、Imano Nobuki、Takeuchi Yuki、Nishibuchi Ikuno、Saito Akito、Nagata Yasushi
    • Journal Title

      Diagnostics

      Volume: 11 Issue: 6 Pages: 1049-1049

    • DOI

      10.3390/diagnostics11061049

    • URL

      https://pure.teikyo.jp/en/publications/f2423bff-be03-4b4b-a6ae-9a80ce899103

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] A Machine Learning Model with Radiomics Based on PET Images to Predict Pathological Response by Neoadjuvant Chemoradiotherapy for Esophageal Cancer.2020

    • Author(s)
      Murakami Y, et al.
    • Organizer
      ASTRO ANNUAL MEETING 2020
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal cancer based on PET images using radiomics and machine-learning.2020

    • Author(s)
      Murakami Y, et al.
    • Organizer
      ASCO‐GI 2020;Gastrointestinal Cancers Symposium
    • Related Report
      2020 Research-status Report
    • Int'l Joint Research
  • [Presentation] RadiomicsとAIによる切除可能 局所進行食道癌術前化学放射線療法後の病理所見予測モデルの検討2020

    • Author(s)
      村上祐司 他
    • Organizer
      第33回日本放射線腫瘍学会 高精度放射線外部照射部会学術大会
    • Related Report
      2020 Research-status Report
  • [Presentation] RadiomicsとAIによる切除可能局所進行食道癌術前化学放射線療法後の病理所見予測モデルの検討2020

    • Author(s)
      村上祐司
    • Organizer
      第33回高精度放射線外部照射部会学術大会
    • Related Report
      2019 Research-status Report
  • [Presentation] A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal cancer based on PET images using radiomics and machine-learning.2020

    • Author(s)
      Yuji Murakami
    • Organizer
      2020 Gastrointestinal Cancers Symposium
    • Related Report
      2019 Research-status Report

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Published: 2019-04-18   Modified: 2023-01-30  

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