Establishment of a next-generation prognostic model for early-stage lung cancer by integrated analysis of ultra-high-resolution morphological and functional images.

2023 Fiscal Year Final Research Report

• Project/Area Number: 19K08149
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 52040:Radiological sciences-related
• Research Institution: Nagoya University
• Principal Investigator: Iwano Shingo 名古屋大学, 医学系研究科, 准教授 (90335034)
• Co-Investigator(Kenkyū-buntansha): 中村 彰太 名古屋大学, 医学部附属病院, 講師 (20612849) ; 伊藤 信嗣 名古屋大学, 医学部附属病院, 講師 (50597846) ; 伊藤 倫太郎 名古屋大学, 医学系研究科, 特任助教 (80813336)
• Project Period (FY): 2019-04-01 – 2024-03-31
• Keywords: 原発性肺癌 / 高精細CT / PET/CT

Outline of Final Research Achievements

In this study, we searched for biomarkers that can predict the invasiveness and prognosis of primary lung cancer in an integrated manner by 3D image analysis and AI of high-definition CT and FDG-PET/CT. The study period was extended to five years due to the Corona disaster, but the following four findings were published as conference presentations and scientific papers.
1) 3D iodine density measurement by contrast-enhanced dual-energy CT can predict the prognosis of lung cancer; 2) quantitative PET/CT data can diagnose mediastinal lymph node metastasis in non-small cell lung cancer; 3) chest wall invasion of primary lung cancer can be diagnosed based on ultra-high-resolution CT findings; 4) 5 mm artificial intelligence to generate virtual high-resolution CT images from 5 mm thick CT images of lung cancer.

Free Research Field

放射線医学

Academic Significance and Societal Importance of the Research Achievements

この研究成果は、原発性肺癌の診断と予後予測を飛躍的に向上させる新しい方法を提供しました。高精細CTとFDG-PET/CTを活用し、AIを用いて得られたデータから、より正確な診断と予後予測が可能となりました。特に、造影dual-energy CTや超高精細CTによる新たな診断法や、AIによる画像生成技術の開発は、医療現場での迅速かつ的確な治療方針の決定に寄与し、患者の生存率向上と医療費の削減に大きく貢献します。