Establishment of radiotherapy technique to minimise the risk of radiation pneumonitis in locally advanced non-small cell lung cancer.

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K20856
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0902:General internal medicine and related fields
• Research Institution: Kyoto University
• Principal Investigator: Kishi Noriko 京都大学, 医学研究科, 特定助教 (70963346)
• Project Period (FY): 2022-08-31 – 2024-03-31
• Keywords: 肺癌 / 放射線治療 / 放射線性肺臓炎

Outline of Final Research Achievements

In 2022, patients with unresectable locally advanced lung cancer who underwent intensity-modulated radiation therapy (IMRT) were the subjects of a study to develop and validate a machine learning-based, low-risk radiotherapy model for radiation pneumonitis. This model enabled the creation of treatment plans with reduced lung doses. However, variations in tumor location and size limited the model’s broader application, an issue that required further investigation in 2023. The results of this study were presented at a conference. Additionally, a review article on postoperative radiotherapy for thymic tumors, along with a conference presentation on the relationship between radiation dose and prognosis of immune cells, were reported as part of a study on related adverse events. Based on these findings, the initially planned prospective observational study will be revised and launched as an interventional study.

Free Research Field

放射線治療

Academic Significance and Societal Importance of the Research Achievements

局所進行非小細胞肺癌において、化学放射線療法後に免疫療法を行うことで予後が有意に改善することが知られている。化学放射線治療後に症候性放射線性肺臓炎をきたした場合、免疫療法を休薬または中止する必要があるため、放射線性肺臓炎の低減が肺癌診療における現在の重要な課題である。
放射線治療計画において機械学習を用いた有害事象低リスクモデルを用いることで、肺線量を低減し、放射線性肺臓炎のリスクを低減することが期待できる。また、将来的に吸気量を指標とした息止めIMRTと併用して放射線性肺臓炎リスクを極小化することで、肺癌治療成績を向上しうるといえる。