| Project/Area Number |
19K17129
|
|---|
| Research Category |
Grant-in-Aid for Early-Career Scientists
|
| Allocation Type | Multi-year Fund |
|---|
| Review Section |
Basic Section 52040:Radiological sciences-related
|
|---|
| Research Institution | Chiba University |
|---|
Principal Investigator |
Nisiyama Akira 千葉大学, 医学部附属病院, 特任助教 (40792429)
|
|---|
| Project Period (FY) |
2019-04-01 – 2023-03-31
|
| Project Status |
Completed (Fiscal Year 2022)
|
| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 肺癌 / PD-L1 / CT / PET/CT / radiomics / 18F-FDG PET / radiomics解析 / Radiomics / 免疫チェックポイント阻害薬 |
|---|
| Outline of Research at the Start |
非小細胞性肺がん患者さんの治療前の検査画像（CTやPET/CT）と、病理組織学的検査（がん細胞・組織を採取し顕微鏡などで行う検査）で得られたがん細胞の特徴（がん細胞表面のPD-L1の発現率、PD-L1は抗がん剤、特に免疫チェックポイント阻害薬の感受性に関わります）を比較し、画像からがん細胞の特徴を推定するモデルを構築します。また、実際の抗がん剤の治療効果とも比較し、治療効果そのものを予測するモデルの構築を目指します。
|
| Outline of Final Research Achievements |
The patients with lung cancer, the expression rate of PD-L1 was confirmed histopathological, were enrolled. Radiomics analysis was performed from CT and PET/CT images, and image features of lung cancer lesions were extracted. Among the extracted image features, image features with high correlations with the PD-L1 expression rates (high expression group of 50% or more and low expression group of less than 50%) were extracted. By combining these imaging features, we constructed a predictive model for the PD-L1 expression rate. We also compared imaging features with therapeutic effects and 5-year prognosis data for patients treated with immune checkpoint inhibitors. However, it has been no easy to narrow down candidates for image features that are thought to be related to therapeutic effects, and it was not possible to construct a model for predicting therapeutic effects and prognosis.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究で、肺癌原発巣病変のPD-L1発現率の予想モデルは作成でき、またある程度高い確率での予測が可能であった。肺癌においても、画像で遺伝子の発現を予測できたことに学術的意義があると考える。生検困難な肺癌症例ではliquid biopsyでPD-L1発現率を確認するが、評価が難しい事が少なくない。この予想モデルを利用することで、PD-L1発現率を予測し、治療方針の決定に寄与できる可能性がある。一方で、治療効果・予後予測モデルは構築できなかった。このモデルが構築できれば、治療効果が高いと予想される患者群への早期治療介入を行うことで無駄の少ない医療が提供できる可能性がある。さらなる研究が望まれる。
|
