2023 Fiscal Year Final Research Report
Creation of an analysis platform and AI to detect lung fibrosis CT images with high risk of developing lung cancer.
| Project/Area Number |
18K15553
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 52040:Radiological sciences-related
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| Research Institution | Saga University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Keywords | 間質性肺炎における肺癌発生 / 肺線維症 / 人工知能 |
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| Outline of Final Research Achievements |
The aim of the research was to detect image findings of lung fibrosis that may be a risk factor for lung cancer development by training an artificial intelligence (AI) on "CT images of lung fibrosis that developed lung cancer" and "CT images of lung fibrosis that did not develop lung cancer after long-term follow-up" in collaboration with an engineering research collaborator. The model was built using the Encoder part of SimSiam and set up so that images with similar features were placed on the latent space. Using the past images of the area where lung cancer occurred in the course of the disease and the same area (positive images) and the images of the course of the area that did not develop cancer (negative images) as search images, it was found that the detection of high-risk areas for carcinogenesis was possible from similar image search.
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| Free Research Field |
間質性肺炎
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| Academic Significance and Societal Importance of the Research Achievements |
肺線維症のCT画像において，肺癌が発生する領域には，発生しない領域と比べ何らかの特徴が存在し，それを把握しておくことにより，リスクの高い患者さんを早期発見し，厳重に経過観察することが可能となると考えられる．
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