| Project/Area Number |
21K08905
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 55040:Respiratory surgery-related
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| Research Institution | The University of Tokushima |
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Principal Investigator |
TAKIZAWA Hiromitsu 徳島大学, 大学院医歯薬学研究部(医学域), 教授 (90332816)
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| Co-Investigator(Kenkyū-buntansha) |
近藤 和也 徳島大学, 大学院医歯薬学研究部(医学域), 教授 (10263815)
宮本 直輝 徳島大学, 病院, 助教 (00865305)
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| Project Period (FY) |
2021-04-01 – 2025-03-31
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| Project Status |
Completed (Fiscal Year 2024)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2022: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2021: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 肺腺癌 / 浸潤診断 / 縮小手術 / 自家蛍光 / 人工知能 |
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| Outline of Research at the Start |
自家蛍光顕微鏡で未染の肺組織を観察すると，肺胞の骨格構造を成す肺胞弾性線維網に一致した強い自家蛍光が観察される．そして肺腺癌の浸潤部位においては，この自家蛍光を発する肺胞弾性線維網に肥厚や断裂などの構造変化が確認される．本研究では，肺腺癌の自家蛍光顕微鏡画像を機械学習アルゴリズムに学習データとしてインプットし，浸潤予測モデルを構築することを目的とする．小型肺腺癌の術中診断にこのモデルを用いることができれば，再発リスクの低い症例を適格に選別し肺機能を温存する術式を適応できるようになる．
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| Outline of Final Research Achievements |
This study aimed to develop a system for diagnosing the extent of lung adenocarcinoma invasion by capturing autofluorescence images of fresh-frozen surgical specimens using a digital slide scanner and analyzing them with artificial intelligence (AI). Sections were prepared from resected specimens, and autofluorescence images were captured using a digital slide scanner. Subsequently, the sections were stained with Elastica Van Gieson (EVG) and imaged. The EVG-stained images were analyzed using pathology image analysis software, where annotations were added to areas such as: (i) Invaded areas, (ii) Non-invaded areas, (iii) Normal lung. The autofluorescence and EVG-stained images were then subdivided, and a convolutional neural network model was trained using these annotated regions as supervised data to identify the extent of lung adenocarcinoma invasion. A program is currently under development to display the invaded areas as a heatmap.
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| Academic Significance and Societal Importance of the Research Achievements |
肺腺癌浸潤範囲同定モデルにより微小な肺癌の浸潤範囲を術中に診断できるようになれば、より個別化した治療方針として肺部分切除や肺区域切除といった、より肺を温存できる術式を適応できる肺癌患者が増える可能性がある。また、このモデルは病理医にとっても判断が難しいとされる肺腺癌の浸潤範囲の決定において、病理医の診断を補完するシステムとなる可能性もある。
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