Quantitative morphometric analysis of breast cancer invasion using a machine learning approach
Project/Area Number |
15K18428
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Tumor diagnostics
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Research Institution | Institute of Physical and Chemical Research (2017) Shinshu University (2015-2016) |
Principal Investigator |
Yamamoto Yoichiro 国立研究開発法人理化学研究所, 革新知能統合研究センター, ユニットリーダー (00573247)
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Project Period (FY) |
2015-04-01 – 2018-03-31
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Project Status |
Completed (Fiscal Year 2017)
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Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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Keywords | 人工知能 / 機械学習 / 乳癌 / 浸潤 / 形態情報 / 非浸潤癌 / ビッグデータ / Digital Pathology / Digital pathology |
Outline of Final Research Achievements |
We found that histological types of breast tumors could be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.
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Report
(4 results)
Research Products
(15 results)
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[Presentation] 人工知能がみた細胞像2015
Author(s)
山本陽一朗
Organizer
大阪病理研究会
Place of Presentation
大阪大学最先端医療イノベーションセンター
Year and Date
2015-12-19
Related Report
Invited
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