Imaging studies on the heterogeneity of breast cancer
| Project/Area Number |
16K10270
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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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| Research Field |
Radiation science
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| Research Institution | Dokkyo Medical University (2019)
Tokyo Medical and Dental University (2016-2018) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
藤岡 友之 東京医科歯科大学, 医学部附属病院, 講師 (60771631)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 乳癌 / 画像診断 / PET / MRI / 超音波 / サブタイプ / heterogeneity / 医療・福祉 / 癌 / ゲノム / 放射線 |
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| Outline of Final Research Achievements |
This study examined the relationship between FDG-PET/CT, MRI, and ultrasound imaging and breast cancer based on our database. Initially, imaging findings in triple negative breast cancer and its subclassifications were analyzed. Subsequently, we have performed a comprehensive analysis and review of breast cancer in general and presented the characteristics of each subtype in particular. In the middle of the research, we learned a method using deep learning in AI (artificial intelligence) and studied it. The results are presented in terms of the characteristics of imaging findings, especially FDG-PET for mucous carcinoma, and the application of AI to diagnostic imaging for benign malignancy differentiation.
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| Academic Significance and Societal Importance of the Research Achievements |
乳癌は遺伝学的にも多数の疾患の複合体であり、病理診断や遺伝学的検査での研究が進められ臨床応用されている。乳癌の画像所見も様々に異なっており、画像診断により病態がわかることで、手術や生検以外の方法で少ない侵襲で治療方針の変更にも繋げることができるため、社会的な意義も大きいと考えられる。また、新しい人工知能による研究手法も用いたことで、今後のさらなる研究の発展にも貢献できると考えられる。
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Report
(5 results)
Research Products
(8 results)
