2021 Fiscal Year Final Research Report
Prognostic Prediction of Breast Cancer Using Artificial Intelligence with Radiomics Features
Project/Area Number |
19K20719
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Research Category |
Grant-in-Aid for Early-Career Scientists
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Allocation Type | Multi-year Fund |
Review Section |
Basic Section 90130:Medical systems-related
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Research Institution | Ritsumeikan University |
Principal Investigator |
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Project Period (FY) |
2019-04-01 – 2022-03-31
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Keywords | Radiomics / ディープラーニング / 乳房MRI画像 |
Outline of Final Research Achievements |
The purpose of this study was to develop a computerized classification method for triple negative breast cancer from breast MRI (magnetic resonance imaging) images using support vector machine (SVM) with radiomics features. We also conducted the prognostic prediction from breast MRI images using a cox proportional hazard model with radiomics features. Our database consisted of T1 weighted images, T2 weighted images, and dynamic MRI images obtained from 66 patients. The classification accuracy, sensitivity, specificity and area under the ROC curve of the proposed method using SVM with radiomics features were 84.8%, 81.3%, 86.0%, and 0.874, respectively.
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Free Research Field |
医用画像処理
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Academic Significance and Societal Importance of the Research Achievements |
従来のCAD(Computer-aided diagnosis)システムの多くは,乳房MRI画像上の病変の存在診断や鑑別診断の支援が目的であった.したがって,従来のCADシステムでは,医師が患者の治療方針を決定するための指標(遺伝子変異など)までを提示することができなかった.そこで本研究では,乳房MRI画像から得られるテクスチャ情報などのRadiomics特徴量と機械学習により,トリプルネガティブ乳がんの推定法の開発と生存予測の検討を行った.提案手法の結果を医師が参考にすることで,治療方針を決定する際の有用な情報となる可能性がある.
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