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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography : Feasibility on Japanese Women With and Without Breast Cancer

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タイトル: Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography : Feasibility on Japanese Women With and Without Breast Cancer
著者: Wang, Jeff 著作を一覧する
Kato, Fumi 著作を一覧する
Yamashita, Hiroko 著作を一覧する
Baba, Motoi 著作を一覧する
Cui, Yi 著作を一覧する
Li, Ruijiang 著作を一覧する
Oyama-Manabe, Noriko 著作を一覧する
Shirato, Hiroki 著作を一覧する
キーワード: Artificial neural networks (ANN)
Breast tissue density
Computer analysis
Full-field digital mammography (FFDM)
Magnetic resonance imaging
Image processing
Machine learning
Imaging phantoms
発行日: 2017年 4月
出版者: Springer
誌名: Journal of digital imaging
巻: 30
号: 2
開始ページ: 215
終了ページ: 227
出版社 DOI: 10.1007/s10278-016-9922-9
抄録: Background: Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating Full-Field Digital Mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. Methods: ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Results: Phantom validations reached an R2 of 0.993. Intra-patient validations ranged from R2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. Conclusions: ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.
Rights: The original publication is available at www.springerlink.com
資料タイプ: article (author version)
URI: http://hdl.handle.net/2115/68664
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: 加藤 扶美

 

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