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

Wang, Jeff; Kato, Fumi; Yamashita, Hiroko; Baba, Motoi; Cui, Yi; Li, Ruijiang; Oyama-Manabe, Noriko; Shirato, Hiroki

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

Wang, Jeff; Kato, Fumi; Yamashita, Hiroko et al.

Journal of digital imaging, 2017, 30(2), 215-227

Permalink : https://hdl.handle.net/2115/68664

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アクセス権	open access
URI	https://hdl.handle.net/2115/68664
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 ; Kato, Fumi ; Yamashita, Hiroko ; Baba, Motoi ; Cui, Yi ; Li, Ruijiang ; Oyama-Manabe, Noriko ; Shirato, Hiroki
著者名 Wang, Jeff
e-Rad_Researcher 80399865 著者名 Kato, Fumi
e-Rad_Researcher 70332947 著者名 Yamashita, Hiroko
著者名 Baba, Motoi
著者名 Cui, Yi
著者名 Li, Ruijiang
e-Rad_Researcher 70463742 著者名 Oyama-Manabe, Noriko
e-Rad_Researcher 20187537 著者名 Shirato, Hiroki
言語	英語
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	2017-04
出版者	Springer
収録物名	Journal of digital imaging
巻(号)	30(2)
ページ	215-227
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.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	The original publication is available at www.springerlink.com
PISSN	0897-1889
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1007/s10278-016-9922-9