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.
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. 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. Phantom validations reached an R2of 0.993. Intra-patient validations ranged from R2of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R2ranging 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. 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.
Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Jeff
J
Wang
Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan. fumikato@med.hokudai.ac.jp.
Fumi
F
Kato
Department of Breast Surgery, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Hiroko
H
Yamashita
Department of Breast Surgery, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Motoi
M
Baba
Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Yi
Y
Cui
Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.|Department of Radiation Oncology, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305, USA.
Ruijiang
R
Li
Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.|Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Noriko
N
Oyama-Manabe
Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.
Hiroki
H
Shirato
D001940:Breast / Q000000981:diagnostic imaging* / Q000473:pathology; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002138:Calibration; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007564:Japan; D008327:Mammography / Q000379:methods*; D016571:Neural Networks, Computer; D009929:Organ Size; D015203:Reproducibility of Results; D012189:Retrospective Studies
Artificial neural networks (ANN); Breast tissue density; Computer analysis; Full-field digital mammography (FFDM); Image processing; Imaging phantoms; Machine learning; Magnetic resonance imaging