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-patt...
| Published in: | Journal of Digital Imaging Vol. 30; no. 2; pp. 215 - 228 |
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| Main Authors: | , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121962657&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121962657 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2017 vid: 30 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121962657 121962657 144125630 121962657 10.1007/s10278-016-9922-9 121962657 ppf: 215 ppct: 13 formats: fmt: @attributes: type: P tig: atl: 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. aug: au: Wang, Jeff Kato, Fumi Yamashita, Hiroko Baba, Motoi Cui, Yi Li, Ruijiang Oyama-Manabe, Noriko Shirato, Hiroki affil: Department of Diagnostic and Interventional Radiology , Hokkaido University Hospital , North 14 West 5 Kita-ku Sapporo 060-8648 Japan sug: subj: Breast Neoplasms Calibration Mammography Neural Networks (Computer) Breast Tissue Density Algorithms Female Human Phantoms, Imaging Risk Assessment Middle Age Aged Magnetic Resonance Imaging Middle Aged: 45-64 years Aged: 65+ years Female ab: 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 R of 0.993. Intra-patient validations ranged from R of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 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. 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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