Automated Breast Density Measurements From Chest Computed Tomography Scans.
To develop an automated method for quantifying percent breast density from chest computed tomography (CT) scans. A naïve Bayesian classifier based on gray-level intensities and spatial relationships was developed on CT scans from 10 patients diagnosed with Hodgkin lymphoma (HL) and imaged as part of...
| Published in: | Journal of Medical Systems Vol. 43; no. 8 |
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| Main Authors: | , , , , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2019
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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=137490026&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490026 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490026 137490026 137490026 10.1007/s10916-019-1363-9 137490026 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Breast Density Measurements From Chest Computed Tomography Scans. aug: au: Qureshi, Touseef A. Veeraraghavan, Harini Sung, Janice S. Kaplan, Jennifer B. Flynn, Jessica Tonorezos, Emily S. Wolden, Suzanne L. Morris, Elizabeth A. Oeffinger, Kevin C. Pike, Malcolm C. Moskowitz, Chaya S. affil: Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 8700 Beverly Blvd, Pact 400, 90048, Los Angeles, CA, USA sug: subj: Breast Tissue Density Radiography Radiography, Thoracic Methods Tomography, X-Ray Computed Automation Human Female Adult Probability Methods Algorithms Correlation Coefficient Confidence Intervals Descriptive Statistics Hodgkin's Disease Radiology Information Systems Adult: 19-44 years Female ab: To develop an automated method for quantifying percent breast density from chest computed tomography (CT) scans. A naïve Bayesian classifier based on gray-level intensities and spatial relationships was developed on CT scans from 10 patients diagnosed with Hodgkin lymphoma (HL) and imaged as part of routine clinical care. The algorithm was validated on CT scans from 75 additional HL patients. The classifier was developed and validated using a reference dataset with consensus manual segmentation of fibroglandular tissue. Accuracy was evaluated at the pixel-level to examine how well the algorithm identified pixels with fibroglandular tissue using true and false positive fractions (TPF and FPF, respectively). Quantitative estimates of the patient-level CT percent density were contrasted to each other using the concordance correlation coefficient, ρc, and to subjective ACR BI-RADS density assessments using Kendall's τb. The pixel-level TPF for identifying pixels with fibroglandular tissue was 82.7% (interquartile range of patient-specific TPFs 65.5%-89.6%). The pixel-level FPF was 9.2% (interquartile range of patient-specific FPFs 2.5%-45.3%). Patient-level agreement of the algorithm's automated density estimate with that obtained from the reference dataset was high, ρc = 0.93 (95% CI 0.90-0.96) as was agreement with a radiologist's subjective ACR-BI-RADS assessments, τb = 0.77. It is possible to obtain automated measurements of percent density from clinical CT scans. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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