Automated Breast Density Measurements From Chest Computed Tomography Scans.

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Qureshi, TA; Veeraraghavan, H; Sung, JS; Kaplan, JB; Flynn, J; Tonorezos, ES; Wolden, SL; Morris, EA; Oeffinger, KC; Pike, MC; Moskowitz, CS

Published Date

  • June 22, 2019

Published In

Volume / Issue

  • 43 / 8

Start / End Page

  • 242 -

PubMed ID

  • 31230138

Pubmed Central ID

  • 31230138

Electronic International Standard Serial Number (EISSN)

  • 1573-689X

Digital Object Identifier (DOI)

  • 10.1007/s10916-019-1363-9

Language

  • eng

Conference Location

  • United States