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Automated Breast Density Measurements From Chest Computed Tomography Scans

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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.

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Abbreviations

ACR:

American College of Radiology

BI-RADS:

Breast Imaging Reporting and Data System

CT:

Computed Tomography

FPF:

False positive fraction

GE:

General Electric

HL:

Hodgkin lymphoma

IQR:

Interquartile range

TPF:

True positive fraction

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Funding

Funding for this work came from the Meg Berté Owen Foundation and from NCI grant P30 CA008748.

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Authors and Affiliations

  1. Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 8700 Beverly Blvd, Pact 400, Los Angeles, CA, 90048, USA

    Touseef A. Qureshi

  2. Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 485 Lexington Avenue, New York, NY, 10017, USA

    Harini Veeraraghavan

  3. Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA

    Janice S. Sung, Jennifer B. Kaplan & Elizabeth A. Morris

  4. Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA

    Jessica Flynn, Malcolm C. Pike & Chaya S. Moskowitz

  5. Memorial Sloan Kettering Cancer Center, Department of Medicine, 485 Lexington Avenue, New York, NY, USA

    Emily S. Tonorezos

  6. Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, 1275 York Avenue, New York, NY, 10065, USA

    Suzanne L. Wolden

  7. Department of Medicine, Duke University, 2424 Erwin Dr, Suite, e 601, Durham, NC, 27705, USA

    Kevin C. Oeffinger

Authors
  1. Touseef A. Qureshi
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  2. Harini Veeraraghavan
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  3. Janice S. Sung
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  4. Jennifer B. Kaplan
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  5. Jessica Flynn
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  6. Emily S. Tonorezos
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  7. Suzanne L. Wolden
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  8. Elizabeth A. Morris
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  9. Kevin C. Oeffinger
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  10. Malcolm C. Pike
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  11. Chaya S. Moskowitz
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Corresponding author

Correspondence to Chaya S. Moskowitz.

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Conflict of interests

TA Qureshi, H Veeraraghavan, JB Kaplan, J Flynn, ES Tonorezos, KC Oeffinger, MC Pike, and CS Moskowitz declare no conflicts of interest. JS Sung has received research grants from Hologic and GE. SL Wolden has received honoraria from YmAbs. EA Morris has received research grants from GRAIL.

Ethical approval

This study used archived images and does not contain any studies with humans or animals performed by any of the authors.

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Informed consent was waived by our institutional review board.

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Qureshi, T.A., Veeraraghavan, H., Sung, J.S. et al. Automated Breast Density Measurements From Chest Computed Tomography Scans. J Med Syst 43, 242 (2019). https://doi.org/10.1007/s10916-019-1363-9

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