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Qureshi TA, Veeraraghavan H, Sung JS, Kaplan JB, Flynn J, Tonorezos ES, Wolden SL, Morris EA, Oeffinger KC, Pike MC, Moskowitz CS. Automated Breast Density Measurements From Chest Computed Tomography Scans. J Med Syst 2019; 43:242. [PMID: 31230138 DOI: 10.1007/s10916-019-1363-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 05/30/2019] [Indexed: 10/26/2022]
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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Affiliation(s)
- Touseef A Qureshi
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 8700 Beverly Blvd, Pact 400, Los Angeles, CA, 90048, USA
| | - Harini Veeraraghavan
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 485 Lexington Avenue, New York, NY, 10017, USA
| | - Janice S Sung
- Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA
| | - Jennifer B Kaplan
- Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA
| | - Jessica Flynn
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA
| | - Emily S Tonorezos
- Memorial Sloan Kettering Cancer Center, Department of Medicine, 485 Lexington Avenue, New York, NY, USA
| | - Suzanne L Wolden
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, 1275 York Avenue, New York, NY, 10065, USA
| | - Elizabeth A Morris
- Memorial Sloan Kettering Cancer Center, Department of Radiology, 1275 York Avenue, New York, NY, 10065, USA
| | - Kevin C Oeffinger
- Department of Medicine, Duke University, 2424 Erwin Dr, Suite, e 601, Durham, NC, 27705, USA
| | - Malcolm C Pike
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA
| | - Chaya S Moskowitz
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, 485 Lexington Avenue, New York, NY, 10017, USA.
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