Paper
22 March 2016 DICOM organ dose does not accurately represent calculated dose in mammography
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Abstract
This study aims to analyze the agreement between the mean glandular dose estimated by the mammography unit (organ dose) and mean glandular dose calculated using Dance et al published method (calculated dose). Anonymised digital mammograms from 50 BreastScreen NSW centers were downloaded and exposure information required for the calculation of dose was extracted from the DICOM header along with the organ dose estimated by the system. Data from quality assurance annual tests for the included centers were collected and used to calculate the mean glandular dose for each mammogram. Bland-Altman analysis and a two-tailed paired t-test were used to study the agreement between calculated and organ dose and the significance of any differences. A total of 27,869 dose points from 40 centers were included in the study, mean calculated dose and mean organ dose (± standard deviation) were 1.47 (±0.66) and 1.38 (±0.56) mGy respectively. A statistically significant 0.09 mGy bias (t = 69.25; p<0.0001) with 95% limits of agreement between calculated and organ doses ranging from -0.34 and 0.52 were shown by Bland-Altman analysis, which indicates a small yet highly significant difference between the two means. The use of organ dose for dose audits is done at the risk of over or underestimating the calculated dose, hence, further work is needed to identify the causal agents for differences between organ and calculated doses and to generate a correction factor for organ dose.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Moayyad E. Suleiman, Patrick C. Brennan, and Mark F. McEntee "DICOM organ dose does not accurately represent calculated dose in mammography", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97832L (22 March 2016); https://doi.org/10.1117/12.2216698
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KEYWORDS
Mammography

Breast

Digital mammography

Error analysis

Cancer

Systems modeling

Breast cancer

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