Abstract
Observations of significant variability in radiologists’ classification of breast density signals the need for objective classification methods. In this study, we develop a model for a radiologist’s BI-RADS classification based on the volumetric glandularity image measured by spectral mammography and a reader study where ten MQSA certified radiologists assigned BI-RADS scores to 300 screening cases. Several combinations of features such as area glandularity based on a certain volumetric glandularity threshold, breast thickness and the spread of glandular tissue were tested as linear classifier parameters. Logistic regression was used to optimize the parameters and cross-validation to assess the agreement with the radiologists’ majority vote, regarded as truth. We show a clear indication that the automatic classification algorithm performs on par with or better than the average individual radiologist.
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Johansson, H., von Tiedemann, M., Cederström, B. (2014). Breast Density Classification Based on Volumetric Glandularity Measured by Spectral Mammography. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_35
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DOI: https://doi.org/10.1007/978-3-319-07887-8_35
Publisher Name: Springer, Cham
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