Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description.

By Jay A. Baker, P. Kornguth, C. Floyd. 1996

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A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “We use Cohen κ to compare our model’s agreement to the agreement of physicians with each other from previous studies [37, 1, 5, 39, 31].”
Breast imaging reporting and data system (BI-RADS). (2002, methodology): “Observer variability in the use of the BI-RADS lexicon was first evaluated by Baker et al [24].”
Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a European consensus meeting. (2011, background): “These recommendations conferred at least two benefits: reduced interobserver variability [20,21] and improved positive predictive value for obtaining pathology from breast biopsy [21].”
Measurement of observer agreement. (2003, methodology): “This is demonstrated in Equation (1) as follows: po pe 1 pe . ( 1 )”
BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value. (2006, result): “Agreement for calcification description on mammograms was somewhat lower in our study than in prior studies (11,12).”