Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations.

By T. M. Kolb, J. Lichy, J. Newhouse. Radiology, 2002

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Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach (2014, background): “Depending on the density of the breasts radiologists may miss up to 30% of breast cancers [3].”
Detecting and classifying lesions in mammograms with Deep Learning (2017, background): “The sensitivity of screening mammography is reported to be between 80-90% [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20].”
Computer-Aided Detection and Diagnosis in Mammography (2005, background): “As a result, radiologists fail to detect 10-30% of cancers [7-9].”
Awareness and current knowledge of breast cancer (2017, background): “Physical examination includes inspection of breasts, area around neck and collarbone, and armpits (axillae) carried out by clinicians [108].”
Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. (2010, neutral): “6 cancers per 1,000 women [70-74].”
Acoustic Metamaterials: Negative Refraction, Imaging, Lensing and Cloaking (2013, background): “the waves are evanescent [17, 22, 36, 39]), localized or defect modes (an exponen-”