Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter-and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (?) and Student's t-test were used to assess the model and reader performance. The model achieved a ?of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a ?of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (? improved from 0.70 to 0.88, p < 0.001) and intra-reader variability (? improved from 0.83 to 0.95, p < 0.01) for four-class density, and significant reduction in inter-reader variability (? improved from 0.77 to 0.96, p < 0.001) and intra-reader variability (? improved from 0.89 to 0.97, p < 0.01) for binary nondense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions.

Automated summary

Breast density is a significant risk factor for breast cancer, but inconsistent reporting by radiologists can cause confusion. A deep learning model for mammographic density grading was tested in a study involving 928 image pairs. The model improved inter- and intra-reader variability, reduced reading time, and achieved high accuracy in density assessment.