Comparison of variability in breast density assessment by BI-RADS category according to the level of experience

Abstract

Background Only few studies have assessed variability in the results obtained by the readers with different experience levels in comparison with automated volumetric breast density measurements. Purpose To examine the variations in breast density assessment according to BI-RADS categories among readers with different experience levels and to compare it with the results of automated quantitative measurements. Material and Methods Density assignment was done for 1000 screening mammograms by six readers with three different experience levels (breast-imaging experts, general radiologists, and students). Agreement level between the results obtained by the readers and the Volpara automated volumetric breast density measurements was assessed. The agreement analysis using two categories-non-dense and dense breast tissue-was also performed. Results Intra-reader agreement for experts, general radiologists, and students were almost perfect or substantial (k = 0.74-0.95). The agreement between visual assessments of the breast-imaging experts and volumetric assessments by Volpara was substantial (k = 0.77). The agreement was moderate between the experts and general radiologists (k = 0.67) and slight between the students and Volpara (k = 0.01). The agreement for the two category groups (nondense and dense) was almost perfect between the experts and Volpara (k = 0.83). The agreement was substantial between the experts and general radiologists (k = 0.78). Conclusion We observed similar high agreement levels between visual assessments of breast density performed by radiologists and the volumetric assessments. However, agreement levels were substantially lower for the untrained readers.

Comparison of breast density assessments according to BI-RADS 4th and 5th editions and experience level

“…However, there was no evidence indicating that the consistency changed as a result of different experience levels. Eom et al (31) conducted their study, which used similar methodology with six readers and three different levels of experience (two breast imaging experts, two general radiologists, and two medical students) and showed substantial to near-perfect intra-reader agreement when using the BI-RADS 5th edition. They also found no statistically significant difference with regard to the different levels of experience for the intra-reader agreement.…”

“…They also found no statistically significant difference with regard to the different levels of experience for the intra-reader agreement. Additionally, they studied the agreement between the Volpara automated volumetric breast density measurements and those obtained using the BI-RADS; they found that expert radiologists showed higher consistency with regard to the volumetric and qualitative assessments than did the students (31).…”

Segmentation and Classification of Bi-Rads Medical Images with the Imaging Biomarkers According To Level of Detail

“…By using 2D mammograms MDs breasts are classified based on thorax density as: (a) entirely fatty, (b) scattered areas of fibroglandular density, (c) heterogeneously dense, and (d) extremely dense. It is important to note that this method is based on MDs in 2D measures, and that it can be wrong if it is done in 3D systems [16][17][18]. Because of this, all the results have been done inside a 2D system, and measured in 2D working domain.…”

Review of quantitative multiscale imaging of breast cancer

“…This visual assessment is clinically useful, but it can vary based on the physician's training and can be inaccurate compared with 3-D measures. [252][253][254] This leaves greater chance for error in patient care. [252][253][254] By comparison, a quantitative imaging biomarker (QIB) is an objective characteristic derived from an in vivo image measured on a ratio or interval.…”

“…[252][253][254] This leaves greater chance for error in patient care. [252][253][254] By comparison, a quantitative imaging biomarker (QIB) is an objective characteristic derived from an in vivo image measured on a ratio or interval. 8 CADe and CADx algorithms use QIB, and trials have demonstrated that they can improve radiologist performance.…”