Mammographic density estimation: Comparison among BI-RADS categories, a semi-automated software and a fully automated one

Abstract

Although breast density is considered a strong predictor of breast cancer risk, its quantitative assessment is difficult. The aim of this study is to demonstrate that breast density assessment with a fully automated software is feasible and correlates with the semi-automated evaluation and the quantitative BI-RADS standards. A data set of 160 mammograms was evaluated by three blinded radiologists. Intra-observer (reader 1: k=0.71; reader 2: k=0.76; reader 3: k=0.62) and inter-observer (reader 1 vs reader 2: k=0.72; reader 2 vs reader 3: k=0.80; reader 3 vs reader 1: k=0.72) variability for the semi-automated software were good on a four-grade scale (D1/D2/D3/D4) and correlated with BI-RADS evaluation made by other two blinded radiologists (r=0.65, p<0.01). Inter-observer (reader 1 vs reader 2: k=0.85; reader 2 vs reader 3: k=0.91; reader 3 vs reader 1: k=0.85) variability for the semi-automated software was very good on a two-grade scale (D1-D2/D3-D4). The use of the fully automated software eliminated intra- and inter-observer differences, correlated with BI-RADS categories (r=0.62, p<0.01) and can replace the semi-automated one (Bland-Altman statistics). Our study demonstrates that automated estimation of breast density is feasible and eliminates subjectivity. Furthermore both the semi-automated and the fully automated density estimation are more accurate than BI-RADS quantitative evaluation and could also be used in the daily clinical practice.

Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation

“…In this regard, our methodology was similar to that in other studies examining interreader performance in diagnostic imaging with and without an intervention such as computer-aided diagnostic software. Parallel methodology has been used in lung disease, [39][40][41][42][43] breast imaging, 29,[44][45][46] and Alzheimer disease, 26,47 without determining intraobserver variability.…”

An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance

“…An improved understanding of the visual assessment processes used by radiologists can help to discover the possible reasons behind MD reporting inconsistencies that are widely found within the literature [12][13][14][15][16][17][18][19][20]. Although radiologists within clinical practice only visually assess MD, they do have their own opinions regarding what kinds of tissue appearance should be regarded as MD.…”

“…However, studies investigating the consistency of radiologist MD reporting only show moderate inter-reader agreement [12][13][14][15][16][17][18][19][20]. Computer-aided methods to measure MD are currently developed [21][22][23][24] but to date are not widely used clinically, as they still require robust validation [14,25].…”

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“…A number of automated or semi-automated methods have been proposed to identify mammographic density using either a threshold based method, such as those described above, or fractal analysis or other texturebased techniques (78)(79)(80)(81)(82)(83)(84)(85)(86). However, so far none of these methods have become widely used.…”