Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: automated measurement development for full field digital mammography

Abstract

Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for standardized mammographic reporting and are assessed visually. This reporting is clinically relevant because breast composition can impact mammographic sensitivity and is a breast cancer risk factor. New techniques are presented and evaluated for generating automated BI-RADS breast composition descriptors using both raw and calibrated full field digital mammography (FFDM) image data.

Methods: A matched case-control dataset with FFDM images was used to develop three automated measures for the BI-RADS breast composition descriptors. Histograms of each calibrated mammogram in the percent glandular (pg) representation were processed to create the new BR(pg) measure. Two previously validated measures of breast density derived from calibrated and raw mammograms were converted to the new BR(vc) and BR(vr) measures, respectively. These three measures were compared with the radiologist-reported BI-RADS compositions assessments from the patient records. The authors used two optimization strategies with differential evolution to create these measures: method-1 used breast cancer status; and method-2 matched the reported BI-RADS descriptors. Weighted kappa (κ) analysis was used to assess the agreement between the new measures and the reported measures. Each measure's association with breast cancer was evaluated with odds ratios (ORs) adjusted for body mass index, breast area, and menopausal status. ORs were estimated as per unit increase with 95% confidence intervals.

Results: The three BI-RADS measures generated by method-1 had κ between 0.25-0.34. These measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.87 (1.34, 2.59) for BR(pg); (b) OR = 1.93 (1.36, 2.74) for BR(vc); and (c) OR = 1.37 (1.05, 1.80) for BR(vr). The measures generated by method-2 had κ between 0.42-0.45. Two of these measures were significantly associated with breast cancer status in the adjusted models: (a) OR = 1.95 (1.24, 3.09) for BR(pg); (b) OR = 1.42 (0.87, 2.32) for BR(vc); and (c) OR = 2.13 (1.22, 3.72) for BR(vr). The radiologist-reported measures from the patient records showed a similar association, OR = 1.49 (0.99, 2.24), although only borderline statistically significant.

Conclusions: A general framework was developed and validated for converting calibrated mammograms and continuous measures of breast density to fully automated approximations for the BI-RADS breast composition descriptors. The techniques are general and suitable for a broad range of clinical and research applications.

Figure 1

Image examples: The top row shows four clinical display mammograms. We refer to these as examples 1–4. We use these as surrogates for the raw images for viewing purposes only because they are more easily displayed than the raw images. From left to right, the case-report BI-RADS categories are 1, 2, 3, and 4. The bottom row shows the corresponding images in the calibrated percent glandular format with 25% erosion.

Figure 2

Calibrated histogram examples: This shows histograms from the four calibrated mammogram examples shown in Fig. 1: (1) example 1 with a solid line; (2) example 2 with short dashes; (3) example 3 with dashes and dots; and (4) example 4 with long dashes. The x-axis represents calibrated pixel values (x = percent glandular quantities) and the y axis is the relative normalized frequency. These normalized histograms approximate the probability distributions for each image.

Figure 3

BR_pg measure examples from optimization method-1. The x-axis represents calibrated pixel values (x = percent glandular quantities). This shows the cumulative distributions determined from the histograms shown in Fig. 2 for the four patient examples. The BR_pg processing with optimization method-1 categorized these examples as follows using x_c = 23 (vertical dashed line) and [q, r, s,] ≈ [0.987, 0.700, 0.228]: (1) example 1 was placed in category 1 denoted with a solid line; (2) example 2 was placed in category 2 denoted with short dashes; (3) example 3 was placed in category 3 denoted with dashes and dots; and (4) example 4 was placed in category 4 denoted with long dashes.

Figure 4

The V_r population distribution for the entire case-control dataset and BR_vr measurement cutoffs derived with optimization method-1: The vertical dashed lines from left to right show the cut-point parameter values for the BR_vr measure with [a, b, c] ≈ [71.9, 151.1, 207.5].