Mammographic quantitative image analysis and biologic image composition for breast lesion characterization and classification

Abstract

Purpose: To investigate whether biologic image composition of mammographic lesions can improve upon existing mammographic quantitative image analysis (QIA) in estimating the probability of malignancy.

Methods: The study population consisted of 45 breast lesions imaged with dual-energy mammography prior to breast biopsy with final diagnosis resulting in 10 invasive ductal carcinomas, 5 ductal carcinomain situ, 11 fibroadenomas, and 19 other benign diagnoses. Analysis was threefold: (1) The raw low-energy mammographic images were analyzed with an established in-house QIA method, "QIA alone," (2) the three-compartment breast (3CB) composition measure-derived from the dual-energy mammography-of water, lipid, and protein thickness were assessed, "3CB alone", and (3) information from QIA and 3CB was combined, "QIA + 3CB." Analysis was initiated from radiologist-indicated lesion centers and was otherwise fully automated. Steps of the QIA and 3CB methods were lesion segmentation, characterization, and subsequent classification for malignancy in leave-one-case-out cross-validation. Performance assessment included box plots, Bland-Altman plots, and Receiver Operating Characteristic (ROC) analysis.

Results: The area under the ROC curve (AUC) for distinguishing between benign and malignant lesions (invasive and DCIS) was 0.81 (standard error 0.07) for the "QIA alone" method, 0.72 (0.07) for "3CB alone" method, and 0.86 (0.04) for "QIA+3CB" combined. The difference in AUC was 0.043 between "QIA + 3CB" and "QIA alone" but failed to reach statistical significance (95% confidence interval [-0.17 to + 0.26]).

Conclusions: In this pilot study analyzing the new 3CB imaging modality, knowledge of the composition of breast lesions and their periphery appeared additive in combination with existing mammographic QIA methods for the distinction between different benign and malignant lesion types.

Figure 1

Example images of (a) an invasive ductal carcinoma (IDC) and (b) a fibroadenoma (FA): Full field CC view (low-energy mammogram) and regions-of-interest of the (1) low-energy x-ray mammogram, (2) water, (3) lipid, and (4) protein images without (top) and with (bottom) the computer-determined lesion boundaries (dashed line), radiologist manual outline (solid line, on low-energy mammogram only), and radiologist-indicated lesion center (“x”). Computer-determined lesion boundaries were used in all calculations presented here.

Figure 2

Boxplots of (a) a QIA feature and (b) a 3CB feature [standardized (“zscore”) to zero mean and unit standard deviation σ] selected in stepwise feature selection in the task of distinguishing between malignant and benign lesions. A horizontal line in each box indicates the median value, the box boundaries denote the 25th and 75th percentiles, the whiskers indicate the range excluding outliers, and “+”s mark individual outliers.

Figure 3

The computer-estimated probability of malignancy in leave-one-case-out cross-validation (a) obtained by “3CB alone” vs “QIA alone” analyses, (b) in a Bland–Altman plot of “QIA+3CB” and “QIA alone” analyses, and (c) as the relative shift of “QIA+3CB” with respect to “QIA alone.”

Figure 4

Areas under the ROC curve (AUC) for individual features selected in stepwise feature selection and features merged by a neural net in a leave-one-case-out cross-validation for (a) “QIA alone” and (b) “3CB alone.”

Figure 5

ROC curves for leave-one-case-out cross-validation for “QIA alone” (AUC = 0.81 ± 0.07), “3CB alone” (AUC = 0.72 ± 0.07), and “QIA+3CB” (AUC = 0.86 ± 0.04).