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. 2019 Mar;290(3):621-628.
doi: 10.1148/radiol.2018180608. Epub 2018 Dec 11.

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set

Karen Drukker  1 , Maryellen L Giger  1 , Bonnie N Joe  1 , Karla Kerlikowske  1 , Heather Greenwood  1 , Jennifer S Drukteinis  1 , Bethany Niell  1 , Bo Fan  1 , Serghei Malkov  1 , Jesus Avila  1 , Leila Kazemi  1 , John Shepherd  1
Affiliations

Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set

Karen Drukker et al. Radiology. 2019 Mar.

Abstract

Purpose To investigate the combination of mammography radiomics and quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnecessary benign breast biopsies. Materials and Methods For this prospective study, dual-energy craniocaudal and mediolateral oblique mammograms were obtained immediately before biopsy in 109 women (mean age, 51 years; range, 31-85 years) with Breast Imaging Reporting and Data System category 4 or 5 breast masses (35 invasive cancers, 74 benign) from 2013 through 2017. The three quantitative compartments of water, lipid, and protein thickness at each pixel were calculated from the attenuation at high and low energy by using a within-image phantom. Masses were automatically segmented and features were extracted from the low-energy mammograms and the quantitative compartment images. Tenfold cross-validations using a linear discriminant classifier with predefined feature signatures helped differentiate between malignant and benign masses by means of (a) water-lipid-protein composition images alone, (b) mammography radiomics alone, and (c) a combined image analysis of both. Positive predictive value of biopsy performed (PPV3) at maximum sensitivity was the primary performance metric, and results were compared with those for conventional diagnostic digital mammography. Results The PPV3 for conventional diagnostic digital mammography in our data set was 32.1% (35 of 109; 95% confidence interval [CI]: 23.9%, 41.3%), with a sensitivity of 100%. In comparison, combined mammography radiomics plus quantitative 3CB image analysis had PPV3 of 49% (34 of 70; 95% CI: 36.5%, 58.9%; P < .001), with a sensitivity of 97% (34 of 35; 95% CI: 90.3%, 100%; P < .001) and 35.8% (39 of 109) fewer total biopsies (P < .001). Conclusion Quantitative three-compartment breast image analysis of breast masses combined with mammography radiomics has the potential to reduce unnecessary breast biopsies. © RSNA, 2018 Online supplemental material is available for this article.

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Figures

Figure 1:
Figure 1:
Images in 71-year-old woman with 1.6-cm invasive ductal carcinoma (Breast Imaging Reporting and Data System category 5, with category C breast density). Low-energy mammogram and corresponding regions of interest for mammogram (top) and breast tissue composition images (bottom two rows).
Figure 2:
Figure 2:
Flowchart of study participant enrollment. DCIS = ductal carcinoma in situ; site 1 = University of California, San Francisco, San Francisco, Calif; site 2 = H. Lee Moffitt Cancer Center, Tampa, Fla.
Figure 3:
Figure 3:
Region of interest from digital mammography depicts invasive cancer misclassified with mammography radiomics. Images in 50-year-old woman with invasive cancer (Breast Imaging Reporting and Data System category 4, with category C breast density) without (left) and with (right) radiologist and computer delineations (solid and dashed lines, respectively).
Figure 4:
Figure 4:
Region of interest from digital mammography depicts invasive cancer misclassified with quantitative three-compartment breast (3CB) analysis and combined mammography radiomics plus quantitative 3CB analysis. Images in 53-year-old woman with invasive cancer (Breast Imaging Reporting and Data System category 4, with category C breast density) without (left) and with (right) radiologist and computer delineations (solid and dashed lines, respectively).
Figure 5:
Figure 5:
Graph shows positive predictive value of biopsy performed at maximum attained sensitivity. Error bars represent 95% confidence intervals. combination = combined quantitative three-compartment breast (q3CB) analysis and mammography radiomics analysis, conventional = conventional diagnostic digital mammography, mammo = mammography.
Figure 6:
Figure 6:
Receiver operating characteristic analysis. Left, “raw staircase” receiver operating characteristic curves with operating points (no error bars for clarity) and, right, receiver operating characteristic curves fitted with the proper binormal model (30) with operating points. Error bars represent 95% confidence intervals. q3CB = quantitative three-compartment breast analysis.
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