Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST).

PURPOSE: To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts. METHODS: In an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results. RESULTS: Mean participant age was 54.8 years (range, 40-75 years). Across 17,552 screens, there were 126 cancer events in 125 women (7.2/1,000; 95% CI, 5.9 to 8.4). In year 1, DBT-alone cancer yield was 5.0/1,000, and of DBT+US, 6.3/1,000, difference 1.3/1,000 (95% CI, 0.3 to 2.1; P = .005). In years 2 + 3, DBT cancer yield was 4.9/1,000, and of DBT+US, 5.9/1,000, difference 1.0/1,000 (95% CI, 0.4 to 1.5; P < .001). False-positive rate increased from 7.0% for DBT in year 1 to 11.5% for DBT+US and from 5.9% for DBT in year 2 + 3 to 9.7% for DBT+US (P < .001 for both). Nine cancers were seen only by double reading DBT and one by double reading US. Ten interval cancers (0.6/1,000 [95% CI, 0.2 to 0.9]) were identified. Despite reduction in specificity, addition of US improved receiver operating characteristic curves, with area under receiver operating characteristic curve increasing from 0.83 for DBT alone to 0.92 for DBT+US in year 1 (P = .01), with smaller improvements in subsequent years. Of 6,179 women, across all 3 years, 172/6,179 (2.8%) unique women had a false-positive biopsy because of DBT as did another 230/6,179 (3.7%) women because of US (P < .001). CONCLUSION: Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.

Computerized assessment of tissue composition on digitized mammograms.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.

Observer performance studies: detection of single versus multiple abnormalities of the chest.

OBJECTIVE: We used receiver operating characteristic (ROC) analysis to compare two methods of evaluating observer performance in detecting an abnormality on chest radiographs. In the first method, the abnormality in question, rib fracture, was one of five investigated, and it was the only one of interest in the second. MATERIALS AND METHODS: Eight experienced observers viewed 117 posteroanterior chest radiographs in two interpretation modes. Fifty-four of these images depicted rib fractures that had been rated as subtle for detection. The likelihood of the presence of a rib fracture was rated as one of five abnormalities in question in one mode and the sole abnormality of interest in the other mode. RESULTS: Six of the observers performed better during the single-abnormality mode, one performed equally well in both modes, and one performed better during the multiple-abnormality mode. The average area under the ROC curves (A(z)) was 0.73 +/- 0.07 for the multiple-abnormality mode and 0.80 +/- 0.04 for the single-abnormality mode. The results were significantly different (p < 0.05). CONCLUSION: Study methodology can significantly affect the results in ROC studies, particularly for abnormalities that may not be perceived as primary or important. The order in which abnormalities appear on a checklist report form may be important.

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography.

OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.