The introduction of computer-aided detection into this practice was not associated with statistically significant changes in recall and breast cancer detection rates, both for the entire group of radiologists and for the subset of radiologists who interpreted high volumes of mammograms.

Purpose:To assess interpretation performance and radiation dose when two-dimensional synthesized mammography (SM) images versus standard full-field digital mammography (FFDM) images are used alone or in combination with digital breast tomosynthesis images. Materials and Methods:A fully crossed, mode-balanced multicase (n = 123), multireader (n = 8), retrospective observer performance study was performed by using deidentified images acquired between 2008 and 2011 with institutional review board approved, HIPAA-compliant protocols, during which each patient signed informed consent. The cohort included 36 cases of biopsy-proven cancer, 35 cases of biopsy-proven benign lesions, and 52 normal or benign cases (Breast Imaging Reporting and Data System [BI-RADS] score of 1 or 2) with negative 1-year follow-up results. Accuracy of sequentially reported probability of malignancy ratings and seven-category forced BI-RADS ratings was evaluated by using areas under the receiver operating characteristic curve (AUCs) in the random-reader analysis. Results:Probability of malignancy-based mean AUCs for SM and FFDM images alone was 0.894 and 0.889, respectively (difference, 20.005; 95% confidence interval [CI]: 20.062, 0.054; P = .85). Mean AUC for SM with tomosynthesis and FFDM with tomosynthesis was 0.916 and 0.939, respectively (difference, 0.023; 95% CI: 20.011, 0.057; P = .19). In terms of the reader-specific AUCs, five readers performed better with SM alone versus FFDM alone, and all eight readers performed better with combined FFDM and tomosynthesis (absolute differences from 0.003 to 0.052). Similar results were obtained by using a nonparametric analysis of forced BI-RADS ratings.

Purpose To compare radiologists’ performance during interpretation of screening mammograms in the clinic to their performance when reading the same examinations in a retrospective laboratory study. Materials and Methods This study was conducted under an Institutional Review Board approved HIPAA compliant protocol where informed consent was waived. Nine experienced radiologists rated an enriched set of examinations that they personally had read in the clinic (“reader-specific”) mixed with an enriched “common” set of examinations that none of the participants had read in the clinic, using a screening BI-RADS rating scale. The original clinical recommendations to recall the women for a diagnostic workup, or not, for both reader-specific and common sets were compared with their recommendations during the retrospective experiment. The results are presented in terms of reader-specific and group averaged “sensitivity” and “specificity” levels and the dispersion (spread) of reader-specific performance estimates. Results On average radiologists performed significantly better in the clinic as compared with their performance in the laboratory (p=0.035). Inter reader dispersion of the computed performance levels was significantly lower during the clinical interpretations (p<0.01). Conclusion Retrospective laboratory experiments may not represent well either expected performance levels or inter- reader variability during clinical interpretations of the same set of examinations in the clinical environment.

Rationale and Objectives Retrospectively compare interpretive performance of synthetically reconstructed two-dimensional images in combination with DBT versus FFDM plus DBT. Materials and Methods Ten radiologists trained in reading tomosynthesis examinations interpreted retrospectively, under two modes, 114 mammograms. One mode included the directly acquired FFDM combined with DBT and the other, synthetically reconstructed projection images combined with DBT. The reconstructed images do not require additional radiation exposure. We compared the two modes with respect to “sensitivity”, namely recommendation to recall a breast with either a pathology proven cancer (n=48) or a high risk lesion (n=6); and “specificity”, namely no recommendation to recall a breast not depicting an abnormality (n=144) or depicting only benign abnormalities (n=30). Results The average sensitivity for FFDM with DBT was 0.826 versus 0.772 for synthetic FFDM with DBT (difference=0.054, p=0.017 and p=0.053 for fixed and random reader effect, respectively). The fraction of breasts with no, or benign, abnormalities recommended to be recalled were virtually the same: 0.298 and 0.297 for the two modalities, respectively (95% confidence intervals for the difference CI= −0.028, 0.036 and CI = −0.070, 0.066 for fixed and random reader effects, correspondingly). Sixteen additional clusters of micro-calcifications (“positive” breasts) were missed by all readers combined when interpreting the mode with synthesized images versus FFDM. Conclusion Lower sensitivity with comparable specificity was observed with the tested version of synthetically generated images versus FFDM, both combined with DBT. Improved synthesized images with experimentally verified acceptable diagnostic quality will be needed to eliminate double exposure during DBT based screening.

In this study, we developed and tested a new multiview-based computer-aided detection (CAD) scheme that aims to maintain the same case-based sensitivity level as a single-image-based scheme while substantially increasing the number of masses being detected on both ipsilateral views. An image database of 450 four-view examinations (1800 images) was assembled. In this database, 250 cases depicted malignant masses, of which 236 masses were visible on both views and 14 masses were visible only on one view. First, we detected suspected mass regions depicted on each image in the database using a single-image-based CAD. For each identified region (with detection score > or = 0.55), we then identified a matching strip of interest on the ipsilateral view based on the projected distance to the nipple along the centerline. By lowering CAD operating threshold inside the matching strip, we searched for a region located inside the strip and paired it with the original region. A multifeature-based artificial neural network scored the likelihood of the paired "matched" regions representing true-positive masses. All single (unmatched) regions except for those either with very high detection scores (> or = 0.85) or those located near the chest wall that cannot be matched on the other view were discarded. The original single-image-based CAD scheme detected 186 masses (74.4% case-based sensitivity) and 593 false-positive regions. Of the 186 identified masses, 91 were detected on two views (48.9%) and 95 were detected only on one view (51.1%). Of the false-positive detections, 54 were paired on the ipsilateral view inside the corresponding matching strips and the remaining 485 were not, which represented 539 case-based false-positive detections (0.3 per image). Applying the multiview-based CAD scheme, the same case-based sensitivity was maintained while cueing 169 of 186 masses (90.9%) on both views and at the same time reducing the case-based false-positive detection rate by 23.7% (from 539 to 411). The study demonstrated that the new multiview-based CAD scheme could substantially increase the number of masses being cued on two ipsilateral views while reducing the case-based false-positive detection rate.