Objectives Compare four groups being screened: women without breast implants undergoing digital mammography (DM), women without breast implants undergoing DM with digital breast tomosynthesis (DM/DBT), women with implants undergoing DM, and women with implants undergoing DM/DBT. Methods Mammograms from February 2011 to March 2017 were retrospectively reviewed after 13,201 were excluded for a unilateral implant or prior breast cancer. Patients had been allowed to choose between DM and DM/DBT screening. Mammography performance metrics were compared using chi-square tests. Results Six thousand forty-one women with implants and 91,550 women without implants were included. In mammograms without implants, DM ( n = 113,973) and DM/DBT ( n = 61,896) yielded recall rates (RRs) of 8.53% and 6.79% (9726/113,973 and 4204/61,896, respectively, p < .001), cancer detection rates per 1000 exams (CDRs) of 3.96 and 5.12 (451/113,973 and 317/61,896, respectively, p = .003), and positive predictive values for recall (PPV1s) of 4.64% and 7.54% (451/9726 and 317/4204, respectively, p < .001), respectively. In mammograms with implants, DM ( n = 6815) and DM/DBT ( n = 5138) yielded RRs of 5.81% and 4.87% (396/6815 and 250/5138, respectively, p = .158), CDRs of 2.49 and 2.92 (17/6815 and 15/5138, respectively, p > 0.999), and PPV1s of 4.29% and 6.0% (17/396 and 15/250, respectively, p > 0.999), respectively. Conclusions DM/DBT significantly improved recall rates, cancer detection rates, and positive predictive values for recall compared to DM alone in women without implants. DM/DBT performance in women with implants trended towards similar improvements, though no metric was statistically significant. Key Points • Digital mammography with tomosynthesis improved recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women without implants. • Digital mammography with tomosynthesis trended towards improving recall rates, cancer detection rates, and positive predictive values for recall compared to digital mammography alone for women with implants, but these trends were not statistically significant — likely related to sample size.

Objective To compare batch reading and interrupted interpretation for modern screening mammography. Methods We retrospectively reviewed digital mammograms without and with tomosynthesis that were originally interpreted with batch reading or interrupted interpretation between January 2015 and June 2017. The following performance metrics were compared: recall rate (per 100 examinations), cancer detection rate (per 1,000 examinations), and positive predictive values for recall and biopsy. Results In all, 9,832 digital mammograms were batch read, yielding a recall rate of 9.98%, cancer detection rate of 4.27, and positive predictive values for recall and biopsy of 4.40% and 35.5%, respectively. There were 49,496 digital mammograms that were read with interrupted interpretation, yielding a recall rate of 11.3%, cancer detection rate of 4.44, and positive predictive values for recall and biopsy of 3.92% and 30.1%, respectively. Of the digital mammograms with tomosynthesis, 7,075 were batch read, yielding a recall rate of 6.98%, cancer detection rate of 5.37, and positive predictive values for recall and biopsy of 7.69% and 38.0%, respectively. Of the digital mammograms with tomosynthesis, 24,380 were read with interrupted interpretation, yielding a recall rate of 8.30%, cancer detection rate of 5.41, and positive predictive values for recall and biopsy of 6.52% and 33.3%, respectively. For both digital mammograms without and with tomosynthesis, recall rates improved with batch reading compared with interrupted interpretation (P < .001), but no significant differences were seen for other metrics. Discussion Batch reading digital mammograms without and with tomosynthesis improves recall rates while maintaining cancer detection rates and positive predictive values compared with interrupted interpretation.

Purpose To develop a computational approach to re-create rarely stored for-processing (raw) digital mammograms from routinely stored for-presentation (processed) mammograms. Materials and Methods In this retrospective study, pairs of raw and processed mammograms collected in 884 women (mean age, 57 years ± 10 [standard deviation]; 3713 mammograms) from October 5, 2017, to August 1, 2018, were examined. Mammograms were split 3088 for training and 625 for testing. A deep learning approach based on a U-Net convolutional network and kernel regression was developed to estimate the raw images. The estimated raw images were compared with the originals by four image error and similarity metrics, breast density calculations, and 29 widely used texture features. Results In the testing dataset, the estimated raw images had small normalized mean absolute error (0.022 ± 0.015), scaled mean absolute error (0.134 ± 0.078) and mean absolute percentage error (0.115 ± 0.059), and a high structural similarity index (0.986 ± 0.007) for the breast portion compared with the original raw images. The estimated and original raw images had a strong correlation in breast density percentage (Pearson r = 0.946) and a strong agreement in breast density grade (Cohen κ = 0.875). The estimated images had satisfactory correlations with the originals in 23 texture features (Pearson r ≥ 0.503 or Spearman ρ ≥ 0.705) and were well complemented by processed images for the other six features. Conclusion This deep learning approach performed well in re-creating raw mammograms with strong agreement in four image evaluation metrics, breast density, and the majority of 29 widely used texture features. Keywords: Mammography, Breast, Supervised Learning, Convolutional Neural Network (CNN), Deep learning algorithms, Machine Learning Algorithms See also the commentary by Chan in this issue. Supplemental material is available for this article. ©RSNA, 2021

PURPOSE:To study the impact of second-opinion interpretation of breast imaging studies submitted from outside facilities to a tertiary cancer center.MATERIALS AND METHODS:A retrospective database review was conducted of second-opinion interpretations rendered at our institution from January 1, 2010, to June 30, 2014, on studies from patients who did not have a concurrent breast cancer diagnosis. A total of 2,253 patients were included.RESULTS:In 800 of 2,253 patients (35.5%), the BI-RADS categories assigned at our institution and at outside facilities were discordant. Of 973 patients assigned BI-RADS category 4 or 5 at outside facilities, 278 (28.6%) were assigned BI-RADS category 1 to 3 (no biopsy necessary) at our institution. Of 923 patients assigned BI-RADS category 1 to 3 at outside facilities, 191 (20.7%) were assigned BI-RADS category 4 or 5 at our institution, and 189 of these had biopsies, which revealed 23 cancers, 15 high-risk lesions, and 151 benign lesions. One high-risk lesion at core biopsy was upgraded to invasive ductal carcinoma and ductal carcinoma in situ (DCIS) on excision, resulting in 24 cancers. Of these, 18 reflected true additional breast cancers detected as a result of second-opinion interpretation: 12 invasive carcinomas and 6 cases of DCIS. These results translate into a 9.4% (18/191) positive predictive value for the number of cancers diagnosed among all biopsies recommended and a 9.5% (18/189) positive predictive value for the number of cancers diagnosed among all biopsies recommended and actually performed.CONCLUSIONS:These findings demonstrate the positive clinical impact of second-opinion interpretation at a tertiary cancer center of outside-facility breast imaging studies in patients without a breast cancer diagnosis.

In response to healthcare reform, a necessary evolution of radiology has shifted from generating volume to demonstrating value. Multidisciplinary tumor boards provide a critical opportunity for radiologists to demonstrate their value to their clinical colleagues, their patients, administrations, and society.

Multidisciplinary tumor boards are an opportunity for radiologists to demonstrate value to referring clinicians, the hospital, and patients. Multidisciplinary tumor boards are commonly utilized in academic institutions, but may not be readily available in community practice. We discuss strategies academic radiologists may employ to assist in the implementation of a multidisciplinary tumor board in the community practice setting. Summary: Strategies to assist in the implementation of a multidisciplinary tumor board in the community practice setting are described.