Purpose:To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials andMethods:In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted k statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results:Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with k values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion:Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns.q RSNA, 2015

National Electrical Manufacturers Association (NEMA) NU 2-2007 performance measurements were conducted on the Inveon ™ preclinical small animal PET system developed by Siemens Medical Solutions. The scanner uses 1.51 × 1.51 × 10 mm LSO crystals grouped in 20 × 20 blocks; a tapered light guide couples the LSO crystals of a block to a position-sensitive photomultiplier tube. There are 80 rings with 320 crystals per ring and the ring diameter is 161 mm. The transaxial and axial fields of view (FOVs) are 100 and 127 mm, respectively. The scanner can be docked to a CT scanner; the performance characteristics of the CT component are not included herein. Performance measurements of spatial resolution, sensitivity, scatter fraction and count rate performance were obtained for different energy windows and coincidence timing window widths. For brevity, the results described here are for an energy window of 350-650 keV and a coincidence timing window of 3.43 ns. The spatial resolution at the center of the transaxial and axial FOVs was 1.56, 1.62 and 2.12 mm in the tangential, radial and axial directions, respectively, and the system sensitivity was 36.2 cps kBq −1 for a line source (7.2% for a point source). For mouse-and rat-sized phantoms, the scatter fraction was 5.7% and 14.6%, respectively. The peak noise equivalent count rate with a noisy randoms estimate was 1475 kcps at 130 MBq for the mouse-sized phantom and 583 kcps at 74 MBq for the rat-sized phantom. The performance measurements indicate that the Inveon ™ PET scanner is a high-resolution tomograph with excellent sensitivity that is capable of imaging at a high count rate.

OBJECTIVE The purpose of this study was to assess the diagnostic performance of supplemental screening molecular breast imaging (MBI) in women with mammographically dense breasts after system modifications to permit radiation dose reduction. SUBJECTS AND METHODS A total of 1651 asymptomatic women with mammographically dense breasts on prior mammography underwent screening mammography and adjunct MBI performed with 300-MBq 99mTc-sestamibi and a direct-conversion (cadmium zinc telluride) gamma camera, both interpreted independently. The cancer detection rate, sensitivity, specificity, and positive predictive value of biopsies performed (PPV3) were determined. RESULTS In 1585 participants with a complete reference standard, 21 were diagnosed with cancer: two detected by mammography only, 14 by MBI only, three by both modalities, and two by neither. Of 14 participants with cancers detected only by MBI, 11 had invasive disease (median size, 0.9 cm; range, 0.5–4.1 cm). Nine of 11 (82%) were node negative, and two had bilateral cancers. With the addition of MBI to mammography, the overall cancer detection rate (per 1000 screened) increased from 3.2 to 12.0 (p < 0.001) (supplemental yield 8.8). The invasive cancer detection rate increased from 1.9 to 8.8 (p < 0.001) (supplemental yield 6.9), a relative increase of 363%, while the change in DCIS detection was not statistically significant (from 1.3 to 3.2, p =0.250). For mammography alone, sensitivity was 24%; specificity, 89%; and PPV3, 25%. For the combination, sensitivity was 91% (p < 0.001); specificity, 83% (p < 0.001); and PPV3, 28% (p = 0.70). The recall rate increased from 11.0% with mammography alone to 17.6% (p < 0.001) for the combination; the biopsy rate increased from 1.3% for mammography alone to 4.2% (p < 0.001). CONCLUSION When added to screening mammography, MBI performed using a radiopharmaceutical activity acceptable for screening (effective dose 2.4 mSv) yielded a supplemental cancer detection rate of 8.8 per 1000 women with mammographically dense breasts.

Addition of gamma imaging to mammography significantly increased detection of node-negative breast cancer in dense breasts by 7.5 per 1000 women screened (95% CI: 3.6, 15.4). To be clinically important, gamma imaging will need to show equivalent performance at decreased radiation doses.

OBJECTIVE Molecular breast imaging with a single-head cadmium zinc telluride (CZT) gamma camera has previously been shown to have good sensitivity for the detection of small lesions. To further improve sensitivity, we developed a dual-head molecular breast imaging system using two CZT detectors to simultaneously acquire opposing breast views and reduce lesion-to-detector distance. We determined the incremental gain in sensitivity of molecular breast imaging with dual detectors. SUBJECTS AND METHODS Patients with BI-RADS category 4 or 5 lesions < 2 cm that were identified on mammography or sonography and scheduled for biopsy underwent molecular breast imaging as follows: After injection of 740 MBq of technetium-99m (99mTc) sestamibi, 10-minute craniocaudal and mediolateral oblique views of each breast were acquired. Blinded reviews were performed using images from both detectors 1 and 2 and images from detector 1 only (simulating a single-head system). Lesions were scored on a scale of 1–5; 2 or higher was considered positive. RESULTS Of the 150 patients in the study, 128 cancers were confirmed in 88 patients. Averaging the results from the three blinded readers, the sensitivity of dual-head molecular breast imaging was 90% (115/128), whereas the sensitivity from review of only single-head molecular breast imaging was 80% (102/128). The sensitivity for the detection of cancers ≤ 10 mm in diameter was 82% (50/61) for dual-head molecular breast imaging and 68% (41/61) for single-head molecular breast imaging. On average, 13 additional cancers were seen on dual-head images and the tumor uptake score increased by 1 or more in 60% of the identified tumors. CONCLUSION Gains in sensitivity with the dual-head system molecular breast imaging are partially due to increased confidence in lesion detection. Molecular breast imaging can reliably detect breast lesions < 2 cm and dual-head molecular breast imaging can significantly increase sensitivity for subcentimeter lesions.