How much can abbreviated protocols for breast MRI increase patient throughput? a multi-centric evaluation.

PURPOSE: To assess the impact of abbreviated breast MRI protocols on patient throughput considering non-scanning time and differences between in- and out-of-hospital settings. MATERIALS & METHODS: A total of 143 breast MRI exams from four study sites (hospital, three radiology centers) were included in this retrospective study. Total exam time (TET), Table Time (TT), Scan Time (ST), Table Switch Time (TST) and Planning Time (PT) were determined from consecutive breast MRI examinations. Possible number of scans and exams per hour were calculated. Four scan protocols were compared: full diagnostic protocol (n = 34, hospital), split dynamic protocol (n = 109, all sites) and two abbreviated protocols (n = 109, calculated, all sites). Data were described as median and interquartile range (IQR) and compared by Mann-Whitney-U-Test. RESULTS: Non-scanning time increased from 50% to 74% of the TET with a TST of 46% and a PT of 28% in the shortest abbreviated protocol. Number of possible scans per hour increased from 4.7 to 18.8 while number of possible exams per hour only increased from 2.3 to 5.1. Absolute TST (4.7 vs. 5.7 min, p = 0.46) and TET (18 min each, p = 0.35) did not differ significantly between in- and out-of-hospital exams. Absolute (4.4 vs. 2.8 min, p < 0.001) and relative (23 vs. 13%, p < 0.001) PT and TT (13.3 vs. 11.5 min, p = 0.004) was longer and relative TST (27% vs. 34%, p = 0.047) was shorter in hospital. CONCLUSION: TST and PT significantly contribute to TET and challenge the effectiveness of abbreviated protocols for increasing patient throughput. These findings show only low setting-dependent differences.

Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies.

OBJECTIVES: To investigate whether combined texture analysis and machine learning can distinguish malignant from benign suspicious mammographic calcifications, to find an exploratory rule-out criterion to potentially avoid unnecessary benign biopsies. METHODS: Magnification views of 235 patients which underwent vacuum-assisted biopsy of suspicious calcifications (BI-RADS 4) during a two-year period were retrospectively analyzed using the texture analysis tool MaZda (Version 4.6). Microcalcifications were manually segmented and analyzed by two readers, resulting in 249 image features from gray-value histogram, gray-level co-occurrence and run-length matrices. After feature reduction with principal component analysis (PCA), a multilayer perceptron (MLP) artificial neural network was trained using histological results as the reference standard. For training and testing of this model, the dataset was split into 70 % and 30 %. ROC analysis was used to calculate diagnostic performance indices. RESULTS: 226 patients (150 benign, 76 malignant) were included in the final analysis due to missing data in 9 cases. Feature selection yielded nine image features for MLP training. Area under the ROC-curve in the testing dataset (n = 54) was 0.82 (95 %-CI: 0.70-0.94) and 0.832 (95 %-CI 0.72-0.94) for both readers, respectively. A high sensitivity threshold criterion was identified in the training dataset and successfully applied to the testing dataset, demonstrating the potential to avoid 37.1-45.7 % of unnecessary biopsies at the cost of one false-negative for each reader. CONCLUSION: Combined texture analysis and machine learning could be used for risk stratification in suspicious mammographic calcifications. At low costs in terms of false-negatives, unnecessary biopsies could be avoided.