Improving Accuracy and Efficiency with Concurrent Use of Artificial Intelligence for Digital Breast Tomosynthesis.

PURPOSE: To evaluate the use of artificial intelligence (AI) to shorten digital breast tomosynthesis (DBT) reading time while maintaining or improving accuracy. MATERIALS AND METHODS: A deep learning AI system was developed to identify suspicious soft-tissue and calcified lesions in DBT images. A reader study compared the performance of 24 radiologists (13 of whom were breast subspecialists) reading 260 DBT examinations (including 65 cancer cases) both with and without AI. Readings occurred in two sessions separated by at least 4 weeks. Area under the receiver operating characteristic curve (AUC), reading time, sensitivity, specificity, and recall rate were evaluated with statistical methods for multireader, multicase studies. RESULTS: Radiologist performance for the detection of malignant lesions, measured by mean AUC, increased 0.057 with the use of AI (95% confidence interval [CI]: 0.028, 0.087; P < .01), from 0.795 without AI to 0.852 with AI. Reading time decreased 52.7% (95% CI: 41.8%, 61.5%; P < .01), from 64.1 seconds without to 30.4 seconds with AI. Sensitivity increased from 77.0% without AI to 85.0% with AI (8.0%; 95% CI: 2.6%, 13.4%; P < .01), specificity increased from 62.7% without to 69.6% with AI (6.9%; 95% CI: 3.0%, 10.8%; noninferiority P < .01), and recall rate for noncancers decreased from 38.0% without to 30.9% with AI (7.2%; 95% CI: 3.1%, 11.2%; noninferiority P < .01). CONCLUSION: The concurrent use of an accurate DBT AI system was found to improve cancer detection efficacy in a reader study that demonstrated increases in AUC, sensitivity, and specificity and a reduction in recall rate and reading time.© RSNA, 2019See also the commentary by Hsu and Hoyt in this issue.

Concurrent Computer-Aided Detection Improves Reading Time of Digital Breast Tomosynthesis and Maintains Interpretation Performance in a Multireader Multicase Study.

OBJECTIVE: Digital breast tomosynthesis (DBT) is more accurate than full-field digital mammography alone but requires a longer reading time. A radiologist reader study evaluated the use of concurrent computer-aided detection (CAD) to shorten the reading time while maintaining interpretation performance. MATERIALS AND METHODS: A CAD system was developed to detect suspicious soft-tissue densities in DBT planes. Abnormalities are extracted from the plane in which they are detected and blended into the corresponding synthetic image. The study used an enriched sample of 240 DBT cases with 68 malignancies in 61 patients. Twenty radiologists retrospectively reviewed all 240 cases in a multireader multicase crossover design to compare reading time and performance with and without CAD. The performance of CAD alone was also evaluated. RESULTS: Reading time improved by 29.2% with CAD (95% CI, 21.1-36.5%; p < 0.01). Reader performance, measured by ROC AUC, was noninferior with CAD (p < 0.01). The mean AUC increased from 0.841 without to 0.850 with CAD (95% CI, -0.012 to 0.030). Mean sensitivity increased from 0.847 without to 0.871 with CAD (difference 95% CI, -0.005 to 0.055), showing a 0.033 increase in sensitivity for cases with soft-tissue densities (95% CI, -0.002 to 0.068). Mean specificity decreased from 0.527 without to 0.509 with CAD (difference 95% CI, -0.041 to 0.005), and mean recall rate for noncancers slightly increased from 0.474 without to 0.492 with CAD (difference 95% CI, -0.006 to 0.041). CONCLUSION: Concurrent use of CAD with DBT resulted in 29.2% faster reading time, while maintaining reader interpretation performance.

Relationship between rhinosinusitis symptoms and mucociliary clearance time.

OBJECTIVE: To determine the correlation between rhinosinusitis symptoms as assessed by the Sino-Nasal Outcomes Test-20 (SNOT-20) and mucociliary clearance as assessed by the saccharin method. STUDY DESIGN AND SETTING: This was a cross-sectional study of 50 adult volunteers. Subjects completed the SNOT-20, and mucociliary clearance was determined with the saccharine method. Correlation coefficients (Spearman's Rho) were calculated for the global SNOT-20 score. RESULTS: The SNOT-20 scores varied from 20 to 54 (mean 30.28) with a possible range of 20 to 100. Clearance times varied from 418 to 2865 seconds (mean 999). There was no significant correlation between global SNOT-20 score and clearance time (r = 0.196). CONCLUSIONS: There is no significant correlation between rhinosinusitis symptoms as assessed by SNOT-20 scores and mucociliary clearance. SIGNIFICANCE: Mucociliary clearance is important for the health of the sinonasal cavities, but clearance time does not appear to be associated with symptom severity in the population studied. EBM RATING: A-1b.