Background:The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments.Purpose: To evaluate if using an artificial intelligence (AI) system could reduce workload without reducing cancer detection in breast cancer screening with digital mammography (DM) or digital breast tomosynthesis (DBT). Materials and Methods:Consecutive screening-paired and independently read DM and DBT images acquired from January 2015 to December 2016 were retrospectively collected from the Córdoba Tomosynthesis Screening Trial. The original reading settings were single or double reading of DM or DBT images. An AI system computed a cancer risk score for DM and DBT examinations independently. Each original setting was compared with a simulated autonomous AI triaging strategy (the least suspicious examinations for AI are not human-read; the rest are read in the same setting as the original, and examinations not recalled by radiologists but graded as very suspicious by AI are recalled) in terms of workload, sensitivity, and recall rate. The McNemar test with Bonferroni correction was used for statistical analysis.Results: A total of 15 987 DM and DBT examinations (which included 98 screening-detected and 15 interval cancers) from 15 986 women (mean age 6 standard deviation, 58 years 6 6) were evaluated. In comparison with double reading of DBT images (568 hours needed, 92 of 113 cancers detected, 706 recalls in 15 987 examinations), AI with DBT would result in 72.5% less workload (P , .001, 156 hours needed), noninferior sensitivity (95 of 113 cancers detected, P = .38), and 16.7% lower recall rate (P , .001, 588 recalls in 15 987 examinations). Similar results were obtained for AI with DM. In comparison with the original double reading of DM images (222 hours needed, 76 of 113 cancers detected, 807 recalls in 15 987 examinations), AI with DBT would result in 29.7% less workload (P , .001), 25.0% higher sensitivity (P , .001), and 27.1% lower recall rate (P , .001). Conclusion:Digital mammography and digital breast tomosynthesis screening strategies based on artificial intelligence systems could reduce workload up to 70%.
Despite its high diagnostic performance, the use of breast MRI in the preoperative setting is controversial. It has the potential for personalized surgical management in breast cancer patients, but two of three randomized controlled trials did not show results in favor of its introduction for assessing the disease extent before surgery. Meta-analyses showed a higher mastectomy rate in women undergoing preoperative MRI compared to those who do not. Nevertheless, preoperative breast MRI is increasingly used and a survey from the American Society of Breast Surgeons showed that 41% of respondents ask for it in daily practice. In this context, a large-scale observational multicenter international prospective analysis (MIPA study) was proposed under the guidance of the European Network for the Assessment of Imaging in Medicine (EuroAIM). The aims were (1) to prospectively and systematically collect data on consecutive women with a newly diagnosed breast cancer, not candidates for neoadjuvant therapy, who are offered or not offered breast MRI before surgery according to local practice; (2) to compare these two groups in terms of surgical and clinical endpoints, adjusting for covariates. The underlying hypotheses are that MRI does not cause additional mastectomies compared to conventional imaging, while reducing the reoperation rate in all or in subgroups of patients. Ninety-six centers applied to a web-based call; 36 were initially selected based on volume and quality standards; 27 were active for enrollment. On November 2018, the target of 7000 enrolled patients was reached. The MIPA study is presently at the analytic phase. Key Points• Breast MRI has a high diagnostic performance but its utility in the preoperative setting is controversial.• A large-scale observational multicenter prospective study was launched to compare women receiving with those not receiving preoperative MRI. • Twenty-seven centers enrolled more than 7000 patients. The study is presently at the analytic phase.
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