AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation

Abstract

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%.

Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy

“…Four additional articles were identified: one through screening the reference lists of relevant systematic reviews, one through contact with experts, and two by hand searches. Overall, 13 articles25262728293031323334353637 reporting 12 studies were included in this review (see supplementary fig 1 for full PRISMA flow diagram). Exclusions on full text are listed in supplementary appendix 3.…”

“…Four studies evaluated datasets from Sweden,26273536 three of which had largely overlapping populations,263536 one from the United States and Germany,32 one from Germany,25 one from the Netherlands,33 one from Spain31 and four from the US 28293037. Four studies enrolled women consecutively or randomly,25273136 while the remaining studies selected cases and controls to enrich the dataset with patients with cancer. Three studies included all patients with cancer and a random sample of those without cancer 262935.…”

“…Finally, one study included only false negative mammograms 37. No prospective test accuracy studies in clinical practice were included, only retrospective test accuracy studies25262729313536 and enriched test set multiple reader multiple case laboratory studies 2830323337. Of these enriched test set laboratory studies, three reported test accuracy for a single AI read as a reader aid 303237.…”

“…Of these enriched test set laboratory studies, three reported test accuracy for a single AI read as a reader aid 303237. Another nine studies reported test accuracy for a single AI read as a standalone system in a retrospective test accuracy study 25262729313536 or an enriched test set multiple reader multiple case laboratory study 2833…”

“…Two studies compared the performance of the AI system with the average performance of nine Dutch single radiologists33 and five US single radiologists,28 respectively, who read the images under laboratory conditions. Four commercial AI systems were evaluated as a pre-screen to remove normal cases25262731 or were used as a post-screen of negative mammograms after double reading to predict interval and next round screen detected cancers 26…”