A computer algorithm that performs at or above the level of radiologists in mammography screening assessment could improve the effectiveness of breast cancer screening.
To perform an external evaluation of 3 commercially available artificial intelligence (AI) computer-aided detection algorithms as independent mammography readers and to assess the screening performance when combined with radiologists.
This retrospective case-control study was based on a double-reader population-based mammography screening cohort of women screened at an academic hospital in Stockholm, Sweden, from 2008 to 2015. The study included 8805 women aged 40 to 74 years who underwent mammography screening and who did not have implants or prior breast cancer. The study sample included 739 women who were diagnosed as having breast cancer (positive) and a random sample of 8066 healthy controls (negative for breast cancer).
Positive follow-up findings were determined by pathology-verified diagnosis at screening or within 12 months thereafter. Negative follow-up findings were determined by a 2-year cancer-free follow-up. Three AI computer-aided detection algorithms (AI-1, AI-2, and AI-3), sourced from different vendors, yielded a continuous score for the suspicion of cancer in each mammography examination. For a decision of normal or abnormal, the cut point was defined by the mean specificity of the first-reader radiologists (96.6%).
The median age of study participants was 60 years (interquartile range, 50-66 years) for 739 women who received a diagnosis of breast cancer and 54 years (interquartile range, 47-63 years) for 8066 healthy controls. The cases positive for cancer comprised 618 (84%) screen detected and 121 (16%) clinically detected within 12 months of the screening examination. The area under the receiver operating curve for cancer detection was 0.956 (95% CI, 0.948-0.965) for AI-1, 0.922 (95% CI, 0.910-0.934) for AI-2, and 0.920 (95% CI, 0.909-0.931) for AI-3. At the specificity of the radiologists, the sensitivities were 81.9% for AI-1, 67.0% for AI-2, 67.4% for AI-3, 77.4% for first-reader radiologist, and 80.1% for second-reader radiologist. Combining AI-1 with first-reader radiologists achieved 88.6% sensitivity at 93.0% specificity (abnormal defined by either of the 2 making an abnormal assessment). No other examined combination of AI algorithms and radiologists surpassed this sensitivity level.
To our knowledge, this study is the first independent evaluation of several AI computer-aided detection algorithms for screening mammography. The results of this study indicated that a commercially available AI computer-aided detection algorithm can assess screening mammograms with a sufficient diagnostic performance to be further evaluated as an independent reader in prospective clinical trials. Combining the first readers with the best algorithm identified more cases positive for cancer than combining the first readers with second readers.
Mattie Salim, MD1,2; Erik Wåhlin, MSc3; Karin Dembrower, MD4,5; Edward Azavedo, MD, PhD1,6; Theodoros Foukakis, MD, PhD1,2; Yue Liu, MSc7; Kevin Smith, MSc, PhD8; Martin Eklund, MSc, PhD9; Fredrik Strand, MD, PhD1,10
1Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden, 2Department of Radiology, Karolinska University Hospital, Stockholm, Sweden, 3Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden, 4Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden, 5Department of Radiology, Capio Sankt Görans Hospital, Stockholm, Sweden, 6Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden, 7Division of Computational Science and Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden, 8KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden, 9Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden, 10Breast Radiology, Karolinska University Hospital, Stockholm, Sweden
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