Diagnostic performance of two versions of an artificial intelligence system in interval breast cancer detection.
| Autor: | Çelik L; Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey., Güner DC; Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey., Özçağlayan Ö; Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey., Çubuk R; Department of Radiology, Maltepe University, School of Medicine, Istanbul, Turkey., Arıbal ME; Deparment of Radiology, Acıbadem University, School of Medicine, Istanbul, Turkey. |
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| Jazyk: | angličtina |
| Zdroj: | Acta radiologica (Stockholm, Sweden : 1987) [Acta Radiol] 2023 Nov; Vol. 64 (11), pp. 2891-2897. Date of Electronic Publication: 2023 Sep 18. |
| DOI: | 10.1177/02841851231200785 |
| Abstrakt: | Background: Various versions of artificial intelligence (AI) have been used as a diagnostic tool aid in the diagnosis of breast cancer. One of the most important problems in breast screening progmrams is interval breast cancer (IBC). Purpose: To compare the diagnostic performance of Transpara v1.6 and v1.7 in the detection of IBC. Material and Methods: Reports of screening mammograms of a total 2,248,665 of women were evaluated retrospectively. Of 2,129,486 mammograms reported as Breast Imaging Reporting and Data System (BIRADS) 1 and 2, the IBC group consisted of 323 cases who were diagnosed as having cancer on mammography and were correlated with pathology in second mammogram taken >30 days after first mammogram. Four hundred and forty-one were defined as the control group because they did not change over 2 years. Cancer risk scores of both groups were determined from 1 to 10 with Tranpara v1.6 and v1.7. Diagnostic performances of both versions were evaluated by the receiver operating characteristic curve. Results: Cancer risk scores 1 and 10 in v1.7 increased compared to v1.6 ( P < 0.001). In all cases, sensitivity for v1.6 was 56.6%, specificity was 90%, and, for v1.7, sensitivity was 65.9% and specificity was 90%, respectively. In all cases, area under the curve values were 0.812 for v1.6 and 0.856 for v1.7, which was higher in v1.7 ( P < 0.001). Diagnostic performance of v1.7 was higher than v1.6 at the 7-12-month period ( P < 0.001). Conclusion: The present study showed that Tranpara v1.7 has a higher specificity, sensitivity and diagnostic performance in IBC determination than v1.6. AI systems can be used in breast screening as a secondary or third reader in screening programs. Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Databáze: | MEDLINE |
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