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• W4385698239 abstract "“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of a biopsy-decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9,700 patient records at the primary institution and validated using another 1,078 patients. All patients were seen from March 2006-December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, USA with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy and cost saving analysis was performed. Results The iBRISK model’s accuracy was 89.5%, area under the receiver operating characteristic curve (AUROC) 0.93 (95% CI: 0.92–0.95), sensitivity 100%, and specificity 81%. A total of 4,209 women (56 [IQR: 45–65] years) were included in the multicenter dataset. Only two of 1,228 patients (0.16%) in the “low” POM group had malignant lesions while in the “high” POM, malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUROC of 0.97 (95% CI: 0.97–0.98). Estimated potential cost savings were over $420 million. Conclusion iBRISK demonstrated high sensitivity in malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM-groups and reduce biopsy-associated costs. Published under a CC BY 4.0 license" @default.
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• W4385698239 date "2023-08-09" @default.
• W4385698239 modified "2023-09-23" @default.
• W4385698239 title "A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms" @default.
• W4385698239 doi "https://doi.org/10.1148/ryai.220259" @default.
• W4385698239 hasPublicationYear "2023" @default.
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