A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.
A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.
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DOI：
10.1148/ryai.220259
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发表时间：
2023-11
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评估活检决策支持算法模型（智能增强乳腺癌风险计算器（iBRISK））在多中心患者数据集上的性能。iBRISK之前是通过将深度学习应用于来自主要机构的9700例患者记录的临床风险因素和乳房X线摄影描述符而开发的，并使用另外1078例患者进行了验证。所有患者均于2006年3月至2016年12月就诊。在这项多中心研究中，对来自德克萨斯州三家主要医疗机构的独立回顾性数据集（2015年1月至2019年6月）进行了进一步评估，使用乳腺成像报告和数据系统（BI-RADS）4类病变。将数据二分和三分以测量风险分层和恶性肿瘤概率（POM）估计的精确度。iBRISK评分也被评价为恶性肿瘤的连续预测因子，并进行了成本节约分析。iBRISK模型的准确性为89.5%，受试者工作特征曲线下面积（AUC）为0.93（95%CI：0.92，0.95），灵敏度为100%，特异性为81%。多中心数据集中共纳入4209名女性（中位年龄56岁[IQR，45-65岁]）。在“低”POM组的1228例患者中，仅有2例（0.16%）发生恶性病变，而在“高”POM组中，恶性病变率为85.9%。iBRISK评分作为恶性肿瘤的连续预测因子，AUC为0.97（95% CI：0.97，0.98）。估计潜在的成本节约超过4.2亿美元。iBRISK在BI-RADS 4病变的恶性预测中表现出高灵敏度。iBRISK可以在低或中度POM组中安全地对高达50%的患者进行活检，并降低活检相关成本。保留字：乳腺X线摄影、乳腺、肿瘤学、活检/针吸、放射组学、精密乳腺X线摄影、AI增强活检决策支持工具、乳腺癌风险计算器、BI-RADS 4乳腺X线摄影风险分层、过度活检减少、乳腺癌发生概率（POM）评估、基于活检的阳性预测值（PPV 3） 补充材料可用于本文。 在CC BY 4.0许可证下发布。另见麦克唐纳和科南特在本期的评论。
To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to 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, 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 savings analysis was performed. The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45–65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the “low” POM group had malignant lesions, while in the “high” POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. iBRISK demonstrated high sensitivity in the 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. Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license. See also the commentary by McDonald and Conant in this issue.
使用GAIL模型，体重指数和SNP的使用来预测异常（BI-RADS 4）乳房X线照片的女性的乳腺癌。
DOI：
10.1186/s13058-014-0509-4
发表时间：
2015-01-08
期刊：
Breast cancer research : BCR
影响因子：
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作者：
McCarthy AM;Keller B;Kontos D;Boghossian L;McGuire E;Bristol M;Chen J;Domchek S;Armstrong K
通讯作者：
Armstrong K