A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Radiology-Artificial Intelligence Pub Date : 2023-08-09 eCollection Date: 2023-11-01 DOI:10.1148/ryai.220259
Chika F Ezeana, Tiancheng He, Tejal A Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela M Otto, Kenneth A Kist, Heather Speck, Lin Wang, Joe Ensor, Ya-Chen T Shih, Bumyang Kim, I-Wen Pan, Adam L Cohen, Kristen Kelley, David Spak, Wei T Yang, Jenny C Chang, Stephen T C Wong
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引用次数: 0

Abstract

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 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.

Results: 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.

Conclusion: 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.

一种深度学习决策支持工具,用于改善BI-RADS 4乳腺造影中的风险分层并减少不必要的活检
目的:评估活检决策支持算法模型--智能增强型乳腺癌风险计算器(iBRISK)--在多中心患者数据集上的性能。材料与方法:iBRISK 是之前通过将深度学习应用于基层医疗机构 9700 份患者记录中的临床风险因素和乳房 X 线照相描述符而开发的,并通过另外 1078 名患者进行了验证。所有患者的就诊时间为 2006 年 3 月至 2016 年 12 月。在这项多中心研究中,iBRISK 对来自德克萨斯州三大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,该数据集涉及乳腺成像报告和数据系统(BI-RADS)第 4 类病变。对数据进行了二分法和三分法处理,以衡量风险分层和恶性肿瘤概率(POM)估算的精确度。还将iBRISK评分作为恶性肿瘤的连续预测因子进行了评估,并进行了成本节约分析:iBRISK模型的准确率为89.5%,接收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%。多中心数据集共纳入了 4209 名女性(中位年龄 56 岁 [IQR:45-65 岁])。在 "低 "POM组的1228名患者中,只有两人(0.16%)出现恶性病变,而在 "高 "POM组中,恶性病变率为85.9%。iBRISK评分作为恶性病变的连续预测指标,其AUC为0.97(95% CI:0.97,0.98)。结论:iBRISK在预测BI-RADS 4病变的恶性程度方面表现出较高的灵敏度。iBRISK可以安全地避免对低度或中度POM组中多达50%的患者进行活检,并降低活检相关的费用:乳腺X线照相术 乳腺 肿瘤 活检/针吸 放射组学 精准乳腺X线照相术 人工智能增强活检决策支持工具 乳腺癌风险计算器 BI-RADS 4乳腺X线照相术风险分层 减少过度活检 恶性肿瘤概率(POM)评估 基于活检的阳性预测值(PPV3) 本文有补充材料。另请参阅本期 McDonald 和 Conant 的评论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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