Vatteroni Giulia, Dietzel Matthias, Baltzer Pascal A T
Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Milan, Pieve Emanuele, Italy.
Department of Radiology, University Hospital Erlangen, Erlangen, Germany.
Eur Radiol. 2025 Mar;35(3):1504-1513. doi: 10.1007/s00330-024-11274-6. Epub 2024 Dec 18.
This systematic review and meta-analysis investigate the added value of structured integration of Breast Imaging Reporting and Data System (BI-RADS) criteria using the Kaiser score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions.
A systematic review and meta-analysis were conducted using predefined criteria. Eligible articles, published in English until May 2024, dealt with KS in the context of BI-RADS 4 MRI. Two reviewers extracted study characteristics, including true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Sensitivity, specificity, negative likelihood ratio, and positive likelihood ratio were calculated using bivariate random effects. Fagan nomograms identified the maximum pre-test probability at which post-test probabilities of a negative MRI aligned with the 2% malignancy rate benchmark for downgrading BI-RADS 4 to BI-RADS 3. I² statistics and meta-regression explored sources of heterogeneity. p-values < 0.05 were considered significant.
Seven studies with 1877 lesions (833 malignant, 1044 benign) were included. The average breast cancer prevalence was 47.3%. Pooled sensitivity was 94.3% (95%-CI 88.9%-97.1%), and pooled specificity was 68.1% (95%-CI 56.6%-77.7%) using a random effects model. Overall, 52/833 cases were FNs (6.2%). Fagan nomograms showed that KS could rule out breast cancer in BI-RADS 4 lesions at a pre-test probability of 20.3% for all lesions, 25.4% for masses, and 15.2% for non-mass lesions.
In MRI-assessed BI-RADS 4 lesions, applying structured BI-RADS criteria with the KS reduces unnecessary biopsies by 70% with a 6.2% FN rate. Breast cancer can be ruled out up to pre-test probabilities of 20.3%.
Question What, if any, value is added by structured integration of BI-RADS criteria using the Kaiser Score (KS) to avoid unnecessary biopsies in BI-RADS 4 lesions? Findings The structured integration of BI-RADS criteria using the Kaiser Score (KS) reduces unnecessary biopsies in BI-RADS 4 lesions by 70%. Clinical relevance The structured approach offered by the Kaiser Score (KS) avoids unnecessary recalls, potentially reducing patient anxiety, lessening the burden on medical personnel, and the need for further imaging and biopsies due to more objective and thus efficient clinical decision-making in evaluating BI-RADS 4 lesions.
本系统评价和荟萃分析旨在研究采用凯泽评分（KS）对乳腺影像报告和数据系统（BI-RADS）标准进行结构化整合，以避免对BI-RADS 4类病变进行不必要活检的附加价值。
采用预定义标准进行系统评价和荟萃分析。纳入截至2024年5月以英文发表的、涉及BI-RADS 4类MRI背景下KS的合格文章。两名研究者提取研究特征，包括真阳性（TP）、假阳性（FP）、真阴性（TN）和假阴性（FN）。使用双变量随机效应模型计算敏感性、特异性、阴性似然比和阳性似然比。费根列线图确定了MRI阴性的检验后概率与将BI-RADS 4类降为BI-RADS 3类的2%恶性率基准一致时的最大检验前概率。I²统计量和荟萃回归分析探索异质性来源。p值<0.05被认为具有统计学意义。
纳入7项研究，共1877个病变（833个恶性，1044个良性）。平均乳腺癌患病率为47.3%。采用随机效应模型，汇总敏感性为94.3%（95%可信区间88.9%-97.1%），汇总特异性为68.1%（95%可信区间56.6%-77.7%）。总体而言，833例中有52例为假阴性（6.2%）。费根列线图显示，KS可在所有病变检验前概率为20.3%、肿块为25.4%、非肿块病变为15.2%时排除BI-RADS 4类病变中的乳腺癌。
在MRI评估的BI-RADS 4类病变中，应用带有KS的结构化BI-RADS标准可减少70%的不必要活检，假阴性率为6.2%。在检验前概率高达20.3%时可排除乳腺癌。
问题：采用凯泽评分（KS）对BI-RADS标准进行结构化整合，以避免对BI-RADS 4类病变进行不必要活检，有何附加价值（若有）？研究结果：采用凯泽评分（KS）对BI-RADS标准进行结构化整合可减少70%的BI-RADS4类病变的不必要活检。临床意义：凯泽评分（KS）提供的结构化方法避免了不必要的召回，可能减轻患者焦虑，减轻医务人员负担，并因在评估BI-RADS 4类病变时更客观从而更高效的临床决策而减少进一步成像和活检的需求。
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