Hua Bei, Yang Guang, Wang Yong, Chen Jun, Rong Xiaocui, Yuan Tao, Quan Guanmin
Department of Radiology and Nuclear Medicine, The First Hospital of Hebei Medical University, No.89 Donggang road, Shijiazhuang, Hebei, China (B.H., Y.W.).
Department of Radiology, The Fourth Hospital of Hebei Medical University, No.12 Jiankang road, Shijiazhuang, Hebei, China (G.Y., J.C., X.R.).
Acad Radiol. 2025 Mar;32(3):1241-1251. doi: 10.1016/j.acra.2024.09.054. Epub 2024 Oct 11.
The Kaiser score (KS) is a simple and intuitive machine-learning derived decision rule for characterizing breast lesions in a clinical setting and screening for breast cancer. The present study aims to investigate the applicability of the KS for contrast-enhanced mammography (CEM) in breast masses, and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). CEM may provide an alternative option for patients with breast masses, especially for those with MRI contraindications.
Two hundred and seventy-five patients with breast enhanced masses were included in the study from May 2019 to September 2022. Patients were further divided into benign and malignant groups based on pathological diagnosis. The CEM and MRI imaging characteristics of these two groups were analyzed statistically. The paired chi-square and Cohen's kappa coefficient (κ) analysis were used to compare imaging characteristics between CEM and MRI. The Breast Imaging Reporting and Data System (BI-RADS) and KS for CEM and MRI were evaluated based on imaging characteristics. The diagnostic performance of BI-RADS and KS for CEM and MRI was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test.
The imaging characteristics of root sign, time-signal intensity curve (TIC/mTIC), margin, internal enhancement pattern (IEP), edema, apparent diffusion coefficient (ADC) values, and suspicious malignant microcalcifications showed significant differences between benign and malignant lesions (all p ≤ 0.011). The detection rate of root sign and margin showed substantial agreement between CEM and MRI (κ = 0.656, κ = 0.640), but IEP, TIC/mTIC, and edema showed poor agreement (κ = 0.380, κ = 0.320, κ = 0.324). For all lesion analyses, the area under the curves (AUCs) of the KS (0.897 ∼ 0.932) were higher than that of BI-RADS (0.691) in CEM (all p < 0.001). The AUC of KS (calcification)-CEM (0.932) was higher than those of both KS-CEM and KS (edema)-CEM (0.897 and 0.899) (all p < 0.001). For subgroup analyses, the AUCs of the KS (0.875 ∼ 0.876) were higher than that of BI-RADS (0.740) in MRI (all p < 0.001). The AUCs of KS-MRI (0.876) and KS (ADC)-MRI (0.875) were similar to those of KS-CEM (0.878) and KS (edema)-CEM (0.870) (all p > 0.100). The AUC of KS (calcification)-CEM (0.934) was slightly higher than those of both KS-MRI (0.876) and KS (ADC)-MRI (0.875), but no significant difference was observed (p = 0.051; p = 0.071).
The KS for CEM provided high diagnostic accuracy in distinguishing breast masses, comparable to that of MRI. The application of KS (calcification)-CEM combined with suspicious malignant microcalcifications can improve diagnostic efficiency with an AUC of 0.932 ∼ 0.934. However, edema did not significantly improve performance when using the KS for CEM.
凯泽评分(KS)是一种简单直观的机器学习衍生决策规则,用于在临床环境中对乳腺病变进行特征描述并筛查乳腺癌。本研究旨在探讨KS在乳腺肿块对比增强乳腺钼靶摄影(CEM)中的适用性,并将其诊断准确性与磁共振成像(MRI)进行比较。CEM可为乳腺肿块患者提供另一种选择,尤其是对那些有MRI禁忌证的患者。
2019年5月至2022年9月,本研究纳入了275例乳腺增强肿块患者。根据病理诊断将患者进一步分为良性和恶性组。对这两组的CEM和MRI成像特征进行统计学分析。采用配对卡方检验和科恩kappa系数(κ)分析比较CEM和MRI之间的成像特征。根据成像特征评估CEM和MRI的乳腺影像报告和数据系统(BI-RADS)及KS。采用受试者操作特征(ROC)分析和德龙检验评估并比较BI-RADS和KS对CEM和MRI的诊断性能。
根部征、时间-信号强度曲线(TIC/mTIC)边缘、内部强化模式(IEP)、水肿、表观扩散系数(ADC)值及可疑恶性微钙化的成像特征在良性和恶性病变之间存在显著差异(均p≤0.011)。根部征和边缘的检出率在CEM和MRI之间显示出高度一致性(κ=0.656,κ=0.640),但IEP、TIC/mTIC和水肿的一致性较差(κ=0.380,κ=0.320,κ=0.324)。对于所有病变分析,CEM中KS的曲线下面积(AUC)(0.897~0.932)高于BI-RADS(0.691)(均p<0.001)。KS(钙化)-CEM的AUC(0.932)高于KS-CEM和KS(水肿)-CEM(0.897和0.899)(均p<0.001)。对于亚组分析,MRI中KS的AUC(0.875~0.876)高于BI-RADS(0.740)(均p<0.001)。KS-MRI(0.876)和KS(ADC)-MRI(0.875)的AUC与KS-CEM(0.878)和KS(水肿)-CEM(0.870)相似(均p>0.100)。KS(钙化)-CEM的AUC(0.934)略高于KS-MRI(0.876)和KS(ADC)-MRI(0.875),但未观察到显著差异(p=0.051;p=0.071)。
CEM的KS在鉴别乳腺肿块方面具有较高的诊断准确性,与MRI相当。KS(钙化)-CEM联合可疑恶性微钙化的应用可提高诊断效率,AUC为0.932~0.934。然而,在使用KS进行CEM时,水肿并未显著提高性能。