Department of Translational Medicine, Diagnostic Radiology, Lund University, Malmö, Sweden.
Department of Imaging and Functional Medicine, Skåne University Hospital, Malmö, Sweden.
Eur Radiol. 2023 Nov;33(11):8132-8141. doi: 10.1007/s00330-023-09730-w. Epub 2023 Jun 8.
Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI.
This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated.
In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs.
A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs.
Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue.
• It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.
三阴性乳腺癌（TNBC）是一种高增殖性乳腺癌亚型。本研究旨在通过测量超快速（UF）DCE-MRI 的最大斜率（MS）和增强时间（TTE）、DWI 上的 ADC 以及 UF DCE-MRI 和早期 DCE-MRI 上的边缘增强，来识别表现为肿块的浸润性癌中的 TNBC。
本回顾性单中心研究于 2015 年 12 月至 2020 年 5 月间纳入表现为肿块的乳腺癌患者。UF DCE-MRI 后立即行早期 DCE-MRI。采用组内相关系数（ICC）和 Cohen's kappa 评估观察者间一致性。对 MRI 参数、病变大小和患者年龄进行单变量和多变量逻辑回归分析，以预测 TNBC 并建立预测模型。还评估了 TNBC 患者程序性死亡配体 1（PD-L1）的表达状态。
共纳入 187 名女性（平均年龄 58 岁±12.9[标准差]）的 191 个病灶（33 个 TNBC）进行评估。MS、TTE、ADC 和病变大小的 ICC 分别为 0.95、0.97、0.83 和 0.99，UF 和早期 DCE-MRI 上的边缘增强的kappa 值分别为 0.88 和 0.84。多变量分析后，UF DCE-MRI 上的 MS 和早期 DCE-MRI 上的边缘增强仍然是显著参数。使用这些显著参数创建的预测模型的曲线下面积为 0.74（95%置信区间，0.65，0.84）。表达 PD-L1 的 TNBC 倾向于具有更高的边缘增强率，而非表达 PD-L1 的 TNBC 则较低。
使用 UF 和早期 DCE-MRI 参数的多参数模型可能是一种潜在的成像生物标志物，可用于识别 TNBC。
在早期诊断阶段预测 TNBC 或非 TNBC 至关重要。本研究提供了 UF 和早期 DCE-MRI 提供解决方案的潜力。
• 在早期临床阶段预测 TNBC 至关重要。• UF DCE-MRI 和早期常规 DCE-MRI 上的参数有助于预测 TNBC。• MRI 预测 TNBC 可能有助于确定适当的临床管理。
