Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting.
Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting.
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DOI：
10.1186/s13058-021-01489-6
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发表时间：
2021-11-27
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影响因子：
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通讯作者：
Yankeelov TE
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作者：
Virostko J;Sorace AG;Slavkova KP;Kazerouni AS;Jarrett AM;DiCarlo JC;Woodard S;Avery S;Goodgame B;Patt D;Yankeelov TE
本研究的目的是确定先进的定量磁共振成像（MRI）是否可以部署在大型研究型学术医院之外，并进入社区护理环境，以预测局部晚期乳腺癌患者对新辅助治疗（NAT）的最终病理完全缓解（pCR）。II/III期乳腺癌患者（N = 28）入组在社区放射学环境中进行的多中心研究。动态对比增强（DCE）和扩散加权（DW）-MRI数据在NAT过程中的四个时间点获得。血管灌注和渗透性的估计，评估的体积转移率（Ktranss）使用Patlak模型，从DCE-MRI数据，而细胞密度的估计，评估的表观扩散系数（ADC），从DW-MRI数据计算。使用半自动分割计算肿瘤体积，并与Ktrans和ADC相结合，分别产生大量肿瘤血流量和细胞结构。计算每次MRI扫描时定量参数的百分比变化，并与手术时的病理反应进行比较。使用受试者工作特征曲线量化每个MRI参数在不同时间点的预测准确性。在基线时，达到pCR（n = 8）和未达到pCR（n = 20）的组之间的肿瘤大小和定量MRI参数相似。在一个周期的NAT后，达到pCR的患者的体积和细胞结构的下降幅度大于未达到pCR的患者（p < 0.05）。在第三次和第四次MRI时，与非pCR患者相比，达到pCR的队列中肿瘤体积、Ktranss、ADC、细胞结构和肿瘤体积流量相对于基线（治疗前）的变化均显著更大（p < 0.05）。DCE-MRI和DW-MRI的定量分析可以在社区护理环境中实施，以准确预测乳腺癌对NAT的反应。将定量MRI传播到社区环境中可以将这些参数纳入护理标准，并增加能够参与需要定量MRI的新药试验的临床社区站点的数量。在线版本包含补充材料，可通过10.1186/s13058-021-01489-6获得。
The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI. The online version contains supplementary material available at 10.1186/s13058-021-01489-6.