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利用乳腺MRI的参数反应映射和径向异质性对局部晚期乳腺癌新辅助治疗结果进行早期预测。

Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI.

作者信息

Drisis Stylianos, El Adoui Mohammed, Flamen Patrick, Benjelloun Mohammed, Dewind Roland, Paesmans Mariane, Ignatiadis Michail, Bali Maria, Lemort Marc

机构信息

Radiology Department, Institute Jules Bordet, Brussels, Belgium.

Medical Imaging Department, Polytechnic University of Mons, Mons, Belgium.

出版信息

J Magn Reson Imaging. 2020 May;51(5):1403-1411. doi: 10.1002/jmri.26996. Epub 2019 Nov 18.

Abstract

BACKGROUND

Early prediction of nonresponse is essential in order to avoid inefficient treatments.

PURPOSE

To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.

STUDY TYPE

This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially recruited, with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE: A 1.5T scanner was used for MRI examinations.

ASSESSMENT

Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis.

RESULTS

PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference.

DATA CONCLUSION

PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.

摘要

背景

早期预测无反应对于避免无效治疗至关重要。

目的

评估参数反应映射(PRM)衍生的生物标志物是否能够预测化疗开始后24 - 72小时的早期形态学反应(EMR)和病理完全缓解(pCR),以及对无反应PRM区域进行同心分析是否能更好地预测反应。

研究类型

这是一项对前瞻性收集队列的回顾性分析,非随机、单中心诊断研究。

研究对象

最初招募了60名患者,最终队列中有39名女性。

场强/序列:使用1.5T扫描仪进行MRI检查。

评估

在基线(时间点1,TP1)、首次化疗后24 - 72小时(TP2)以及蒽环类药物治疗结束后(TP3)采集动态对比增强(DCE)-MR图像。使用仿射配准算法对TP1和TP2的T减影图像进行融合后进行PRM。值增加超过10%的像素(PRMdce+)为肿瘤相应的无反应区域。TP1和TP3之间最大直径减小(%dDmax)超过30%的患者被定义为EMR反应者。pCR患者的残余癌症负担评分为0。

统计检验

采用t检验、受试者操作特征(ROC)曲线和逻辑回归进行分析。

结果

PRM在pCR反应组之间显示出统计学差异(P < 0.01),预测非pCR的AUC为0.88。逻辑回归分析表明,PRMdce+和II级对非pCR预测具有显著性(P < 0.01)(AUC = 0.94)。外周肿瘤区域在预测非pCR方面(AUC = 0.85)比中间和中心区域表现更好;然而,统计比较显示无显著差异。

数据结论

PRM可在化疗开始后24 - 72小时预测非pCR。此外,外周区域在非pCR预测中的AUC增加,且非pCR肿瘤在治疗期间信号强度增加,这些信息可用于优化组织采样。

证据水平

1技术效能阶段:4《磁共振成像杂志》2020;51:1403 - 1411。

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