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功能 4-D 聚类用于描述动态成像中的肿瘤内异质性:在 FDG PET 中作为乳腺癌预后生物标志物的评估。

Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer.

机构信息

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.

Department of Radiology, University of Pennsylvania, Rm. D702 Richards Bldg. 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.

出版信息

Eur J Nucl Med Mol Imaging. 2021 Nov;48(12):3990-4001. doi: 10.1007/s00259-021-05265-8. Epub 2021 Mar 7.

Abstract

PURPOSE

Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making.

METHODS

We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers.

RESULTS

Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes.

CONCLUSIONS

Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

摘要

目的

基于探针的动态(4D)成像方式可在空间和动力学上同时捕获肿瘤内异质性。通过具有不同功能行为的肿瘤亚群来描述异质性,可能阐明肿瘤生物学,以提高靶向治疗的特异性,并实现精确的临床决策。

方法

我们提出了一种用于 4D 成像的无监督聚类算法,该算法将马尔可夫随机场(MRF)图像分割与时间序列分析相结合,以描述动力学肿瘤内异质性。我们通过识别具有空间接近约束的不同时间-活性曲线(TAC)轮廓,将其应用于动态 FDG PET 扫描。我们首先使用模拟动态数据评估算法性能。然后,我们将该算法应用于一组 50 名局部晚期乳腺癌女性的数据集,这些女性在治疗前进行了动态 FDG PET 成像,并进行了随访以监测疾病复发情况。然后从每个肿瘤的功能不同的亚区域中提取功能肿瘤异质性(FTH)特征。然后进行交叉验证时间事件分析,以评估 FTH 特征与已建立的组织病理学和动力学预后标志物相比的预后价值。

结果

将 FTH 特征添加到疾病复发的已知预测因子的基线模型中,并添加了已建立的 FDG 摄取和动力学标志物,将一致性统计量(C 统计量)从 0.59 提高到 0.74(p=0.005)。FTH 特征的无监督层次聚类确定了两种显著(p<0.001)的肿瘤异质性表型,对应于高 FTH 和低 FTH。两种表型之间的 FDG 通量或 Ki 分布差异显著(p=0.04)。

结论

我们的研究结果表明,FTH 的成像标志物在预测乳腺癌无复发生存率方面除了标准 PET 成像指标之外还具有独立的价值,因此值得进一步研究。

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