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