Nguyen Huyen T, Jia Guang, Shah Zarine K, Pohar Kamal, Mortazavi Amir, Zynger Debra L, Wei Lai, Yang Xiangyu, Clark Daniel, Knopp Michael V
Wright Center of Innovation in Biomedical Imaging, Department of Radiology, Ohio State University, Columbus, Ohio, USA.
J Magn Reson Imaging. 2015 May;41(5):1374-82. doi: 10.1002/jmri.24663. Epub 2014 Jun 19.
To apply k-means clustering of two pharmacokinetic parameters derived from 3T dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the chemotherapeutic response in bladder cancer at the mid-cycle timepoint.
With the predetermined number of three clusters, k-means clustering was performed on nondimensionalized Amp and kep estimates of each bladder tumor. Three cluster volume fractions (VFs) were calculated for each tumor at baseline and mid-cycle. The changes of three cluster VFs from baseline to mid-cycle were correlated with the tumor's chemotherapeutic response. Receiver-operating-characteristics curve analysis was used to evaluate the performance of each cluster VF change as a biomarker of chemotherapeutic response in bladder cancer.
The k-means clustering partitioned each bladder tumor into cluster 1 (low kep and low Amp), cluster 2 (low kep and high Amp), cluster 3 (high kep and low Amp). The changes of all three cluster VFs were found to be associated with bladder tumor response to chemotherapy. The VF change of cluster 2 presented with the highest area-under-the-curve value (0.96) and the highest sensitivity/specificity/accuracy (96%/100%/97%) with a selected cutoff value.
The k-means clustering of the two DCE-MRI pharmacokinetic parameters can characterize the complex microcirculatory changes within a bladder tumor to enable early prediction of the tumor's chemotherapeutic response.
应用基于3T动态对比增强磁共振成像（DCE-MRI）得出的两个药代动力学参数进行k均值聚类，以预测膀胱癌在治疗周期中期的化疗反应。
设定聚类数为3，对每个膀胱肿瘤的无量纲化Amp和kep估计值进行k均值聚类。在基线期和治疗周期中期计算每个肿瘤的三个聚类体积分数（VF）。从基线期到治疗周期中期三个聚类VF的变化与肿瘤的化疗反应相关。采用受试者工作特征曲线分析评估每个聚类VF变化作为膀胱癌化疗反应生物标志物的性能。
k均值聚类将每个膀胱肿瘤分为1类（低kep和低Amp）、2类（低kep和高Amp）、3类（高kep和低Amp）。发现所有三个聚类VF的变化均与膀胱肿瘤对化疗的反应相关。在选定的临界值下，2类的VF变化呈现出最高的曲线下面积值（0.96）和最高的灵敏度/特异性/准确率（96%/100%/97%）。
对两个DCE-MRI药代动力学参数进行k均值聚类可以表征膀胱肿瘤内复杂的微循环变化，从而能够早期预测肿瘤的化疗反应。
