Prediction of chemotherapeutic response in bladder cancer using K-means clustering of dynamic contrast-enhanced (DCE)-MRI pharmacokinetic parameters

Abstract

Purpose To apply k-means clustering of two pharmacokinetic parameters derived from 3T DCE-MRI to predict chemotherapeutic response in bladder cancer at the mid-cycle time-point. Materials and Methods With the pre-determined number of 3 clusters, k-means clustering was performed on non-dimensionalized 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. Results 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. Conclusion 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.

Non-invasive quantification of tumour heterogeneity in water diffusivity to differentiate malignant from benign tissues of urinary bladder: a phase I study

“…Implementation of k-means clustering analysis: Previous studies on k-means clustering of MRI parameters showed that three clusters provided the best characterisation of cancer tissues [14, 17, 18]. Therefore, we selected k of 3 to perform k-means clustering of voxel-wise ADC values for each case.…”

“…Therefore, we selected k of 3 to perform k-means clustering of voxel-wise ADC values for each case. The case-based k-means clustering was executed on Microsoft Excel using a method described in [17] to determined three cluster centres. Subsequently, two widely used measurements were calculated for each case: (1) mean intra-cluster distance (MICD), which is the average of the distance from each data point (ADC value) to its centre cluster; (2) the largest inter-cluster distance (LICD), which is the largest distance among three cluster centres: $$MICD=\frac{1}{N}{\sum }_{i=1}^3{\sum }_{j=1}^{M_i}false|x_j-c_{i}false|,$$ where N is the total number of data points in the data set, i ranges from 1 to 3 and is denoted for cluster i , c i is the centre of cluster i , M i is the number of data points in cluster i , and x j is the j th data point of cluster i .…”

“…Srinivasan [18] concluded that voxel-wise ADC analysis with k-means clustering could better characterise and differentiate malignant from benign neck pathologies than whole-lesion mean ADC alone. In bladder cancer, it was also demonstrated that k-means clustering of voxel-wise pharmacokinetic parameters could characterise the spatial distribution of microcirculation characteristics in a tumour to facilitate early prediction of chemotherapeutic response [17]. Therefore, it is of clinical interest to investigate the diagnostic value of voxel-wise ADC analysis with k-means clustering in the characterisation of tumour diffusivity to differentiate malignant tumours from the benign wall thickening of the urinary bladder.…”

Contemporary role of advanced imaging for bladder cancer staging

“…head and neck malignancies. A recent article by Nguyen et al [75] used K-means cluster maps of DCE-MRI to show microvascular changes between responders and nonresponders to neoadjuvant chemotherapy. There has been no standardization of MP-MRI for bladder cancer across institutions either, thus, making comparison difficult.…”

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

“…DCE-MRI is a non-invasive method to assess microvasculature in a target tissue by monitoring the change of MR contrast over a certain period of time after injection. DCE-MRI has been utilized to diagnose malignant tumors and to assess tumor response to various therapies [1][2][3][4] . Quantitative DCE-MRI has presented high reproducibility 5 .…”