Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers.

INTRODUCTION: Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse.
MATERIALS AND METHODS: Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, K(trans) and ύ(e), were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters.
RESULTS: Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels.
CONCLUSIONS: Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control.