Pharmacokinetic analysis of DCE-MRI data of locally advanced cervical carcinoma with the Brix model.

Background: There is significant evidence that DCE-MRI may have the potential to provide clinically useful biomarkers of the outcome of locally advanced cervical carcinoma. However, there is no consensus on how to analyze DCE-MRI data to arrive at the most powerful biomarkers. The purpose of this study was to analyze DCE-MRI data of cervical cancer patients by using the Brix pharmacokinetic model and to compare the biomarkers derived from the Brix analysis with biomarkers determined by non-model-based analysis [i.e., low-enhancing tumor volume (LETV) and tumor volume with increasing signal (TVIS)] of the same patient cohort. Material and methods: DCE-MRI recordings of 80 patients (FIGO stage IB-IVA) treated with concurrent cisplatin-based chemoradiotherapy were analyzed voxel-by-voxel, and frequency distributions of the three parameters of the Brix model ( A _Brix , k _ep , and k _el ) were determined. Moreover, risk volumes were calculated from the Brix parameters and termed RV- A _Brix , RV- k _ep , and RV- k _el , where the RVs represent the tumor volume with voxel values below a threshold value determined by ROC analysis. Disease-free survival (DFS) and overall survival (OS) were used as measures of treatment outcome. Results: Significant associations between the median value or any other percentile value of A _Brix , k _ep , or k _el and treatment outcome were not found. However, RV- A _Brix , RV- k _ep , and RV- k _el correlated with DFS and OS. Multivariate analysis revealed that the prognostic power of RV- A _Brix , RV- k _ep , and RV- k _el was independent of well-established clinical prognostic factors. RV- A _Brix , RV- k _ep , and RV- k _el correlated with each other as well as with LETV and TVIS. Conclusion: Strong biomarkers of the outcome of locally advanced cervical carcinoma can be provided by subjecting DCE-MRI series to pharmacokinetic analysis using the Brix model. The prognostic power of these biomarkers is not necessarily superior to that of biomarkers identified by non-model-based analyses.