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

Abstract

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.

Acknowledgments

Hans Henrik Lien and Kjetil Knutstad, Department of Radiology and Nuclear Medicine, are thanked for their active cooperation in the study.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Additional information

Funding

Financial support was received from the Norwegian Cancer Society and South-Eastern Norway Regional Health Authority