DCE-MRI of locally-advanced carcinoma of the uterine cervix: Tofts analysis versus non-model-based analyses

Background Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may provide biomarkers of the outcome of locally-advanced cervical carcinoma (LACC). There is, however, no agreement on how DCE-MR recordings should be analyzed. Previously, we have analyzed DCE-MRI data of LACC using non-model-based strategies. In the current study, we analyzed DCE-MRI data of LACC using the Tofts pharmacokinetic model, and the biomarkers derived from this analysis were compared with those derived from the non-model-based analyses. Methods Eighty LACC patients given cisplatin-based chemoradiotherapy with curative intent were included in the study. Treatment outcome was recorded as disease-free survival (DFS) and overall survival (OS). DCE-MRI series were analyzed voxelwise to produce K-trans and v(e) frequency distributions, and ROC analysis was used to identify the parameters of the frequency distributions having the greatest potential as biomarkers. The prognostic power of these parameters was compared with that of the non-model-based parameters LETV (low-enhancing tumor volume) and TVIS (tumor volume with increasing signal). Results Poor DFS and OS were associated with low values of K-trans, whereas there was no association between treatment outcome and v(e). The K-trans parameters having the greatest prognostic value were p35-K-trans (the K-trans value at the 35 percentile of a frequency distribution) and RV-K-trans (the tumor subvolume with K-trans values below 0.13 min(- 1)). Multivariate analysis including clinical parameters and p35-K-trans or RV-K-trans revealed that RV-K-trans was the only independent prognostic factor of DFS and OS. There were significant correlations between RV-K-trans and LETV and between RV-K-trans and TVIS, and the prognostic power of RV-K-trans was similar to that of LETV and TVIS. Conclusions Biomarkers of the outcome of LACC can be provided by analyzing DCE-MRI series using the Tofts pharmacokinetic model. However, these biomarkers do not appear to have greater prognostic value than biomarkers determined by non-model-based analyses.