Abstract
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.