Background: The authors prospectively evaluated magnetic resonance imaging (MRI) parameters quantifying heterogeneous perfusion pattern and residual tumor volume early during treatment in cervical cancer, and compared their predictive power for primary tumor recurrence and cancer death with the standard clinical prognostic factors. A novel approach of augmenting the predictive power of clinical prognostic factors with MRI parameters was assessed.
Methods: Sixty-two cervical cancer patients underwent dynamic contrast-enhanced (DCE) MRI before and during early radiation/chemotherapy (2-2.5 weeks into treatment). Heterogeneous tumor perfusion was analyzed by signal intensity (SI) of each tumor voxel. Poorly perfused tumor regions were quantified as lower 10th percentile of SI (SI[10%]). DCE-MRI and 3-dimensional (3D) tumor volumetry MRI parameters were assessed as predictors of recurrence and cancer death (median follow-up, 4.1 years). Their discriminating capacity was compared with clinical prognostic factors (stage, lymph node status, histology) using sensitivity/specificity and Cox regression analysis.
Results: SI(10%) and 3D volume 2-2.5 weeks into therapy independently predicted disease recurrence (hazard ratio [HR], 2.6; 95% confidence interval [95% CI], 1.0-6.5 [P = .04] and HR, 1.9; 95% CI, 1.1-3.5 [P = .03], respectively) and death (HR, 1.9; 95% CI, 1.0-3.5 [P = .03] and HR, 1.9; 95% CI, 1.2-2.9 [P = .01], respectively), and were superior to clinical prognostic factors. The addition of MRI parameters to clinical prognostic factors increased sensitivity and specificity of clinical prognostic factors from 71% and 51%, respectively, to 100% and 71%, respectively, for predicting recurrence, and from 79% and 54%, respectively, to 93% and 60%, respectively, for predicting death.
Conclusions: MRI parameters reflecting heterogeneous tumor perfusion and subtle tumor volume change early during radiation/chemotherapy are independent and better predictors of tumor recurrence and death than clinical prognostic factors. The combination of clinical prognostic factors and MRI parameters further improves early prediction of treatment failure and may enable a window of opportunity to alter treatment strategy.