Abstract
BACKGROUND: The objectives of this study were to investigate outcome prediction by measuring absolute tumor volume and regression ratios using serial magnetic resonance imaging (MRI) during radiation therapy (RT) for cervical cancer and to develop algorithms capable of identifying patients at risk of a poor therapeutic outcome. METHODS: Eighty patients with stage IB2 through IVA cervical cancer underwent 4 MRI scans: before RT (MRI1), during RT at 2 to 2.5 weeks (MRI2) at 4 to 5 weeks (MRI3), and 1 to 2 months after RT (MRI4). The median follow-up was 6.2 years (range, 0.2-9.4 years). Tumor volumes at MRI1, MRI2, MRI3, and MRI4 (V1, V2, V3, and V4, respectively) and tumor regression ratios (V2/V1, V3/V1, and V4/V1) were measured by 3-dimensional volumetry. Predictive metrics based on tumor volume/regression parameters were correlated with ultimate clinical outcomes, including tumor local recurrence (LR) and dying of disease (DOD). Predictive power was evaluated using the Mann-Whitney test, sensitivity/specificity analyses, and Kaplan-Meier analyses. RESULTS: Both tumor volume and regression ratio were strongly correlated with LR (P=.06, P = 5×10(-4), P=1×10(-6), and P=2×10(-8) for V1, V2, V3, and V4, respectively; and P=7×10(-5), P=1×10(-6), and P=1×10(-8) for V2/V1, V3/V1, and V4/V1, respectively) and DOD (P=.015, P=.004, P=.001, and P=3×10(-4) for V1, V2, V3, and V4, respectively; and P=.03, P=.009, and P=3×10(-4) for V2/V1, V3/V1, and V4/V1, respectively). Algorithms that combined tumor volumes and regression ratios improved predictive power (sensitivity, 61%-89%; specificity, 79%-100%). The strongest predictor, pre-RT volume and regression ratio at MRI3 (V1>40 cm3 and V3/V1>20%, respectively), achieved 89% sensitivity, 87% specificity, and 88% accuracy for LR and achieved 54% sensitivity, 83% specificity, and 73% accuracy for DOD. CONCLUSIONS: The current results suggested that tumor volume/regression parameters obtained during primary therapy are useful in predicting LR and DOD. Both tumor volume and regression ratio provided important information as early outcome predictors that may guide early intervention for patients with cervical cancer who are at high risk of treatment failure.