Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Abstract

Background

Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment.

Purpose

To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer.

Material and Methods

X-tile was used to calculate the optimal threshold for ΔADC_mean(%) for prognostic stratification. Kaplan–Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model.

Results

ΔADC_mean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADC_mean(%) to diagnose prognosis was 40.8. Kaplan–Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years (P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862.

Conclusion

ΔADC_mean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADC_mean(%) predict PFS of patients with cervical cancer with moderate accuracy.