Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study

Abstract

Purpose

We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA).

Materials and methods

A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve.

Results

The clinical model (Model^clinic) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images. The model yield the best performance (Model^{clinic&AP&PVP}) was build using three independent variables, Signature^AP and Signature^PVP. In training and testing cohorts, the concordance indexes (C-indexes) of Model^clinic were 0.846 (95 % CI, 0.735−0.957) and 0.755 (95 % CI, 0.540–969), and Model^{clinic&AP&PVP} achieved C-indexes of 0.962 (95 % CI, 0.905−1) and 0.814 (95 % CI, 0.569−1). Both Model^clinic and Model^{clinic&AP&PVP} outperformed the TNM staging system. Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis for Model^clinic and Model^{clinic&AP&PVP}.

Conclusion

Two preoperative nomograms were constructed to predict 1-, 3-, and 5-years survival for individual pCCA patients, demonstrating the potential for clinical application to assist decision-making.