Development and validation of a nomogram to evaluate the therapeutic effects of second-line axitinib in patients with metastatic renal cell carcinoma

Abstract

The unpredictable biological behavior and tumor heterogeneity of metastatic renal cell carcinoma (mRCC) cause significant differences in axitinib efficacy. The aim of this study is to establish a predictive model based on clinicopathological features to screen patients with mRCC who can benefit from axitinib treatment. A total of 44 patients with mRCC were enrolled and divided into the training set and validation set. In the training set, variables related with the therapeutic efficacy of second-line treatment with axitinib were screened through univariate Cox proportional hazards regression and least absolute shrinkage and selection operator analyses. A predictive model was subsequently established to assess the therapeutic efficacy of second-line treatment with axitinib. The predictive performance of the model was evaluated by analyzing the concordance index and time-dependent receiver operating characteristic, calibration, and decision curves. The accuracy of the model was similarly verified in the validation set. The International Metastatic RCC Database Consortium (IMDC) grade, albumin, calcium, and adverse reaction grade were identified as the best predictors of the efficacy of second-line axitinib treatment. Adverse reaction grade was an independent prognostic index that correlated with the therapeutic effects of second-line treatment with axitinib. Concordance index value of the model was 0.84. Area under curve values for the prediction of 3-, 6-, and 12-month progression-free survival after axitinib treatment were 0.975, 0.909, and 0.911, respectively. The calibration curve showed a good fit between the predicted and actual probabilities of progression-free survival at 3, 6, and 12 months. The results were verified in the validation set. Decision curve analysis revealed that the nomogram based on a combination of four clinical parameters (IMDC grade, albumin, calcium, and adverse reaction grade) had more net benefit than adverse reaction grade alone. Our predictive model can be useful for clinicians to identify patients with mRCC who can benefit from second-line treatment with axitinib.