Objectives: To explore the relationship between Fuhrman grade of renal cell carcinoma (RCC) and the DDD score.
Methods: We reviewed the records of 527 nonmetastatic RCC patients. Demographic, clinical, and pathologic characteristics were reviewed. Binary logistic regression was used to explore the independent risk factors for high-grade RCC (HGRCC).
Results: Sex, BMI (Body Mass Index), RNS, and DDD score were significantly correlated with HGRCC. Based on these independent risk factors, we constructed two predictive models integrating the RNS and DDD scores with sex and BMI to predict tumor grade. The calibration curves of the predictive model showed good agreement between the observations and predictions. The concordance indexes (C-indexes) of the predictive models were 0.768 (95% CI, 0.713-0.824), and 0.809 (95% CI, 0.759-0.859). Receiver operating characteristic (ROC) curves were performed to compare the predictive power of the nomograms, and the prediction model including the DDD score had better prognostic ability (p = 0.01).
Conclusions: This study found that RNS, DDD score, BMI, and sex were independent predictors of HGRCC. We developed effective nomograms integrating the above risk factors to predict HGRCC. Of note, the nomogram including the DDD score achieves better prediction ability for HGRCC.
Keywords: Fuhrman grade; nephrometry; prediction model; renal cell carcinoma.
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