Anatomic features of enhancing renal masses predict malignant and high-grade pathology: a preoperative nomogram using the RENAL Nephrometry score

Abstract

Background: Counseling patients with enhancing renal mass currently occurs in the context of significant uncertainty regarding tumor pathology.

Objective: We evaluated whether radiographic features of renal masses could predict tumor pathology and developed a comprehensive nomogram to quantitate the likelihood of malignancy and high-grade pathology based on these features.

Design, setting, and participants: We retrospectively queried Fox Chase Cancer Center's prospectively maintained database for consecutive renal masses where a Nephrometry score was available.

Intervention: All patients in the cohort underwent either partial or radical nephrectomy.

Measurements: The individual components of Nephrometry were compared with histology and grade of resected tumors. We used multiple logistic regression to develop nomograms predicting the malignancy of tumors and likelihood of high-grade disease among malignant tumors.

Results and limitations: Nephrometry score was available for 525 of 1750 renal masses. Nephrometry score correlated with both tumor grade (p < 0.0001) and histology (p < 0.0001), such that small endophytic nonhilar tumors were more likely to represent benign pathology. Conversely, large interpolar and hilar tumors more often represented high-grade cancers. The resulting nomogram from these data offers a useful tool for the preoperative prediction of tumor histology (area under the curve [AUC]: 0.76) and grade (AUC: 0.73). The model was subjected to out-of-sample cross-validation; however, lack of external validation is a limitation of the study.

Conclusions: The current study is the first to objectify the relationship between tumor anatomy and pathology. Using the Nephrometry score, we developed a tool to quantitate the preoperative likelihood of malignant and high-grade pathology of an enhancing renal mass.

Fig. 1

– Calibration after grouping individuals by decile of regression predicted probabilities. Area under the curve (AUC) for cancer model: 0.76; AUC for grade model: 0.73. In a well calibrated plot, the points would lie on the dashed line. This model is well calibrated. AUC = area under the curve

Fig. 2

– Nomogram evaluating risks of an enhancing renal mass being malignant and high grade. Total point values are independently calculated for the cancer and the high-grade models and then applied to the corresponding probability scale at the bottom of the figure.