Elsevier

Urology

Volume 75, Issue 6, June 2010, Pages 1365-1370.e3
Urology

Oncology
Comparison of the UCLA Integrated Staging System and the Leibovich Score in Survival Prediction for Patients With Nonmetastatic Clear Cell Renal Cell Carcinoma

https://doi.org/10.1016/j.urology.2009.07.1289Get rights and content

Objectives

To directly compare the models—the UCLA-Integrated Scoring System (UISS) and the Leibovich models—using various survival endpoints. Several Phase III trials of adjuvant therapy in renal cell carcinoma (RCC) have been initiated after advances in targeted therapy. To select patients at high risk of relapse and mortality, 2 aforementioned prognostic models have been incorporated into these trials. These models have not been compared previously.

Methods

A retrospective study of 355 patients with unilateral nonmetastatic clear cell RCC undergoing nephrectomy between 1990 and 2006 at the Singapore General Hospital was undertaken. Performance of the UISS and the Leibovich models, as well as corresponding trial inclusion criteria, was directly compared using log-likelihood statistics. Adequacy and concordance indices were also calculated. Study endpoints tested were overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS).

Results

Likelihood ratio testing demonstrated a significant benefit in prediction when adding the Leibovich model to the UISS model in all outcomes tested, with no benefit using the converse approach (OS: P = .002 vs P = .27; CSS: P = .0001 vs P = .57; DFS: P = <0.0001 vs P = .30). Benefit was seen primarily in disease-free survival when adding the Leibovich trial criteria to UISS trial criteria, with no benefit using the converse approach (OS: P = .16 vs P = .27; CSS: P = .17 vs P = .11; DFS: P = .01 vs P = .26).

Conclusions

Both the Leibovich model and trial criteria are superior to the UISS model and trial criteria, respectively, in estimating survival outcomes in patients with nonmetastatic clear cell RCC after nephrectomy.

Section snippets

Materials and Methods

We identified 355 patients with nonmetastatic unilateral clear cell RCC, and who underwent nephrectomies performed in Singapore General Hospital between 1990 and 2006. A pathologist reviewed archived specimens to confirm histology. Patients with metastatic disease (regional or nonregional lymph nodes or distant metastases) were excluded from this study (n = 52). It should be noted that the UISS classifies patients with regional lymph node metastasis only separately from patients with

Results

The clinico-pathologic characteristics of the 355 patients evaluated are reported in Table 1. A comparison is provided against the Leibovich dataset; no equivalent data are available for the UCLA dataset. Over a median follow-up of 56 months, 78 patients had relapsed, 46 had died of disease, and 26 had died of causes other than cancer. The survival outcomes in terms of UISS and Leibovich models and trial criteria are reported in Table 2 and presented in Figure 1. Cox regression showed that

Discussion

Several prognostic models have been developed to improve survival prediction in patients with RCC. A recent systematic review found 11 different models proposed for this purpose,4 and the UISS and the Leibovich models are 2 such algorithm-based models. Both of these models have been incorporated for patient selection in large trials of adjuvant therapy in RCC. Our results show that both the UISS and the Leibovich models provide excellent estimates of various survival outcomes in our single

Conclusions

Both the UISS model and the Leibovich model yield good estimates of OS, CSS and DFS in an external population. The Leibovich model is superior to the UISS model in terms of predicting outcomes in an independent cohort of patients with nonmetastatic clear cell RCC, and this should be considered in future adjuvant trial design.

Acknowledgments

The author would like to thank the residents and medical students attached to the Department of Medical Oncology, National Cancer Centre, Singapore, for their capable assistance rendered in reviewing clinical data.

References (20)

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T. Min-Han and R. Kanesvaran have contributed equally to this work.

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