OncologyComparison of the UCLA Integrated Staging System and the Leibovich Score in Survival Prediction for Patients With Nonmetastatic Clear Cell Renal Cell Carcinoma
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.
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2022, Urologic Oncology: Seminars and Original InvestigationsLeibovich score is the optimal clinico-pathological system associated with recurrence of non-metastatic clear cell renal cell carcinoma
2021, Urologic Oncology: Seminars and Original InvestigationsCitation Excerpt :Standardising clinical practice to a single stratification system would be beneficial for outcome-comparison between centres, to best control for disease stage in clinical trials or for determining if novel biomarkers are useful in addition to clinico-pathological recurrence risk scoring. Tan et al highlighted that different oncological trials are selecting patients based on different prognostic scores, and that comparison of scores is important to minimise confusion and ideally to ensure uniformity in future trial design [4]. Existing clinico-pathological scoring systems range in complexity and the number of factors included.
External validation of the updated Leibovich prognostic models for clear cell and papillary renal cell carcinoma in an Asian population
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2017, Annals of OncologyCitation Excerpt :Although adding more variables and data can increase the accuracy of a model, it also increases the complexity and may not significantly improve its predictive ability or clinical utility. The UISS score was developed using the kidney cancer database from the UCLA Kidney Cancer Program, with the goal of providing a clinically simple and accurate algorithm for predicting survival, using a few variables readily available in medical practice [7]. An impediment to the widespread clinical adoption and external validation of the SSIGN is its reliance on tumor necrosis, a pathological variable that lacks a standardized definition and reporting method and is not quickly available at most centers, especially in private practice [24].
T. Min-Han and R. Kanesvaran have contributed equally to this work.