UK Multicenter Prospective Evaluation of the Leibovich Score in Localized Renal Cell Carcinoma: Performance has Altered Over Time (original) (raw)
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Urology
OBJECTIVE To examine changes in outcome by the Leibovich score using contemporary and historic cohorts of patients presenting with renal cell carcinoma (RCC) PATIENTS AND METHODS Prospective observational multicenter cohort study, recruiting patients with suspected newly diagnosed RCC. A historical cohort of patients was examined for comparison. Metastasis-free survival (MFS) formed the primary outcome measure. Model discrimination and calibration were evaluated using Cox proportional hazard regression and the Kaplan-Meier method. Overall performance of the Leibovich model was assessed by estimating explained variation. RESULTS Seven hundred and six patients were recruited between 2011 and 2014 and RCC confirmed in 608 (86%) patients. Application of the Leibovich score to patients with localized clear cell RCC in this contemporary cohort demonstrated good model discrimination (c-index = 0.77) but suboptimal calibration, with improved MFS for intermediate-and high-risk patients (5-year MFS 85% and 50%, respectively) compared to the original Leibovich cohort (74% and 31%) and a historic (1998-2006) UK cohort (76% and 37%). The proportion of variation in outcome explained by the model is low and has declined over time (28% historic vs 22% contemporary UK cohort). CONCLUSION Prognostic models are widely employed in patients with localized RCC to guide surveillance intensity and clinical trial selection. However, the majority of the variation in outcome remains unexplained by the Leibovich model and, over time, MFS rates among intermediate-and highrisk classified patients have altered. These findings are likely to have implications for all such models used in this setting. UROLOGY 136: 162−168, 2020.
Journal of Endourology, 2013
To assess the use of prognostic factors and models in renal cell carcinoma (RCC) and to gain insight in the motivations precluding prognosis estimation and the use of prognosticators. Materials and Methods A questionnaire was send to 110 urologists involved in the CROES renal mass study. Frequencies were gathered using descriptive statistics. Results The majority of the 86 responders worked in a university hospital in Europe. Most of the urologists (97.7%) used the TNM classification and 44% performed prognosis estimations in all patients. Main reason not to estimate prognosis was lack of accuracy (20.9%) and of additional benefit (11.6%). Additionally clinical, laboratory or pathological factors were used by 89.5% of the urologists and biomarkers by 16.3%. Preoperative models were utilized by 20.9%, postoperative models by 38.4% and metastatic models by 38.4%. The Raj and Motzer models were the most used in preoperative and metastatic settings while no predominance among the different postoperative models was seen. The most important reasons to skip the use of models were "lack of additional value" and "lack of familiarity" reported by 30.2% and 27.9% of the responders respectively. Conclusions The TNM is the mainstay for assessing prognosis in RCC. Our data indicates that penetration of prognostic systems is at the most moderate, suggesting limited use outside original developmental settings. On the contrary, clinical, laboratory and pathological factors are used by almost all urologists for prognosis estimations. The most important reason not to use models is the lack of additional value.
External validation of the modified Glasgow prognostic score for renal cancer
Indian Journal of Urology, 2014
Purpose: Purpose: The modifi ed Glasgow prognostic Score (mGPS) incorporates C-reactive protein and albumin as a clinically useful marker of tumor behavior. The ability of the mGPS to predict metastasis in localized renal cell carcinoma (RCC) remains unknown in an external validation cohort. Patients and Methods: Patients and Methods: Patients with clinically localized clear cell RCC were followed for 1 year post-operatively. Metastases were identifi ed radiologically. Patients were categorized by mGPS score as low-risk (mGPS = 0 points), intermediate-risk (mGPS = 1 point) and high-risk (mGPS = 2 points). Univariate, Kaplan-Meier and multivariate Cox regression analyses examined Recurrence-free survival (RFS) across patient and disease characteristics. Results: Results: Of the 129 patients in this study, 23.3% developed metastases. Of low, intermediate and high risk patients, 10.1%, 38.9% and 89.9% recurred during the study. After accounting for various patient and tumor characteristics in multivariate analysis including stage and grade, only mGPS was signifi cantly associated with RFS. Compared with low-risk patients, intermediate-and high-risk patients experienced a 4-fold (hazard ratios [HR]: 4.035, 95% confi dence interval [CI]: 1.312-12.415, P = 0.015) and 7-fold (HR: 7.012, 95% CI: 2.126-23.123 P < 0.001) risk of metastasis, respectively. Conclusions: Conclusions: mGPS is a robust predictor of metastasis following potentially curative nephrectomy for localized RCC. Clinicians may consider mGPS as an adjunct to identify high-risk patients for possible enrollment into clinical trials or for patient counseling.
Renal Cell Carcinoma – How Can We Predict its Outcomes in Clinical Practice?
Acta Chirurgica Latviensis, 2013
Summary Morbidity and mortality data of RCC (renal cell carcinoma) differs a lot among the European countries. In Latvia a growing trend in both incidence and mortality rates is still observed. The expanding availability of multiple treatment strategies has increased the importance of skilled individualized outcome prediction for patients. Several prognostic factors are available in RCC including anatomical, histological, clinical and molecular ones, but none of them is very precise, when used alone. Therefore increasing number of prognostic systems has been created in local and metastatic disease to increase predictive accuracy. In order to encourage the clinicians to use the available models in their routine practice, we tried to select the most relevant ones and include them in a simple algorithm to be used in common clinical scenarios throughout entire history of the disease in patients with RCC