Predicting Oncologic Outcomes in Renal Cell Carcinoma After Surgery

Leibovich, Bradley C.; Lohse, Christine M.; Cheville, John C.; Zaid, Harras B.; Boorjian, Stephen A.; Frank, Igor; Thompson, R. Houston; Parker, William P.

doi:10.1016/j.eururo.2018.01.005

Cited by 152 publications

(143 citation statements)

References 27 publications

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“…To validate the discriminative ability of our prognostic model, we used the C‐index, bootstrapping, and our data to test the new model and the 2018 Leibovich model, respectively. The C‐index and 95% CI for our new model are 0.791 and 0.7571–0.8190, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Postoperatively, approximately 10.2–31.6% of patients with localized ccRCC experience recurrence . Therefore, a specific prognostic model is required to evaluate the risk of recurrence in patients with ccRCC.…”

Section: Discussionmentioning

confidence: 99%

“…Several variant models to estimate the risk of recurrence or death from ccRCC have been reported. According to the research, Leibovich's model is known to more accurately predict the progression of and death from ccRCC than are the Cindolo nomogram, the Kattan nomogram, the SSIGN score and the Karakiewicz post‐treatment nomogram . However, Leibovich's model is based more on a European population than on an Asian population.…”

Section: Discussionmentioning

confidence: 99%

“…Postoperatively, approximately 10.2-31.6% of patients with localized ccRCC experience recurrence. 7,8 Therefore, a specific prognostic model is required to evaluate the risk of recurrence in patients with ccRCC. The specific prognostic model for oncological outcomes in ccRCC is based on the effects of various factors on the prognosis of ccRCC, while using statistical methods to convert the results into nomograms or algorithms to visually judge the risk for recurrence and survival rate.…”

Section: Discussionmentioning

confidence: 99%

“…According to the research, Leibovich's model is known to more accurately predict the progression of and death from ccRCC than are the Cindolo nomogram, the Kattan nomogram, the SSIGN score and the Karakiewicz post-treatment nomogram. 7 However, Leibovich's model is based more on a European population than on an Asian population. Therefore, we carried out the present retrospective study to make the model more suitable to the Asian population, referring to Leibovich's model and others.…”

Section: Discussionmentioning

confidence: 99%

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Postoperative prognostic model for patients with clear cell renal cell carcinoma in a Chinese population

Zhu

Yang

et al. 2019

Int J of Urology

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Objectives To develop a predictive model for the oncological outcomes of clear cell renal cell carcinoma in a Chinese population. Methods A retrospective study of 1108 patients with clear cell renal cell carcinoma who underwent nephrectomy or partial nephrectomy between January 2006 and December 2013 was carried out. Recurrence‐free survival was calculated using Kaplan–Meier analysis. Differences between the groups were compared using the log–rank test. Cox proportional hazard regression was used to test associations between features and outcomes. The discriminative ability of the models was validated using Harrell's concordance index and bootstrapping. Results Overall, 942 patients who met the inclusion criteria had been followed. The median follow‐up period was 72 months (range 1–143 months). Multivariate analysis showed that age, Eastern Cooperative Oncology Group performance status, preoperative platelet count, neutrophil‐to‐lymphocyte ratio, tumor size, 2010 tumor stage (pT3 and pT4) and Fuhrman nuclear grade were independent risk factors affecting recurrence‐free survival in clear cell renal cell carcinoma patients (P < 0.05). These factors were assigned to develop a new model. The patients were divided into three groups based on the risk of recurrence. The difference among the prognoses of patients in the three groups was statistically significant (P < 0.05). The concordance index for our new model and that for Leibovich's 2018 model were 0.791 and 0.750, respectively. Conclusions In the present study, the new model has a higher concordance index than does Leibovich's 2018 model of clear cell renal cell carcinoma in the Asian population, with no added pain for patients. This new model might be an appropriate risk stratification tool for clinical work.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Postoperative prognostic model for patients with clear cell renal cell carcinoma in a Chinese population

Zhu

Yang

et al. 2019

Int J of Urology

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Low nuclear expression of HIF‐hydroxylases PHD2/EGLN1 and PHD3/EGLN3 are associated with poor recurrence‐free survival in clear cell renal cell carcinoma

Luomala,

Mattila,

Vainio

et al. 2024

Cancer Medicine

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BackgroundHypoxia inducible factors, HIF‐1α and HIF‐2α, and their main regulators, the prolyl hydroxylase domain proteins (PHDs), mediate cellular response to hypoxia and contribute to tumor progression in clear cell renal cell carcinoma (ccRCC). These biomarkers may improve the value of traditional histopathological features in predicting disease progression after nephrectomy for localized ccRCC and guide patient selection for adjuvant treatments.Patients and MethodsIn this study, we analyzed the associations of PHD2 and PHD3 with histopathological tumor features and recurrence‐free survival (RFS) in a retrospective cohort of 173 patients who had undergone surgery for localized ccRCC at Helsinki University Hospital (HUH), Finland. An external validation cohort of 191 patients was obtained from Turku University Hospital (TUH), Finland. Tissue‐microarrays (TMA) were constructed using the primary tumor samples. Clinical parameters and follow‐up information from 2006 to 2019 were obtained from electronic medical records. The cytoplasmic and nuclear expression of PHD2, and PHD3 were scored based on immunohistochemical staining and their associations with histopathological features and RFS were evaluated.ResultsNuclear PHD2 and PHD3 expression in cancer cells were associated with lower pT‐stage and Fuhrman grade compared with negative nuclei. Patients with positive nuclear expression of PHD2 and PHD3 in cancer cells had favorable RFS compared with patients having negative tumors. The nuclear expression of PHD2 was independently associated with a decreased risk of disease recurrence or death from RCC in multivariable analysis. These results were observed in both cohorts.ConclusionsThe absence of nuclear PHD2 and PHD3 expression in ccRCC was associated with poor RFS and the nuclear expression of PHD2 predicted RFS regardless of other known histopathological prognostic factors. Nuclear PHD2 and PHD3 are potential prognostic biomarkers in patients with localized ccRCC and should be further investigated and validated in prospective studies.

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2018 Leibovich prognostic model for renal cell carcinoma: Performance in a large population with special consideration of Black race

Schmeusser,

Patil,

Nicaise

et al. 2023

Cancer

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BackgroundThe 2018 Leibovich prognostic model for nonmetastatic renal cell carcinoma (RCC) combines clinical, surgical, and pathologic factors to predict progression‐free survival (PFS) and cancer‐specific survival (CSS) for patients with clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) histology. Despite high accuracy, <1% of the original cohort was Black. Here, the authors examined this model in a large population with greater Black patient representation.MethodsBy using a prospectively maintained RCC institutional database, patients were assigned Leibovich model risk scores. Survival outcomes included 5‐year and 10‐year PFS and CSS. Prognostic accuracy was determined using area under the curve (AUC) analysis and calibration plots. Black patient subanalyses were conducted.ResultsIn total, 657 (29%) of 2295 patients analyzed identified as Black. Declines in PFS and CSS were observed as scores increased. Discrimination for ccRCC was strong for PFS (AUC: 5‐year PFS, 0.81; 10‐year PFS, 0.78) and for CSS (AUC: 5‐year CSS, 0.82; 10‐year CSS, 0.74). The pRCC AUC for PFS was 0.74 at 5 years and 0.71 at 10 years; and the AUC for CSS was 0.74 at 5 years and 0.70 at 10 years. In chRCC, better performance was observed for CSS (AUC at 5 years, 0.75) than for PFS (AUC: 0.66 at 5 years; 0.55 at 10 years). Black patient subanalysis revealed similar‐to‐improved performance for ccRCC at 5 years (AUC: PFS, 0.79; CSS, 0.87). For pRCC, performance was lower for PFS (AUC at 5 years, 0.63) and was similar for CSS (AUC at 5 years, 0.77). Sample size limited Black patient 10‐year and chRCC analyses.ConclusionsThe authors externally validated the 2018 Leibovich RCC prognostic model and found optimal performance for ccRCC, followed by pRCC, and then chRCC. Importantly, the results were consistent in this large representation of Black patients.Plain Language Summary In 2018, a model to predict survival in patients with renal cell carcinoma (kidney cancer) was introduced by Leibovich et al. This model has performed well; however, Black patients have been under‐represented in examination of its performance. In this study, 657 Black patients (29%) were included, and the results were consistent. This work is important for making sure the model can be applied to all patient populations.

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Predicting Oncologic Outcomes in Renal Cell Carcinoma After Surgery

Cited by 152 publications

References 27 publications

Postoperative prognostic model for patients with clear cell renal cell carcinoma in a Chinese population

Postoperative prognostic model for patients with clear cell renal cell carcinoma in a Chinese population

Low nuclear expression of HIF‐hydroxylases PHD2/EGLN1 and PHD3/EGLN3 are associated with poor recurrence‐free survival in clear cell renal cell carcinoma

2018 Leibovich prognostic model for renal cell carcinoma: Performance in a large population with special consideration of Black race

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