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The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

Authors :
Łukasz Zapała
Aleksander Ślusarczyk
Rafał Wolański
Paweł Kurzyna
Karolina Garbas
Piotr Zapała
Piotr Radziszewski
Source :
Biomedicines, Vol 10, Iss 5, p 1202 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.

Details

Language :
English
ISSN :
22279059
Volume :
10
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Biomedicines
Publication Type :
Academic Journal
Accession number :
edsdoj.02b58f6d7911457a98c3f944c11af3eb
Document Type :
article
Full Text :
https://doi.org/10.3390/biomedicines10051202