INTRODUCTION: This study aimed to determine the prognostic value of a panel of SIR-biomarkers, relative to standard clinicopathological variables, to improve mRCC patient selection for cytoreductive nephrectomy (CN). MATERIAL AND METHODS: A panel of preoperative SIR-biomarkers, including the albumin-globulin ratio (AGR), De Ritis ratio (DRR), and systemic immune-inflammation index (SII), was assessed in 613 patients treated with CN for mRCC. Patients were randomly divided into training and testing cohorts (65/35%). A machine learning-based variable selection approach (LASSO regression) was used for the fitting of the most informative, yet parsimonious multivariable models with respect to prognosis of cancer-specific survival (CSS). The discriminatory ability of the model was quantified using the C-index. After validation and calibration of the model, a nomogram was created, and decision curve analysis (DCA) was used to evaluate the clinical net benefit. RESULTS: SIR-biomarkers were selected by the machine-learning process to be of high discriminatory power during the fitting of the model. Low AGR remained significantly associated with CSS in both training (HR 1.40, 95% CI 1.07-1.82, p = 0.01) and testing (HR 1.78, 95% CI 1.26-2.51, p = 0.01) cohorts. High levels of SII (HR 1.51, 95% CI 1.10-2.08, p = 0.01) and DRR (HR 1.41, 95% CI 1.01-1.96, p = 0.04) were associated with CSS only in the testing cohort. The exclusion of the SIR-biomarkers for the prognosis of CSS did not result in a significant decrease in C-index (- 0.9%) for the training cohort, while the exclusion of SIR-biomarkers led to a reduction in C-index in the testing cohort (- 5.8%). However, SIR-biomarkers only marginally increased the discriminatory ability of the respective model in comparison to the standard model. CONCLUSION: Despite the high discriminatory ability during the fitting of the model with machine-learning approach, the panel of readily available blood-based SIR-biomarkers failed to add a clinical benefit beyond the standard model.

Cancer Prognostics and Health Outcomes Unit Division of Urology University of Montreal Health Center Montreal QC Canada

Department of Urology 2nd Faculty of Medicine Charles University Prague Czech Republic

Department of Urology Comprehensive Cancer Center Vienna General Hospital Medical University of Vienna Währinger Gürtel 18 20 1090 Vienna Austria

Department of Urology King Fahad Specialist Hospital Dammam Saudi Arabia

Department of Urology Medical University of Silesia Zabrze Poland

Department of Urology Okayama University Graduate School of Medicine Dentistry and Pharmaceutical Sciences Okayama Japan

Department of Urology The Jikei University School of Medicine Tokyo Japan

Department of Urology University Hospital Zurich Zurich Switzerland

Department of Urology University Medical Center Hamburg Eppendorf Hamburg Germany

Department of Urology University of Texas Southwestern Dallas TX USA

Department of Urology Weill Cornell Medical College New York NY USA

Division of Urology Department of Special Surgery Jordan University Hospital The University of Jordan Amman Jordan

Institute for Urology and Reproductive Health Sechenov University Moscow Russia

Karl Landsteiner Institute of Urology and Andrology Vienna Austria

Men's Health and Reproductive Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran

Research Center for Evidence Based Medicine Tabriz University of Medical Sciences Tabriz Iran

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