Laukhtina E, Schuettfort VM, D'Andrea D, Pradere B, Quhal F, Mori K, Sari Motlagh R, Mostafaei H, Katayama S, Grossmann NC, Rajwa P, Karakiewicz PI, Schmidinger M, Fajkovic H, Enikeev D, Shariat SF. Selection and evaluation of preoperative systemic inflammatory response biomarkers model prior to cytoreductive nephrectomy using a machine-learning approach.
World J Urol 2022;
40:747-754. [PMID:
34671856 PMCID:
PMC8948147 DOI:
10.1007/s00345-021-03844-w]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/03/2021] [Indexed: 11/27/2022] Open
Abstract
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.
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