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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Authors | Ekaterina Laukhtina, Victor M Schuettfort, David D'Andrea, Benjamin Pradere, Fahad Quhal, Keiichiro Mori, Reza Sari Motlagh, Hadi Mostafaei, Satoshi Katayama, Nico C Grossmann, Pawel Rajwa, Pierre I Karakiewicz, Manuela Schmidinger, Harun Fajkovic, Dmitry Enikeev, Shahrokh F Shariat |
Journal | World journal of urology
(World J Urol)
Vol. 40
Issue 3
Pg. 747-754
(Mar 2022)
ISSN: 1433-8726 [Electronic] Germany |
PMID | 34671856
(Publication Type: Journal Article, Randomized Controlled Trial)
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Copyright | © 2021. The Author(s). |
Chemical References |
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Topics |
- Biomarkers
- Carcinoma, Renal Cell
(pathology)
- Cytoreduction Surgical Procedures
- Humans
- Kidney Neoplasms
(pathology)
- Machine Learning
- Nephrectomy
(methods)
- Retrospective Studies
- Systemic Inflammatory Response Syndrome
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