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Introduction: Prediction models are increasingly being used to predict outcomes after surgery, and such a model would be a precious tool for patients with clear cell renal cell carcinoma (ccRCC) after surgery. Aim: To develop a comprehensive model for predicting disease-free survival (DFS) in patients with localized ccRCC. Material and methods: In a retrospective analysis of 612 patients, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to identify significant predictors, and then risk factors were used to construct a prognostic model. Harrell's concordance index (C-index) was used to assess the accuracy of the model. Results: The lymphocyte-to-monocyte ratio (LMR), Mayo Clinic stage, size, grade, necrosis score (SSIGN), and Mayo adhesive probability score (MAPS) were the significant risk factors screened by LASSO Cox regression and reconfirmed by multivariate Cox regression analysis in 44 variables. Then a model was constructed by combining the LMR, SSIGN, and MAPS. The C-index of the LMR-SSIGN-MAPS model was greater than the SSIGN score alone. Kaplan-Meier survival analysis demonstrated a significant association between higher LMR-SSIGN-MAPS score and poorer DFS. Conclusions: The LMR-SSIGN-MAPS model, which consists of preoperative inflammation biomarkers, a perinephric adipose tissue image-based scoring system, and pathological features, showed the strengths of easy-to-use and high predictability and might also be used as a promising prognosis model in predicting DFS for patients with localized ccRCC. [ABSTRACT FROM AUTHOR]