A Pathology-Based Model for Postoperative Recurrence or Metastasis Prediction and Adjuvant Immunotherapy Implications of Clear-Cell Renal Cell Carcinoma: A Multicenter, Retrospective Study.

Abstract

Approximately 30% of patients with clear cell renal cell carcinoma (ccRCC) experience recurrence after surgery, making it crucial to improve the prognosis for these patients. Stratification of patients with nonmetastatic ccRCC based on the risk of postoperative recurrence helps guide adjuvant immunotherapy after surgery. We enrolled 2154 patients from two centers. Only patients without postoperative adjuvant therapy were used to analyze risk factors and construct models. A multivariable model was constructed to predict disease-free survival (DFS) and stratify patients. The log-rank test was used to examine the effects of immune checkpoint inhibitors (ICIs) and targeted therapy on DFS across different strata. We identified seven independent risk factors: sex, microvascular invasion (MVI), T stage, pathological grade, sarcomatoid differentiation, necrosis, and capsular involvement. Using these characteristics, a prognostic model for nonmetastatic ccRCC was constructed. Using the constructed model, we stratified patients and validated stratification efficacy with DFS, overall survival (OS), and cancer-specific survival (CSS) as endpoints (all P < 0.001). With our model, we stratified patients who either received or did not receive postoperative adjuvant therapy and found that ICIs significantly improved DFS in high-risk patients. Compared with the current medication criteria in clinical trials for ICIs, our model demonstrated superior overall performance. We identified seven crucial prognostic features influencing the prognosis of nonmetastatic ccRCC and developed a prognostic model using these features. On the basis of our model, we stratified patients and discovered that high-risk patients could benefit from treatment with ICIs.