A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma

Sci Rep. 2021 Apr 21;11(1):8650. doi: 10.1038/s41598-021-88177-9.

Abstract

After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4-8.6) and 5.4 years (4.0-7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Carcinoma, Renal Cell / diagnosis
  • Carcinoma, Renal Cell / mortality*
  • Carcinoma, Renal Cell / pathology
  • Carcinoma, Renal Cell / surgery
  • Disease-Free Survival
  • Female
  • Humans
  • Kidney Neoplasms / diagnosis
  • Kidney Neoplasms / mortality*
  • Kidney Neoplasms / pathology
  • Kidney Neoplasms / surgery
  • Male
  • Middle Aged
  • Models, Statistical
  • Prognosis
  • Proportional Hazards Models
  • Risk Factors
  • Young Adult