Post-metastasis survival in extremity soft tissue sarcoma: A recursive partitioning analysis of prognostic factors

Abstract

BackgroundRecursive partitioning analysis (RPA) enables grouping of patients into homogeneous prognostic groups in a visually intuitive form and has the capacity to account for complex interactions among prognostic variables. In this study, we employed RPA to generate a prognostic model for extremity soft tissue sarcoma (STS) patients with metastatic disease. MethodsA retrospective review was conducted on 135 patients with metastatic STS who had undergone surgical removal of their primary tumours. Patient and tumour variables along with the performance of metastasectomy were analysed for possible prognostic effect on post-metastatic survival. Significant prognostic factors on multivariate analysis were incorporated into RPA to build regression trees for the prediction of post-metastatic survival. ResultsRPA identified six terminal nodes based on histological grade, performance of metastasectomy and disease-free interval (DFI). Based on the median survival time of the terminal nodes, four prognostic groups with significantly different post-metastatic survival were generated: (1) group A: low grade/metastasectomy; (2) group B: low grade/no metastasectomy/DFI⩾12months or high grade/metastasectomy; (3) group C: low grade/no metastasectomy/DFI<12months or high grade/no metastasectomy/DFI⩾12months; and (4) group D: high grade/no metastasectomy/DFI<12months. The 3-year survival rates for each group were: group A, 76.1±9.6%; group B, 42.3±10.3%; group C, 18.8±8.0%; and group D, 0.0±0.0%. ConclusionOur prognostic model using RPA successfully divides STS patients with metastasis into groups that can be easily implemented using standard clinical parameters.