Seungcheol Kang1, Han-Soo Kim1, SungJu Kim2, Wanlim Kim1, Ilkyu Han3. 1. Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea; Musculoskeletal Tumor Center, Seoul National University Cancer Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea. 2. Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 136-701, Republic of Korea. 3. Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea; Musculoskeletal Tumor Center, Seoul National University Cancer Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea. Electronic address: hik19@snu.ac.kr.
Abstract
BACKGROUND: Recursive 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. METHODS: A 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. RESULTS: RPA 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 ⩾ 12 months or high grade/metastasectomy; (3) group C: low grade/no metastasectomy/DFI < 12 months or high grade/no metastasectomy/DFI ⩾ 12 months; and (4) group D: high grade/no metastasectomy/DFI < 12 months. 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%. CONCLUSION: Our prognostic model using RPA successfully divides STS patients with metastasis into groups that can be easily implemented using standard clinical parameters.
BACKGROUND: Recursive 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. METHODS: A 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. RESULTS: RPA 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 ⩾ 12 months or high grade/metastasectomy; (3) group C: low grade/no metastasectomy/DFI < 12 months or high grade/no metastasectomy/DFI ⩾ 12 months; and (4) group D: high grade/no metastasectomy/DFI < 12 months. 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%. CONCLUSION: Our prognostic model using RPA successfully divides STS patients with metastasis into groups that can be easily implemented using standard clinical parameters.
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