Post-metastasis survival in extremity soft tissue sarcoma: A recursive partitioning analysis of prognostic factors |
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| Affiliation: | 1. Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea;2. Musculoskeletal Tumor Center, Seoul National University Cancer Hospital, 101 Daehak-ro Jongno-gu, Seoul 110-744, Republic of Korea;3. Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 136-701, Republic of Korea;1. Department of Internal Medicine, Pusan National University School of Medicine, Busan, Korea;2. Department of Pathology, Pusan National University School of Medicine, Busan, Korea;1. Department of Pathology, Brigham and Women''s Hospital and Harvard Medical School, Boston, MA, USA;2. Department of Medical Oncology, Dana–Farber Cancer Institute, Harvard Medical School, Boston, MA, USA;3. Department of Thoracic Surgery, Brigham and Women''s Hospital and Harvard Medical School, Boston, MA, USA;1. Department of Surgery, University of North Carolina, at Chapel Hill, Chapel Hill, NC;2. Department of Medical Oncology, University of North Carolina, at Chapel Hill, Chapel Hill, NC;3. Alliance Statistics and Data Center, Duke University Medical Center, Durham, NC;4. Alliance Statistics and Data Center, MD Anderson Cancer Center, Houston, TX;5. Program in Women''s Oncology, Women and Infants Hospital of Rhode Island, Providence, RI;6. Alpert Medical School of Brown University, Providence, RI;7. Department of Medical Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY;8. Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA;9. Department of Surgery, Brigham and Women''s Hospital, Boston, MA |
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| 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 ⩾ 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%.ConclusionOur 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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| Keywords: | Soft tissue sarcoma Extremity Metastasis Survival Recursive partitioning analysis Grade Metastasectomy Disease-free interval |
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