COMPARATIVE STUDY
JOURNAL ARTICLE
RESEARCH SUPPORT, U.S. GOV'T, P.H.S.
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A comparison of staging systems for localized extremity soft tissue sarcoma.

Cancer 2000 June 16
BACKGROUND: Staging systems for soft tissue sarcoma (STS) are important to identify patients with similar systemic risk who might benefit from specific treatments. This study compared four commonly used staging systems for predicting systemic outcomes of patients with localized extremity STS, as proposed by the fourth and fifth editions of the American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) staging system, the Memorial Sloan-Kettering Cancer Center (MSK) system, and the Surgical Staging System (SSS) of the Musculoskeletal Tumor Society.

METHODS: Three hundred consecutive adult patients with newly diagnosed nonmetastatic STS of the lower extremity were treated at Memorial Sloan-Kettering Cancer Center between 1982 and 1989. Metastasis free survival was the end point of the study. The prognostic value of the four staging systems and their components were examined in univariate and multivariate analyses. The Akaike information criterion (AIC) was used to identify the system that best predicted the risk of systemic recurrence.

RESULTS: Compartment status, depth, grade, and size were all independent predictors of outcome within their respective staging systems. However, when compared with one another, only depth, grade, and size retained their prognostic significance. Of the four models, the AIC predicted that the MSK was the best predictor of systemic relapse, followed by the fifth edition of the AJCC/UICC staging system.

CONCLUSIONS: Staging systems such as the MSK system or the fifth edition of the AJCC/UICC system, which include tumor depth, grade, and size as prognostic factors, are the most predictive of systemic relapse in patients presenting with localized extremity STS. Both of these systems identify the same group of patients at the highest risk who would be the most suitable for adjuvant chemotherapy trials.

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