BACKGROUND: Body composition and inflammation are gaining importance for prognostication in cancer. This study investigated the individual and combined utility of the preoperative skeletal muscle index (SMI) and the modified Glasgow Prognostic Score (mGPS) for estimating postoperative outcomes in patients with localized renal cell carcinoma (RCC) undergoing nephrectomy. METHODS: The authors performed a retrospective review of 352 patients with localized RCC. SMI was measured via computed tomography or magnetic resonance imaging. Patients met the criteria for sarcopenia by body mass index-and sex-stratified thresholds. Multivariable and Kaplan-Meier analyses of associations of sarcopenia and mGPS with overall survival (OS), recurrence-free survival (RFS), and cancer-specific survival (CSS) were performed. Variables were analyzed independently and combined into risk groups: low risk (nonsarcopenic, low mGPS), medium risk (sarcopenia only), medium risk (inflammation only), and high risk (sarcopenic, high mGPS). Receiver operating characteristic (ROC) curves were used to analyze risk groups in comparison with the Stage, Size, Grade, and Necrosis (SSIGN) score and the modified International Metastatic RCC Database Consortium (IMDC) score. RESULTS: The majority of the patients were at stage pT3 (63%), 39.5% of the patients were sarcopenic, and 19.3% had an elevated mGPS at the baseline. The median follow-up time was 30.4 months. Sarcopenia and mGPS were independently associated with worse OS (hazard ratio for sarcopenia, 1.64; P = .006; hazard ratio for mGPS, 1.72; P = .012), CSS, and RFS. Risk groups had an increasing association with worse RFS (P = .015) and CSS (P = .004) but not OS (P = .087). ROC analyses demonstrated a higher area under the curve for risk groups in comparison with the SSIGN and IMDC scores at 5 years. CONCLUSIONS: Sarcopenia and an elevated mGPS were associated with worse clinical outcomes in this study of patients with localized RCC. This has implications for preoperative prognostication and treatment decision-making.
Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinicfriendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.
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