Sarcopenia and systemic inflammation are associated with decreased survival after cytoreductive nephrectomy for metastatic renal cell carcinoma.

BACKGROUND: This study was aimed at assessing the associations of sarcopenia, muscle density, adiposity, and inflammation with overall survival (OS) after cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma. METHODS: In all, 158 patients undergoing CN from 2001 to 2014 had digitized preoperative imaging for tissue segmentation via Slice-O-Matic software (version 5.0) at the mid-L3 level. The skeletal muscle index was calculated with the skeletal muscle area (cm2 ) normalized for height (m2 ), and the skeletal muscle density (SMD) was calculated with average Hounsfield units. Adiposity was measured with the cross-sectional area (cm2 ) of visceral, subcutaneous, and intramuscular adiposity compartments and was similarly normalized for height. The average fat density was obtained in Hounsfield units. OS was estimated with the Kaplan-Meier method. Associations between body composition, inflammation metrics, and relevant clinicopathology and OS were assessed with univariable and multivariate Cox analyses. RESULTS: Seventy-six of the 158 patients (48%) were sarcopenic. Sarcopenia was associated with elevated neutrophil to lymphocyte ratios (NLRs; P = .02), increased age (P = .001), lower body mass indices (P = .009), greater modified Motzer scores (P = .019), and lower SMD (P = .006). The median OS was 15.0 and 29.4 months for sarcopenic and nonsarcopenic patients, respectively (P = .04). Elevated inflammation (NLR or C-reactive protein), in addition to sarcopenia, was independently associated with OS, with an elevated NLR ≥ 3.5 and sarcopenia associated with the poorest OS at 10.2 months. No associations were observed between measurements of muscle density or adiposity and OS. CONCLUSIONS: Sarcopenia and measures of high systemic inflammation are additively associated with inferior OS after CN and may be of use in preoperative risk stratification. LAY SUMMARY: Body composition and sarcopenia (a deficiency in skeletal musculature) have been shown to affect outcomes in cancer. We found that sarcopenic patients had poor survival in comparison with nonsarcopenic patients in the setting of metastatic renal cell carcinoma (mRCC). Patients with both elevated inflammation and sarcopenia had the poorest survival. Sarcopenia is an objective measure of nutrition that can assist in therapeutic counseling and decision-making for individualized treatment in mRCC.

Adiposity may predict survival in patients with advanced stage cancer treated with immunotherapy in phase 1 clinical trials.

BACKGROUND: Body mass index (BMI) is used to define obesity, but it is an imperfect measure of body composition. In the current study, the authors explored the association between types of fat and survival in patients treated with immunotherapy. METHODS: A retrospective analysis of 90 patients who were treated with immunotherapy on phase 1 clinical trials at the Winship Cancer Institute in Atlanta, Georgia, from 2009 through 2017 was performed. Overall survival (OS) and progression-free survival (PFS) were used to measure clinical outcomes. Baseline BMI and radiographic images at the middle of the third lumbar vertebrae were obtained. Fat densities were calculated and converted to indices (subcutaneous fat index [SFI], intermuscular fat index [IFI], and visceral fat index [VFI]) after dividing by height in meters squared. Risk groups were created using recursive partitioning and the regression trees method for SFI and IFI, which were selected by stepwise variable selection among all fat-related variables. The Cox proportional hazards model and Kaplan-Meier method were used for the association with OS and PFS. RESULTS: The majority of patients (59%) were male and diagnosed with melanoma (33%) or gastrointestinal cancers (22%). The median BMI was 27.4 kg/m2 , the median SFI was 62.78, the median IFI was 4.06, and the median VFI was 40.53. Low-risk patients (those with an SFI ≥73) had a significantly longer OS (hazard ratio, 0.20; 95% CI, 0.09-0.46 [P < .001]) and PFS (hazard ratio, 0.38; 95% CI, 0.20-0.72 [P = .003]) compared with patients at intermediate risk (those with an SFI <73 and IFI <3.4) and poor risk (those with an SFI <73 and IFI ≥3.4). The Uno concordance statistics were found to be higher for fat risk groups than BMI in predicting OS (0.603 vs 0.574; P = .581) and PFS (0.602 vs 0.586; P = .71). CONCLUSIONS: Increased BMI, increased SFI, and decreased IFI may be associated with prolonged survival in patients with cancer who are treated with immunotherapy. Further studies are needed to elucidate the effect of adiposity on the host immune response to immunotherapy.

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos.

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 clinic-friendly 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.