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Abstract: Skeletal muscle loss, commonly known as sarcopenia, is highly prevalent and prognostic of adverse outcomes in oncology. However, there is limited information on adults with early breast cancer and examination of other skeletal muscle indices, despite the potential prognostic importance. This study characterizes and examines age‐related changes in body composition of adults with early breast cancer and describes the creation of a novel integrated muscle measure. Female patients diagnosed with stage I‐III breast cancer with abdominal computerized tomography (CT) scans within 12 weeks from diagnosis were identified from local tumor registry (N = 241). Skeletal muscle index (muscle area per height [cm2/m2]), skeletal muscle density, and subcutaneous and visceral adipose tissue areas, were determined from CT L3 lumbar segments. We calculated a novel integrated skeletal measure, skeletal muscle gauge, which combines skeletal muscle index and density (SMI × SMD). 241 patients were identified with available CT imaging. Median age 52 years and range of 23‐87. Skeletal muscle index and density significantly decreased with age. Using literature based cut‐points, older adults (≥65 years) had significantly higher proportions of sarcopenia (63 vs 28%) and myosteatosis (90 vs 11%) compared to younger adults (<50 years). Body mass index was positively correlated with skeletal muscle index and negatively correlated with muscle density. Skeletal muscle gauge correlated better with increasing age (ρ = 0.52) than with either skeletal muscle index (ρ = 0.20) or density (ρ = 0.46). Wide variations and age‐related changes in body composition metrics were found using routinely obtained abdominal CT imaging. Skeletal muscle index and density provide independent, complementary information, and the product of the two metrics, skeletal muscle gauge, requires further research to explore its impact on outcomes in women with curable breast cancer. [ABSTRACT FROM AUTHOR]

We hypothesized that amplification or overexpression of HER-2 (c-erbB-2), the Ki-67 antigen (Mib1), cyclin D-1 (CD1), interleukin-6 (IL-6), or the transforming growth factor beta II receptor, (TGFβRII), would predict relapse in women with early stage, estrogen (ER) and/or progesterone receptor (PR) positive breast cancer treated with tamoxifen. Conditional logistic regression models and a new novel analytic method––support vector machines (SVM) were used to assess the effect of multiple variables on treatment outcome. All patients had stage I–IIIa breast cancer (AJCC version 5). We paired 63 patients who were disease-free on or after tamoxifen with 63 patients who had relapsed (total 126); both disease-free and relapsed patients were matched by duration of tamoxifen therapy and time to recurrence. These 126 patients also served as the training set for SVM analysis and 18 other patients used as a validation set for SVM. In a multivariate analysis, larger tumor size, increasing extent of lymph node involvement, and poorer tumor grade were significant predictors of relapse. When HER-2 or CD1 were added to the model both were borderline significant predictors of relapse. The SVM model, after including all of the clinical and marker variables in the 126 patients as a training set, correctly predicted relapse in 78% of the 18 patient validation samples. In this trial, HER-2 and CD1 proved of borderline significance as predictive factors for recurrence on tamoxifen. An SVM model that included all clinical and biologic variables correctly predicted relapse in >75% of patients. [ABSTRACT FROM AUTHOR]