Purpose Poor body composition metrics (BCM) are associated with inferior cancer outcomes; however, in early breast cancer (EBC) there is a paucity of evidence regarding BCM’s impact on toxicities. This study investigates associations between BCM and treatment-related toxicity in EBC patients receiving anthracyclines-taxane based chemotherapy. Experimental Design Pretreatment computerized tomography (CT) images were evaluated for skeletal muscle area (SMA), density (SMD), and fat tissue at the 3rd lumbar vertebrae. Skeletal muscle index (SMI) (SMA/height2) and skeletal muscle gauge (SMG=SMI x SMD) were also calculated. Relative risks (RR) are reported for associations between body composition measures and toxicity outcomes, after adjustment for age and body surface area (BSA). Results BCM were calculated for 151 patients with EBC (median age 49, range 23 to 75). Fifty patients (33%) developed grade 3 or 4 toxicity, which was significantly higher in those with low SMI (RR=1.29, p=0.002), low SMG (RR=1.09, p=0.01), and low LBM (RR=1.48, p=.002). ROC analysis showed the SMG measure to be the best predictor of grade 3 and 4 toxicity. Dividing SMG into tertiles showed toxicity rates of 46%, and 22% for lowest versus highest tertile, respectively (p=0.005). After adjusting for age and BSA, low SMG (<1475 units) was significantly associated with hematological (RR=2.12, p=0.02), gastrointestinal grade 3–4 toxicities (RR=6.49, p=0.02), and hospitalizations (RR=1.91, p=0.05). Conclusions Poor BCM are significantly associated with increased treatment-related toxicities. Further studies are needed to investigate how these metrics can be used to more precisely dose chemotherapy to reduce treatment related toxicity while maintaining efficacy.

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The proportions of muscle and fat tissues in the human body, referred to as body composition is a vital measurement for cancer patients. Body composition has been recently linked to patient survival and the onset/recurrence of several types of cancers in numerous cancer research studies. This paper introduces a fully automatic framework for the segmentation of muscle and fat tissues from CT images to estimate body composition. We developed a novel finite element method (FEM) deformable model that incorporates a priori shape information via a statistical deformation model (SDM) within the template-based segmentation framework. The proposed method was validated on 1000 abdominal and 530 thoracic CT images and we obtained very good segmentation results with Jaccard scores in excess of 90% for both the muscle and fat regions.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

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 [cm /m ]), 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.