PDF(Pubmed)
Abstract：
Determination of body composition (the relative distribution of fat, muscle, and bone) has been used effectively to assess the risk of progression and overall clinical outcomes in different malignancies. Sarcopenia (loss of muscle mass) is especially associated with poor clinical outcomes in cancer. However, estimation of muscle mass through CT scan has been a cumbersome, manually intensive process requiring accurate contouring through dedicated personnel hours. Recently, fully automated technologies that can determine body composition in minutes have been developed and shown to be highly accurate in determining muscle, bone, and fat mass. We employed a fully automated technology, and analyzed images from a publicly available cancer imaging archive dataset (TCIA) and a tertiary academic center. The results show that adrenocortical carcinomas (ACC) have relatively sarcopenia compared to benign adrenal lesions. In addition, functional ACCs have accelerated sarcopenia compared to non-functional ACCs. Further longitudinal research might shed further light on the relationship between body component distribution and ACC prognosis, which will help us incorporate more nutritional strategies in cancer therapy.
摘要：
身体成分的测定（脂肪的相对分布，肌肉，和骨）已有效用于评估不同恶性肿瘤的进展风险和总体临床结局。肌肉减少症(肌肉质量损失)与癌症的不良临床结果特别相关。然而,通过CT扫描估计肌肉质量一直是一个麻烦，手动密集的过程需要准确的轮廓通过专门的人员小时。最近,可以在几分钟内确定身体成分的全自动技术已经被开发出来，并被证明在确定肌肉方面非常准确，骨头,和脂肪量。我们采用了完全自动化的技术，并分析了来自公开可用的癌症成像档案数据集（TCIA）和三级学术中心的图像。结果表明，与良性肾上腺病变相比，肾上腺皮质癌（ACC）具有相对的少肌症。此外,与非功能性ACCs相比,功能性ACCs具有加速的肌肉减少症。进一步的纵向研究可能会进一步阐明身体成分分布与ACC预后之间的关系，这将有助于我们在癌症治疗中纳入更多的营养策略。
