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Abstract
Background & AimsBody composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Here we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE). MethodsThis retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010–2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival (OS) and performed multivariate analysis including established estimates of survival. ResultsUnivariate survival analysis revealed that impaired median OS was predicted by low SM volume (p < 0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. ConclusionsSkeletal muscle volume is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine. Impact and implicationsBody composition assessment parameters, especially skeletal muscle mass, have been identified as relevant prognostic factors for many diseases and treatments. In this study skeletal muscle volume has been identified as an independent prognostic factor for patients with HCC undergoing TACE. Therefore, skeletal muscle mass as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with AI evaluation is essential for automated body composition assessment quantification output enabling broad availability in multidisciplinary case discussions.
Published Version
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