TY  - JOUR
A1  - Müller, Lukas
A1  - Mähringer-Kunz, Aline
A1  - Auer, Timo Alexander
A1  - Fehrenbach, Uli
A1  - Gebauer, Bernhard
A1  - Haubold, Johannes
A1  - Schaarschmidt, Benedikt Michael Sebastian
A1  - Kim, Moon-Sung
A1  - Hosch, René
A1  - Nensa, Felix
A1  - Kleesiek, Jens Philipp
A1  - Diallo, Thierno Diawo
A1  - Eisenblätter, Michel
A1  - Kuzior, Hanna
A1  - Röhlen, Natascha
A1  - Bettinger, Dominik
A1  - Steinle, Verena Maria
A1  - Mayer, Philipp
A1  - Zopfs, David
A1  - Pinto dos Santos, Daniel
A1  - Klöckner, Roman Trutz
T1  - AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE: results from a multicenter study
T2  - JHEP Reports
N2  - Highlights:
• Assessment of body composition parameters in a large cohort of patients with HCC undergoing TACE.
• Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition.
• Skeletal muscle volume and related parameters were independent prognostic factors in patients with HCC undergoing TACE.

Background & Aims: Body composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Herein, we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE).
Methods: This 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, visceral adipose tissue, 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 and performed multivariate analysis including established estimates of survival.
Results: Univariate survival analysis revealed that impaired median overall survival was predicted by low SM (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. This predictive role of SM was confirmed in a subgroup analysis of patients with BCLC stage B.
Conclusions: SM 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 implications: Body composition assessment parameters, especially skeletal muscle volume, 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 hepatocellular carcinoma undergoing transarterial chemoembolization. Therefore, skeletal muscle volume 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 artificial intelligence is essential for automated, quantitative body composition assessment, enabling broad availability in multidisciplinary case discussions.
KW  - Hepatocellular Carcinoma
KW  - Artificial Intelligence
KW  - Transarterial Chemoembolization
KW  - Body Composition
Y1  - 2024
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/85770
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-857703
SN  - 2589-5559
VL  - 6
IS  - 8, 101125
SP  - 1
EP  - 11
PB  - Elsevier
CY  - Amsterdam
ER  -