AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study
Lukas Müller,
Aline Mähringer-Kunz,
Timo Alexander Auer,
Uli Fehrenbach,
Bernhard Gebauer,
Johannes Haubold,
Benedikt Michael Schaarschmidt,
Moon-Sung Kim,
René Hosch,
Felix Nensa,
Jens Kleesiek,
Thierno D. Diallo,
Michel Eisenblätter,
Hanna Kuzior,
Natascha Roehlen,
Dominik Bettinger,
Verena Steinle,
Philipp Mayer,
David Zopfs,
Daniel Pinto Dos Santos,
Roman Kloeckner
Affiliations
Lukas Müller
Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
Aline Mähringer-Kunz
Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
Timo Alexander Auer
Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany; Berlin Institute of Health at Charité - University Medicine Berlin, Berlin, Germany
Uli Fehrenbach
Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
Bernhard Gebauer
Department of Radiology, Charité - University Medicine Berlin, Berlin, Germany
Johannes Haubold
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
Benedikt Michael Schaarschmidt
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Moon-Sung Kim
Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
René Hosch
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
Felix Nensa
Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
Jens Kleesiek
Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany
Thierno D. Diallo
Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
Michel Eisenblätter
Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany
Hanna Kuzior
Department of Diagnostic and Interventional Radiology, Freiburg University Hospital, Freiburg, Germany
Natascha Roehlen
Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
Dominik Bettinger
Department of Medicine II, Freiburg University Hospital, Freiburg, Germany
Verena Steinle
Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
Philipp Mayer
Department of Diagnostic and Interventional Radiology, University Medical Center Heidelberg, Heidelberg, Germany
David Zopfs
Department of Radiology, University Hospital Cologne, Cologne, Germany
Daniel Pinto Dos Santos
Department of Radiology, University Hospital Cologne, Cologne, Germany; Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
Roman Kloeckner
Institute of Interventional Radiology, University Hospital of Schleswig-Holstein – Campus Lübeck, Lübeck, Germany; Corresponding author. Address: Institute of Interventional Radiology, University Hospital of Schleswig-Holstein – Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany.
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.
