Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies
Abstract
Abstract Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessing steps to extract body composition measures from routine computed tomography (CT) scans. In 337 surgical oncology patients, total skeletal muscle tissue (SMtotal), psoas muscle tissue (SMpsoas), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were quantified both manually and using the pipeline. Automated and manual measurements showed strong correlations (SMpsoas: r = 0.776, VAT: r = 0.993, SAT: r = 0.984; all P < 0.001). Measurement discrepancies primarily resulted from segmentation errors, anatomical anomalies or image irregularities. SMpsoas measurements showed substantial variability depending on slice selection, whereas SMtotal, averaged across all L3 levels, provided greater measurement stability. Overall, SMtotal performed comparably to SMpsoas in predicting overall survival (OS). In summary, body composition measures derived from the pipeline strongly correlated with manual measurements and were prognostic for OS. The increased stability of SMtotal across vertebral levels suggests it may serve as a more reliable alternative to psoas-based assessments. Future studies should address the identified areas of improvement to enhance the accuracy of automated segmentation models.
Article Details
Authors (12)
Felix O. Hofmann
Christian Heiliger
Tengis Tschaidse
Stefanie Jarmusch
Liv A. Auhage
Ughur Aghamaliyev
Alena B. Gesenhues
Tobias S. Schiergens
Hanno Niess
Matthias Ilmer
Jens Werner
From Bielefeld University, Medical School and University Medical Center Ostwestfalen-Lippe, Campus Hospital Lippe, Detmold, Germany (J.H.); the Department of Radiation Oncology, Medical University of Graz, Graz, Austria (T.B.); the Clinical Trials Unit, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany (C.S.); the Institute of Surgical Pathology, University Medical Center Freiburg, Germany (P.B.); the Department of Surgery, University Medical Center Schleswig-Holstein–Campus Lübeck, Lübeck, Germany (B.K., T.K.); Comprehensive Cancer Center Augsburg, Faculty of Medicine, University of Augsburg, Augsburg, Germany (R.C.); the Department of General and Visceral Surgery, University Medical Center Freiburg, Freiburg, Germany (S.U.); the Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg–Eppendorf, Hamburg, Germany (J.R.I.); the Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute and San Raffaele Vita-Salute Universi...
Bernhard W. Renz