Body composition analysis by radiological imaging – methods, applications, and prospects

Nicolas Linder; Timm Denecke; Harald Busse

doi:10.1055/a-2263-1501

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Table of Contents

Rofo 2024; 196(10): 1046-1054
DOI: 10.1055/a-2263-1501

Review

Radiologische Bestimmung der Gewebezusammensetzung im menschlichen Körper (Body Composition) – Methoden, Anwendungen und Aussichten

Article in several languages: English | deutsch

Authors

Nicolas Linder

¹Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany

²Division of Radiology and Nuclear Medicine, Kantonsspital St. Gallen, Sankt Gallen, Switzerland
Timm Denecke

¹Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany
Harald Busse

¹Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Leipzig, Germany

Abstract

Zusammenfassung

Hintergrund Die vorliegende Arbeit stellt die quantitative Erfassung der Gewebezusammensetzung im menschlichen Körper (Body Composition) mit den Mitteln der Radiologie vor. Derartige Analysen gewinnen vor allem bei onkologischen und metabolischen Fragestellungen an Bedeutung. Zielsetzung ist es, einer radiologischen Leserschaft die unterschiedlichen Methoden und Definitionen auf diesem Gebiet vorzustellen, um deren Anwendung und Verbreitung zu erleichtern. Das Hauptaugenmerk gilt dabei der radiologischen Schnittbildgebung.

Methoden Die Übersicht stützt sich auf eine aktuelle Literaturrecherche im Katalog der US-amerikanischen National Library of Medicine (pubmed.gov) mit entsprechenden Suchbegriffen (body composition, obesity, sarcopenia, osteopenia in Verbindung mit imaging bzw. radiology), sowie auf eigene Arbeiten und Erfahrungen, insbesondere mit der MRT- und CT-gestützten Analyse abdomineller Fettkompartimente und Muskelgruppen.

Ergebnisse und Schlussfolgerung Zentrale Nachverarbeitungsmethoden wie die Segmentierung von tomografischen Datensätzen sind inzwischen gut etabliert und finden in zahlreichen klinischen Studien Anwendung, u. a. in der Adipositas-Chirurgie. Für die verlässliche Beurteilung der radiologischen Messgrößen, z. B. einer Verfettung von Leber oder Muskulatur, sind validierte Referenzwerte erforderlich. Ansätze der Künstlichen Intelligenz (Deep Learning) ermöglichen bereits heute die automatisierte Segmentierung unterschiedlicher Gewebe und Kompartimente, damit die umfänglichen Datensätze zeiteffizient bearbeitet werden können – beim sogenannten opportunistischen Screening sogar retrospektiv aus diagnostischen Untersuchungen. Als Limitation gilt die Verfügbarkeit von Analyse-Werkzeugen sowie geeigneter Datensätze für das KI-Training.

Kernaussagen

Radiologische Bildgebungsmethoden werden zunehmend zur Bestimmung der Körperzusammensetzung (Body Composition, BC) herangezogen.
Die BC-Parameter sind in der Regel quantitativ und gut reproduzierbar
CT-Bilddaten aus klinischen Routineuntersuchungen lassen sich retrospektiv für eine BC-Analyse verwenden
Prospektiv bieten sich MRT-Untersuchungen an, um auch organspezifische BC-Parameter zu bestimmen
Perspektivisch bedeutsam erscheinen automatisierte und vertiefte Analyseverfahren (Deep Learning bzw. Radiomics)

Zitierweise

Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging – methods, applications, and prospects. Fortschr Röntgenstr 2024; 196: 1046 – 1055

Keywords

CT - MR-imaging - physiological studies - treatment effects - CT-quantitative - metabolic Disorders

Full Text

References

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