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

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

Publication History

Received: 24 June 2023

Accepted after revision: 24 December 2023

Article published online:
03 April 2024

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