Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma

Article Radiology, Nuclear Medicine & Medical Imaging

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT

Sebastian Nowak et al.

Summary: This study developed an end-to-end automated deep learning pipeline for the large-scale opportunistic assessment of body composition metrics and sarcopenia biomarkers in clinical routine, achieving high agreement between manual and fully automated analyses.

EUROPEAN RADIOLOGY (2022)

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Review Geriatrics & Gerontology

Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis

Fanny Petermann-Rocha et al.

Summary: The diagnosis and prevalence of sarcopenia vary considerably, and future studies should follow current guidelines to facilitate comparison of results across different studies and populations globally.

JOURNAL OF CACHEXIA SARCOPENIA AND MUSCLE (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves

Kirti Magudia et al.

Summary: This study demonstrated the validity of fully automated, deep learning body composition (BC) analysis from abdominal CT examinations in predicting survival. Population reference curves for BC were generated, showing significant variations by age, race, and sex. The z scores derived from these curves better captured the demographic distribution of BC compared with standard methods and proved to be predictive of survival.

RADIOLOGY (2021)

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Article Engineering, Biomedical