Development of a Simplified Protocol for Respiratory Muscle Segmentation in Unenhanced Chest CT and Identification of New Radiomic Biomarkers of Sarcopenia in Lung Diseases: A Retrospective Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background/Objectives: Respiratory muscle sarcopenia worsens outcomes in chronic lung disease, and quantitative CT may provide objective biomarkers; this study aimed to develop a time-efficient segmentation protocol and identify radiomic biomarkers of respiratory muscle sarcopenia. Methods: This retrospective study analyzed 30 unenhanced chest CT from adult patients. The whole volume of the pectoralis major (PM), pectoralis minor (Pm), serratus anterior (SA), and fourth intercostal (4I) muscles were manually segmented. Patients were classified as sarcopenic or non-sarcopenic. Radiomics features and mean muscle density were extracted using PyRadiomics. Features associated with sarcopenia were selected using LASSO regression and backward stepwise selection. Four sets of slices consisting of one, three, five, and seven slices were then sampled from each muscle around a fixed anatomical landmark. Deviations of each set of slices from whole-muscle metrics were evaluated using MAE and MAPE. Results: Features selection identified 25 biomarkers of sarcopenia in PM, 24 in Pm, and 34 in SA. Variability-related features were significantly associated with sarcopenia (OR = 2.26; P = .012), while structural features showed an inverse association (OR = 0.18; P = .004). Mean muscle density and most radiomic features were well represented by single slice for every muscle. In the PM and Pm eight and six radiomic features were better approximated segmenting more than one slice (p < 0.05). Conclusions: Radiomics enables quantitative assessment of sarcopenia. For SA, a simplified segmentation protocol consisting of a single slice enables to approximate muscle density and radiomics of whole muscle volume. For PM and Pm, three or more slices allow a better representation of 8 and 6 radiomic features respectively.
Source: Journal of Clinical Medicine
DOI: 10.3390/jcm14248712
Authors: Riccardo Picasso, Maria Elena Susi, Giovanni Marcenaro, Marta Macciò, Federico Zaottini, Federico Pistoia, Ludovico La Grutta, Giulia Sollami, Arianna Maggio, Diego Bagnasco, Fulvio Braido, Melissa Ferraris, Ludovica Napoli, Benedetta Bondi, Giulia Carpani, Gaia Vettori, Maurizio Mongelli, Alessia Paglialonga, Carlo Martinoli
Institutions: Ospedale Policlinico San Martino, University of Genoa, Azienda Ospedaliera Universitaria Policlinico "Paolo Giaccone" di Palermo, University of Palermo, Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione, Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, Politecnico di Milano
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4417159191
