Katia Charrière, Antoine Ragusa, Béatrice Genoux, Antoine Vilotitch, Svetlana Artemova, Charlène Dumont, Paul-Antoine Beaudoin, Pierre-Ephrem Madiot, Gilbert R. Ferretti, Ivan Bricault, Eric Fontaine, Jean-Luc Bosson, Alexandre Moreau-Gaudry, Joris Giai, Cécile Bétry
{"title":"ODIASP：用于SMI自动测定的开源软件-在住院患者群体中的应用","authors":"Katia Charrière, Antoine Ragusa, Béatrice Genoux, Antoine Vilotitch, Svetlana Artemova, Charlène Dumont, Paul-Antoine Beaudoin, Pierre-Ephrem Madiot, Gilbert R. Ferretti, Ivan Bricault, Eric Fontaine, Jean-Luc Bosson, Alexandre Moreau-Gaudry, Joris Giai, Cécile Bétry","doi":"10.1002/jcsm.70023","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement. The GLIM framework specifically suggests using skeletal muscle index (SMI) assessed via CT scan at the third lumbar level (L3) as a first-line approach. However, manual segmentation of muscle from CT images is often time-consuming and infrequently performed in clinical practice. This study is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Data were retrospectively collected from a clinical data warehouse at Grenoble Alpes University Hospital, including epidemiological and imaging data from CT scans. All consecutive adult patients admitted in 2018 to our tertiary centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>SMI was available for 2503 participants, 53.3% male, with a median age of 66 years (51–78) and a median BMI of 24.8 kg/m<sup>2</sup> (21.7–28.7). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm<sup>2</sup> [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open-source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.</p>\n </section>\n </div>","PeriodicalId":48911,"journal":{"name":"Journal of Cachexia Sarcopenia and Muscle","volume":"16 4","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcsm.70023","citationCount":"0","resultStr":"{\"title\":\"ODIASP: An Open-Source Software for Automated SMI Determination—Application to an Inpatient Population\",\"authors\":\"Katia Charrière, Antoine Ragusa, Béatrice Genoux, Antoine Vilotitch, Svetlana Artemova, Charlène Dumont, Paul-Antoine Beaudoin, Pierre-Ephrem Madiot, Gilbert R. 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This study is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Data were retrospectively collected from a clinical data warehouse at Grenoble Alpes University Hospital, including epidemiological and imaging data from CT scans. All consecutive adult patients admitted in 2018 to our tertiary centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>SMI was available for 2503 participants, 53.3% male, with a median age of 66 years (51–78) and a median BMI of 24.8 kg/m<sup>2</sup> (21.7–28.7). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm<sup>2</sup> [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. 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ODIASP: An Open-Source Software for Automated SMI Determination—Application to an Inpatient Population
Background
The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement. The GLIM framework specifically suggests using skeletal muscle index (SMI) assessed via CT scan at the third lumbar level (L3) as a first-line approach. However, manual segmentation of muscle from CT images is often time-consuming and infrequently performed in clinical practice. This study is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination.
Methods
Data were retrospectively collected from a clinical data warehouse at Grenoble Alpes University Hospital, including epidemiological and imaging data from CT scans. All consecutive adult patients admitted in 2018 to our tertiary centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.
Results
SMI was available for 2503 participants, 53.3% male, with a median age of 66 years (51–78) and a median BMI of 24.8 kg/m2 (21.7–28.7). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings.
Conclusions
This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open-source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.
期刊介绍：
The Journal of Cachexia, Sarcopenia and Muscle is a peer-reviewed international journal dedicated to publishing materials related to cachexia and sarcopenia, as well as body composition and its physiological and pathophysiological changes across the lifespan and in response to various illnesses from all fields of life sciences. The journal aims to provide a reliable resource for professionals interested in related research or involved in the clinical care of affected patients, such as those suffering from AIDS, cancer, chronic heart failure, chronic lung disease, liver cirrhosis, chronic kidney failure, rheumatoid arthritis, or sepsis.
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