Charrière Katia, Ragusa Antoine, Genoux Béatrice, Vilotitch Antoine, Artemova Svetlana, Dumont Charlène, Beaudoin Paul-Antoine, Madiot Pierre-Ephrem, Ferretti Gilbert R, Bricault Ivan, Fontaine Eric, Bosson Jean-Luc, Moreau-Gaudry Alexandre, Giai Joris, Bétry Cécile
Public Health Department, Univ. Grenoble Alpes, Clinical Investigation Center-Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France.
Univ. Grenoble Alpes, Méthodologie de l'information en Santé, Biostatistiques, Recherche clinique et Innovation Technologique, Pôle Santé Publique, CHU Grenoble Alpes, Grenoble, France.
J Cachexia Sarcopenia Muscle. 2025 Aug;16(4):e70023. doi: 10.1002/jcsm.70023.
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.
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.
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 (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 [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.
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.
营养不良的诊断已随着全球营养不良领导倡议（GLIM）的建议而发展，该倡议主张整合包括肌肉量测量在内的表型标准。GLIM框架特别建议将通过第三腰椎（L3）水平的CT扫描评估的骨骼肌指数（SMI）作为一线方法。然而，从CT图像中手动分割肌肉通常很耗时，并且在临床实践中很少进行。本研究旨在开发并验证一种名为ODIASP的开放获取、简单的软件工具，用于自动测定SMI。
从格勒诺布尔阿尔卑斯大学医院的临床数据仓库中回顾性收集数据，包括CT扫描的流行病学和影像数据。纳入2018年入住我们三级中心的所有连续成年患者，这些患者至少接受了一次在L3椎体水平采集图像的CT扫描且记录了身高。ODIASP结合了两种算法来自动选择L3切片并进行骨骼肌分割，确保过程无缝衔接。使用组内相关系数（ICC）评估使用ODIASP获得的横断面肌肉面积（CSMA）值与参考方法（即手动测定）之间的一致性。还评估了SMI降低的患病率。
2503名参与者有SMI数据，其中男性占53.3%，中位年龄为66岁（51 - 78岁），中位BMI为24.8kg/m²（21.7 - 28.7）。在674次扫描的验证子集中，参考方法与ODIASP之间的一致性很强（ICC：0.971；95%CI：0.825 - 0.989），在纠正系统高估（5.8cm[5.4 - 6.3]）后提高到极佳（ICC：0.984；95%CI：0.982 - 0.986），表明一致性极佳。总体SMI降低的患病率为9.1%（男性为11.0%，女性为6.6%）。ODIASP软件可作为可下载的可执行文件获取，以支持其在研究环境中的使用。
本研究表明，ODIASP是一种用于从CT扫描自动测定L3椎体水平SMI的可靠工具。将经过验证的人工智能算法集成到一个简单的开源软件中，能够对不同患者群体进行可扩展、标准化的SMI评估，并支持未来整合到临床工作流程中以改善营养评估。
