ArticleInternational journal of environmental research and public health2021
Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrated Assessment of Sarcopenia in Patients with Gastric Cancer Using Deep Learning and Radiomics.Journal of cachexia, sarcopenia and muscle · 2026Article
- Construction of a severity prediction model for hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease based on machine learning.European journal of medical research · 2025Article
- Identifying risk factors for sarcopenia using machine learning: insights from multimodal data.European geriatric medicine · 2025Article
- Value of grayscale histogram analysis based on ultrasound images in diagnosing sarcopenia.Quantitative imaging in medicine and surgery · 2025Article
- Development and validation of machine learning models for classifying cancer-related sarcopenia using Kinect-based mixed-reality exercises in breast cancer survivors.Translational cancer research · 2025Article
- Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images.Journal of imaging informatics in medicine · 2025Article
- Optimal lumbar vertebral level for trunk muscle CT assessments in opportunistic sarcopenia screening: a cross-sectional study.BMJ open · 2025Article
- Sarcopenia in liver transplantation: A comprehensive bibliometric study of current research trends and future directions.Open life sciences · 2025Article
- The Management of Sarcopenic Dysphagia: A Multidisciplinary Approach Leveraging Emerging Technologies.Aging and disease · 2024Review
- Radiomics signature of osteoarthritis: Current status and perspective.Journal of orthopaedic translation · 2024Review
- Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma.Journal of Zhejiang University. Science. B · 2024Article
- Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets.Frontiers in public health · 2024Article
- Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data.Healthcare (Basel, Switzerland) · 2023Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- Radiomics of spinal muscles: toward a radiological biomarker for allograft rejection in lung transplant.La Radiologia medica · 2023Article
- Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma.Frontiers in nutrition · 2023Article
- Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.BMC geriatrics · 2022Article
- Sarcopenia: imaging assessment and clinical application.Abdominal radiology (New York) · 2022Review
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Abstract
The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.
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