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肌肉减少症:影像学评估与临床应用。

Sarcopenia: imaging assessment and clinical application.

机构信息

Clinica di Radiologia EOC IIMSI, Lugano, Switzerland.

Ospedale Evangelico Betania, Napoli, Italy.

出版信息

Abdom Radiol (NY). 2022 Sep;47(9):3205-3216. doi: 10.1007/s00261-021-03294-3. Epub 2021 Oct 23.

DOI:10.1007/s00261-021-03294-3
PMID:34687326
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8536908/
Abstract

Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.

摘要

肌肉减少症是一种以肌肉质量和力量减少为特征的进行性、全身性骨骼肌疾病。它与不良后果的增加有关,包括跌倒、骨折、身体残疾和死亡,特别是在老年患者中。如今,肌肉减少症已成为一种能够预测患者临床结局的特定影像学生物标志物。肌肉纤维减少已被证明是癌症患者术前的不利预测因素,与术后并发症、发病率、死亡率较差以及对放化疗的耐受性降低有关。几种影像学方法,包括双能 X 射线吸收法、CT、MRI 和 US,可用于估计肌肉质量和质量以达到肌肉减少症的诊断。本文综述了肌肉减少症的临床意义,以及如何通过不同的影像学方法来评估这种情况,以及肌肉减少症影像学的未来展望。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/75043f8b345c/261_2021_3294_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/734430c971b8/261_2021_3294_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/94bb4b974eaf/261_2021_3294_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/75043f8b345c/261_2021_3294_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/734430c971b8/261_2021_3294_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/57b36a49556d/261_2021_3294_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/654f024b2079/261_2021_3294_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/edfc7ff95fac/261_2021_3294_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/94bb4b974eaf/261_2021_3294_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb98/9388462/75043f8b345c/261_2021_3294_Fig6_HTML.jpg

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Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography.基于 CT 肌肉影像组学特征的肌少症识别机器学习模型。
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Depletion of skeletal muscle mass adversely affects long-term outcomes for men undergoing gastrectomy for gastric cancer.
基于超声图像的灰度直方图分析在诊断肌肉减少症中的价值。
Quant Imaging Med Surg. 2025 Sep 1;15(9):7885-7895. doi: 10.21037/qims-2025-638. Epub 2025 Aug 19.
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