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On-line Access: 2024-01-02
Received: 2023-06-09
Revision Accepted: 2023-08-25
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Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma
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Zhikun LIU, Yichao WU, Abid Ali KHAN, Lun LU, Jianguo WANG, Jun CHEN, Ningyang JIA, Shusen ZHENG, Xiao XU. Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma[J]. Journal of Zhejiang University Science B, 2024, 25(1): 83-90.
@article{title="Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma",
author="Zhikun LIU, Yichao WU, Abid Ali KHAN, Lun LU, Jianguo WANG, Jun CHEN, Ningyang JIA, Shusen ZHENG, Xiao XU",
journal="Journal of Zhejiang University Science B",
volume="25",
number="1",
pages="83-90",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2300363"
}
%0 Journal Article
%T Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma
%A Zhikun LIU
%A Yichao WU
%A Abid Ali KHAN
%A Lun LU
%A Jianguo WANG
%A Jun CHEN
%A Ningyang JIA
%A Shusen ZHENG
%A Xiao XU
%J Journal of Zhejiang University SCIENCE B
%V 25
%N 1
%P 83-90
%@ 1673-1581
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2300363
TY - JOUR
T1 - Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma
A1 - Zhikun LIU
A1 - Yichao WU
A1 - Abid Ali KHAN
A1 - Lun LU
A1 - Jianguo WANG
A1 - Jun CHEN
A1 - Ningyang JIA
A1 - Shusen ZHENG
A1 - Xiao XU
J0 - Journal of Zhejiang University Science B
VL - 25
IS - 1
SP - 83
EP - 90
%@ 1673-1581
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2300363
Abstract: Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscle (SM) mass that may be age-related or the result of malnutrition in cancer patients (Cruz-Jentoft and Sayer, 2019). Preoperative sarcopenia in HCC patients treated with hepatectomy or liver transplantation is an independent risk factor for poor survival (Voron et al., 2015; van Vugt et al., 2016). Previous studies have used various criteria to define sarcopenia, including muscle area and density. However, the lack of standardized diagnostic methods for sarcopenia limits their clinical use. In 2018, the European Working Group on sarcopenia in Older People (EWGSOP) renewed a consensus on the definition of sarcopenia: low muscle strength, loss of muscle quantity, and poor physical performance (Cruz-Jentoft et al., 2019). Radiological imaging-based measurement of muscle quantity or mass is most commonly used to evaluate the degree of sarcopenia. The gold standard is to measure the SM and/or psoas muscle (PM) area using abdominal computed tomography (CT) at the third lumbar vertebra (L3), as it is linearly correlated to whole-body SM mass (van Vugt et al., 2016). According to a “North American Expert Opinion Statement on sarcopenia,” SM index (SMI) is the preferred measure of sarcopenia (Carey et al., 2019). The variability between morphometric muscle indexes revealed that they have different clinical relevance and are generally not applicable to broader populations (Esser et al., 2019).
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