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Journal of Zhejiang University SCIENCE B 2024 Vol.25 No.1 P.83-90

http://doi.org/10.1631/jzus.B2300363


Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma


Author(s):  Zhikun LIU, Yichao WU, Abid Ali KHAN, Lun LU, Jianguo WANG, Jun CHEN, Ningyang JIA, Shusen ZHENG, Xiao XU

Affiliation(s):  Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First Peoples Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China; more

Corresponding email(s):   zjxu@zju.edu.cn, zyzss@zju.edu.cn, ningyangjia@163.com

Key Words:  Deep learning, Radiomics, HCC, Sarcopenia, Prognosis


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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.

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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",
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pages="83-90",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2300363"
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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).

基于影像组学的深度学习评估肌少症对肝癌切除和移植患者预后的影响

刘治坤1,5,吴逸超2,5,Abid Ali KHAN2,5,陆伦3,王建国1,5,陈峻1,5,贾宁阳3,郑树森4,5,6,徐骁1,2,5
1浙江省肿瘤融合与智能医学实验室,浙江大学医学院附属杭州市第一人民医院肝胆胰外科,中国杭州市,310006
2浙江大学医学院,中国杭州市,310058
3海军医科大学东方肝胆外科医院放射科,中国上海市,200438
4浙江大学医学院附属第一附属医院肝胆胰外科,中国杭州市,310003
5国家卫健委多器官联合移植重点实验室,中国杭州市,310003
6树兰(杭州)医院肝胆胰外科,中国杭州市,310022
摘要:肌少症是指因持续骨骼肌含量流失、强度和功能下降引起的综合征,且与包括肝细胞癌(HCC)在内的肿瘤患者预后密切相关。目前该病的检测手段局限且无统一标准。本文旨在利用基于影像组学的深度学习(DL)技术评估肌少症,用于肝癌患者行肝脏部分切除术或肝移植术的预后预测。本研究回顾性纳入浙大一院肝癌手术切除492例(训练集+内部验证集)与肝癌肝移植173例患者(外部LT验证集),东方肝胆医院肝癌切除患者161例(外部验证集),并收集患者术前一个月内的腹部计算机断层扫描(CT)平扫期影像与临床资料;单中心肝切除术组入组患者按7:3随机分为训练集和内部验证集(训练集345例,验证集147例),肝移植组及第二中心肝癌切除组作为外部验证集,经训练集建立预测模型,并利用内部和外部验证集验证预测模型的预测性能;对训练集患者CT图像中第3腰椎骨(L3)层面的骨骼肌(SM)及腰大肌(PM)轮廓进行人工勾画;抽提SM与PM影像组学特征,随后利用自编码器(AutoEncoder)压缩特征,TFDeepSurv生存分析网络构建DL预后预测模型,预测HCC术后无瘤生存率(RFS)与总体生存时间(OS);最后计算时间依赖性受试者工作特征曲线(ROC)的曲线下面积(AUC)和一致性指数(C-index),采用应用净重新分类改善指数(NRI)和临床决策曲线(DCA)评价模型预测性能。最终从勾画的CT图像L3层面的SM及PM中采集相应肌肉中1343个影像组学特征。经AutoEncoder将此高阶影像组学特征降维至100个特征。运用TFDeepSurv生存分析网络完成DL预测模型的构建,将HCC患者根据预后的差异分为高危组和低危组,高危组HCC患者行肝部分切除手术后预后显著低于低危组患者。此外,通过Kaplan-Meier生存曲线分析等方法证实DL模型在内部及外部验证集、外部LT验证集中均可对肝癌患者术后的预后进行准确预测,一致性指数分别达0.775和0.613。NRI和DCA同样显示DL模型具有较高的预测性能。本研究创新性地提出了基于影像组学的DL技术构建的预后预测模型;该模型可在术前对肝癌手术切除和肝移植术后的生存风险进行个体化预测,从而实现对肝癌患者OS的早期预判,有助于制定合理的临床决策和指导临床实践。

关键词:影像组学;深度学习;肌少症;肝细胞肝癌;肝部分切除术;肝移植

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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