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基于CT检查影像组学早期肝细胞癌切除术后肿瘤复发的预测模型构建及其应用价值 被引量:11

Construction and application value of CT-based radiomics model for predicting recurrence of early-stage hepatocellular carcinoma after resection
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摘要 目的构建基于X线计算机体层摄影术(CT)检查影像组学早期肝细胞癌切除术后肿瘤复发的预测模型,探讨其应用价值。方法采用回顾性队列研究方法。收集2009年1月至2016年12月国内2家医疗中心收治的243例(南京医科大学第一附属医院165例、无锡市人民医院78例)行肝切除术治疗早期肝细胞癌患者的临床病理资料;男182例,女61例;中位年龄为57岁,年龄范围30~86岁。243例患者通过计算机产生随机数方法以2∶1比例分为训练集162例和测试集81例。利用影像组学技术,从CT检查动脉期和门静脉期的肿瘤内部及周边分别提取定量图像特征,每位患者提取3384个影像组学特征参数。在训练集中,通过联合多重特征选择算法[最大相关最小冗余(MRMR)和随机生存森林(RSF)的特征排序+LASSO-COX回归分析]对稳定的特征参数进行降维,建立影像组学标签并预测效能。采用单因素COX回归筛选肿瘤复发危险因素,采用多因素COX逐步向后回归分析构建2个影像组学预测模型,包括影像组学模型1(术前模型)和影像组学模型2(术后模型),并分别在训练集和测试集中验证模型效能。观察指标:(1)随访情况。(2)早期肝细胞癌切除术后肿瘤复发相关的影像组学标签建立。(3)早期肝细胞癌切除术后肿瘤复发相关的影像组学标签预测效能。(4)早期肝细胞癌切除术后肿瘤复发相关的影像组学预测模型构建。(5)早期肝细胞癌切除术后肿瘤复发相关的影像组学预测模型验证。(6)影像组学模型与其他临床统计学模型和现有肝细胞癌分期系统的预测效能比较。(7)影像组学模型对早期肝细胞癌切除术后肿瘤复发风险的分层分析。采用门诊或电话方式进行随访。术后2年内每3个月随访1次,2年后每6个月随访1次。随访内容包括病史采集,实验室指标检查和腹部B超检查。每6个月进行1次增强CT或磁共振成像(MRI)检查,若实验室� Objective To construct a computed tomography(CT)-based radiomics model for predicting tumor recurrence of early-stage hepatocellular carcinoma(HCC)after resection,and explore its application value.Methods The retrospective cohort study was conducted.The clinicopathological data of 243 patients with early-stage HCC who underwent hepatectomy in 2 medical centers between January 2009 and December 2016 were collected,including 165 in the First Affiliated Hospital of Nanjing Medical University and 78 in the Wuxi People′s Hospital.There were 182 males and 61 females,aged from 30 to 86 years,with a median age of 57 years.According to the random numbers showed in the computer,243 patients were randomly assigned into training dataset consisting of 162 patients and test dataset consisting of 81 patients,with a ratio of 2∶1.Using radiomics technique,a total of 3384 radiomics features were extracted from the tumor and its periphery at arterial-phase and portal-phase images of CT scan.In the training dataset,a radiomics signature was constructed and predicted its performance after dimension reduction of stable features by using aggregated feature selection algorithms[feature ranking via maximal relevance and minimal redundancy(MRMR)combined with random survival forest(RSF)+LASSO-COX regression analysis].Risk factors for tumor recurrence were selected using the univariate COX regression analysis,and two radiomics models including radiomics 1(preoperative)and radiomics 2(postoperative)were constructed and predicted their performance using backward stepwise multivariate COX regression analysis.The two models were validated in the training and test dataset.Observation indicators:(1)follow-up;(2)construction of HCC recurrence-related radiomics signature for early-stage HCC after resection;(3)prediction performance of HCC recurrence-related radiomics signature for early-stage HCC after resection;(4)construction of HCC recurrence-related radiomics prediction model for early-stage HCC after resection;(5)validation of HCC recurren
作者 季顾惟 王科 吴晓峰 夏永祥 李长贤 张慧 王宏伟 吴鸣宇 蔡兵 李相成 王学浩 Ji Guwei;Wang Ke;Wu Xiaofeng;Xia Yongxiang;Li Changxian;Zhang Hui;Wang Hongwei;Wu Mingyu;Cai Bing;Li Xiangcheng;Wang Xuehao(Hepatobiliary Center,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China;Department of Hepatobiliary Surgery,Wuxi People′s Hospital,Wuxi 214023,Jiangsu Province,China)
出处 《中华消化外科杂志》 CAS CSCD 北大核心 2020年第2期204-216,共13页 Chinese Journal of Digestive Surgery
基金 国家自然科学基金重点项目(31930020) 国家自然科学基金(81530048、81470901、81670570) 江苏省重点病种规范化诊疗研究(BE2016789)。
关键词 肝肿瘤 影像组学 预测模型 肿瘤复发 手术切除 Hepatic neoplasms Radiomics Prediction model Tumor recurrence Surgical resection
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