基于CEMRI瘤内瘤周影像组学预测肝细胞癌分化程度的研究  

CEMRI-based intratumoral and peritumoral radiomics for predicting the degree of pathological differentiation of hepatocellular carcinoma

作　　者：陆煜杰 顾文豪[2] 许大波[2] 刘海峰 邢伟 LU Yujie;GU Wenhao;XU Dabo;LIU Haifeng;XING Wei(Medical College of Yangzhou University,Yangzhou 225009,China;Department of Radiology,The First People's Hospital of Taicang,Suzhou 215400,China;Department of Radiology,the Third Affiliated Hospital of Soochow University,Changzhou 213000,China)

机构地区：[1]扬州大学医学院,扬州225009 [2]太仓市第一人民医院影像科,苏州215400 [3]苏州大学附属第三医院放射科,常州213000

出　　处：《磁共振成像》2025年第3期51-57,共7页Chinese Journal of Magnetic Resonance Imaging

基　　金：2024年常州市第一人民医院临床研究专项(编号:2024-14);2024年度苏州市应用基础研究(医疗卫生)科技创新(第二批)指导性项目(编号:SYWD2024020)。

摘　　要：目的建立并验证基于对比增强磁共振成像(contrast enhanced magnetic resonance imaging,CEMRI)瘤内瘤周影像组学模型预测肝细胞癌(hepatocellular carcinoma,HCC)分化程度的价值。材料与方法回顾性分析2020年1月至2023年7月苏州大学附属第三医院213例经术后病理结果证实为HCC患者的资料(223个病灶),包括62个低度分化的HCC(poorly differentiated HCC,pHCC)、161个非低度分化的HCC(non-poorly differentiated HCC,npHCC)。采用交叉验证方法按照7∶3的比例随机分为训练集(149例,156个HCC病灶)、测试集(64例,67个HCC病灶)。使用ITK-SNAP软件在动脉期、门静脉期和延迟期图像上勾画HCC全域感兴趣区(region of interest,ROI),基于PyRadiomics软件包共提取3045个组学特征,先后采用Spearman相关性分析、最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归和最大相关性-最小冗余(maximum relevance-minimum redundancy,mRMR)方法进行数据降维并选择最佳特征,随后使用支持向量机算法分别构建瘤内(Intratumoral)、瘤周5 mm(Peri_5mm)、瘤周10 mm(Peri_10mm)模型,并融合瘤内及最佳瘤周参数构建瘤内瘤周融合(IntraPeri)模型。基于受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under the curve,AUC)和决策曲线分析(decision curve analysis,DCA)评估影像组学模型预测pHCC效能及临床效益。结果Intratumoral、Peri_5mm、Peri_10mm、IntraPeri模型分别纳入10、17、11、12个组学特征。Intratumoral模型预测pHCC训练集和测试集的AUC分别为0.92、0.93;Peri_10mm模型预测pHCC的AUC值在训练集(0.88 vs.0.82)、测试集(0.90 vs.0.85)均高于Peri_5mm模型。IntraPeri模型预测pHCC效能最佳,在训练集和测试集AUC值分别为0.95、0.95。DCA提示Intratumoral模型及Peri_10mm模型均具有良好的临床收益,其中IntraPeri模型最佳。结论基于CEMRI的瘤内瘤周影像组学模型可准确预测HCC分化程度,并且具有较Objective:To develop and validate intratumoral and multiregion peritumoral radiomics models based on contrast-enhanced magnetic resonance imaging(CEMRI) for predicting pathological differentiation in hepatocellular carcinoma(HCC) patients.Materials and Methods:A total of 213 HCC patients diagnosed between January 2020 and July 2023 at the Third Affiliated Hospital of Soochow University was included in the retrospective study,comprising 62 poorly differentiated HCC(pHCC) and 161 non-poorly differentiated HCCs(npHCC).The HCCs were randomly divided into training(149 patients,156 HCCs) and validation(64 patients,67HCCs) cohorts at a 7∶3 ratio.The ITK-SNAP software delineated the region of interest(ROI) on arterial,portal vein,and delayed phase images,while PyRadiomics software extracted 3045 radiomic features.Feature selection was carried out using Spearman rank correlation,least absolute shrinkage and selection operator(LASSO),and maximum relevance-minimum redundancy(mRMR)approaches,followed by support vector machine algorithm to build Intratumoral,5 mm peritumoral(Peri_5mm),10 mm peritumoral(Peri_10mm),and Intratumoral + 10 mm peritumoral(IntraPeri) models.The predictive performance of these models was assessed using the area under the curve(AUC) of receiver operating characteristic and decision curve analysis(DCA).Results:The Intratumoral,Peri_5mm,Peri_10mm,and IntraPeri models consisted of 10,17,11,and 12 features,respectively.In the Intratumoral model,the AUC values for predicting pHCC in the training and validation cohorts were 0.92 and 0.93,respectively.The Peri_10mm model exhibited higher AUCs compared to the Peri_5mm model:0.88 versus 0.82 in the training cohort and 0.90 versus 0.85 in the validation cohort.The IntraPeri model demonstrated superior performance with AUC values of 0.95 and 0.95 in the training and validation cohorts,respectively.DCA suggested that the Intratumoral,Peri_5mm,and Peri_10mm models provided notable clinical benefits,with the IntraPeri model being the most optimal.Conclusions:The

关 键 词：肝细胞癌 分化程度 磁共振成像 瘤内 瘤周 影像组学 

分 类 号：R445.2[医药卫生—影像医学与核医学] R735.7[医药卫生—诊断学]