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作 者:聂佩[1] 郝大鹏[1] 王宁[3] 杨光杰[2] 颜蕾 苗文杰 段绍峰 左盼莉 徐文坚[1] NIE Pei;HAO Da-peng;WANG Ning(Department of Radiology,the Affiliated Hospital of Qingdao University,Qingdao 266005,China)
机构地区:[1]青岛大学附属医院放射科,青岛266005 [2]青岛大学附属医院PET-CT中心,青岛266005 [3]山东省立医院放射科,济南255021 [4]GE医疗,上海200000 [5]慧影医疗科技有限公司,北京100089
出 处:《放射学实践》2021年第1期27-32,共6页Radiologic Practice
摘 要:目的:建立术前鉴别中轴骨脊索瘤与骨巨细胞瘤的影像组学模型,并验证其诊断效能。方法:回顾性纳入中轴骨脊索瘤59例、骨巨细胞瘤33例共92例患者,64例为训练集,28例为验证集。基于CT图像进行影像组学特征提取,采用LASSO模型进行特征选择,构建影像组学模型,并计算影像组学得分(Rad-score)。通过Logistic多元回归分析,以独立临床预测因素联合Rad-score构建综合模型,通过校正、ROC曲线、决策曲线评估模型效能。结果:从CT图像中共提取1409个组学特征,降维后获得7个最有鉴别价值的特征构建影像组学模型。该标签在训练集(AUC为0.890,95%CI:0.800~0.980)和验证集(AUC为0.860,95%CI:0.700~1.000)中均有较高的诊断效能。年龄和位于中轴骨两端是独立的临床预测因素。联合独立临床预测因素和Rad-score构建的综合模型,在训练集(AUC为0.970,95%CI:0.930~1.000)和验证集(AUC为0.920,95%CI:0.810~1.000)中均具有较高的诊断效能;训练集中,综合模型的AUC优于组学模型(Z=2.092,P=0.036)。决策曲线分析结果表明,综合模型较组学模型具有更高的临床净获益。结论:基于CT影像组学联合临床特征建立的综合模型术前鉴别中轴骨脊索瘤和骨巨细胞瘤具有较高的诊断效能,有助于临床决策。Objective:The purpose of this study was to develop and validate a radiomics nomogram for preoperative differentiation of chordoma and giant cell tumor(GCT)in axial skeleton.Methods:A total of 92 patients with chordoma(n=59)and GCT(n=33)in axial skeleton were retrospectively enrolled and divided into a training set(n=64)and a validation set(n=28).Radiomics features were extracted from CT images.A radiomics signature was constructed with the least absolute shrinkage and selection operator algorithm and a radiomics score(Rad-score)was calculated.Combined with the Rad-score and independent clinical risk factors,a radiomics nomogram was constructed by multivariate logistic regression analysis.Nomogram performance was assessed with respect to calibration,ROC curves and decision curve analysis.Results:14093D features were extracted and reduced to 7 features as the most important discriminators to build the radiomics signature.The radiomics signature showed good discrimination in the training set(AUC:0.890;95%CI:0.800~0.980)and the validation set(AUC:0.860;95%CI:0.700~1.000).Age and location in the cephalic and caudal axial skeleton were the independent clinical factors.The radiomics nomogram combined with the Rad-score and clinical factors showed good discrimination capability in the training set(AUC:0.970;95%CI:0.930~1.000)and the validation set(AUC:0.920;95%CI:0.810~1.000),and showed better discrimination capability(Z=2.092,P=0.036)compared with the radiomics signature in the training set.Decision curve analysis demonstrated that the nomogram outperformed the radiomics signature in terms of clinical usefulness.Conclusion:The CT-based radiomics nomogram,a preoperative prediction tool that combined with the Rad-score and clinical factors,shows favorable predictive efficacy for differentiating chordoma from GCT in axial skeleton,which might contribute to clinical decision making.
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