文章摘要
徐青青,单文莉,朱 艳,黄陈翠,包丝雨,郭莉莉.基于CT影像组学对孤立性肺结节性质分类的预测效能[J].南京医科大学学报,2021,(4):617~623
基于CT影像组学对孤立性肺结节性质分类的预测效能
Predictive performance of the classification of solitary pulmonary nodule based on CT radiomics
投稿时间:2020-01-14  
DOI:10.7655/NYDXBNS20210425
中文关键词: 孤立性  肺结节  CT  影像组学  病理  分类
英文关键词: solitary  pulmonary nodule  computed tomography  radiomics  pathology  classification
基金项目:江苏省博士后科研资助计划(2019K278)
作者单位
徐青青 南京医科大学附属淮安第一医院影像科江苏 淮安 223300 
单文莉 南京医科大学附属淮安第一医院影像科江苏 淮安 223300 
朱 艳 南京医科大学附属淮安第一医院影像科江苏 淮安 223300 
黄陈翠 北京深睿博联科技有限责任公司研发中心科研合作部北京 100089 
包丝雨 北京深睿博联科技有限责任公司研发中心科研合作部北京 100089 
郭莉莉 南京医科大学附属淮安第一医院影像科江苏 淮安 223300 
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中文摘要:
      目的:探讨肺结节CT影像组学特征与病理分类的关系,并评估不同分层递进影像组学模型对肺结节病理分类的预测效果。方法:纳入2017年7月—2019年8月189例病理证实且具有完整资料的肺结节患者,包括良性结节71例,恶性非浸润结节51例,恶性浸润性结节67例。分别构建3种CT影像组学模型,模型1对良性及恶性结节(包括恶性非浸润及浸润性结节)分类;模型2对恶性非浸润及浸润性结节分类;模型3对良性、恶性非浸润及浸润性结节分类。对所有勾画感兴趣区(ROI)进行高通量特征采集,采用智能方法进行特征和分类器筛选建立最佳模型。受试者工作特征(ROC)曲线及曲线下面积(AUC)用于评价模型的预测效能,计算灵敏度、特异度、准确率、阳性预测值和阴性预测值。结果:模型1、2及3分别筛选出20、2及20个影像组学特征,绘制ROC曲线,验证组AUC值分别为0.85、0.89及0.84,模型1灵敏度、特异度、准确率、阳性预测值及阴性预测值为79.66%、70.42%、84.59%、81.74%及67.57%;模型2为88.06%、74.51%、82.2%、81.94%及82.61%;模型3为71.34%、85.05%、70.37%%、83.2%及76.3%。结论:基于CT图像影像组学特征模型可以较好地反映良性结节、恶性非浸润结节及浸润性结节的差异,对其进行分类。
英文摘要:
      Objective:To investigate the correlation between CT radiomics features and pathological classification of pulmonary nodules,and to evaluate the predictive performance of three radiomics models on pathological classification of pulmonary nodules. Methods:A total of 189 patients with pathologically?proven pulmonary nodule and complete clinical data and CT images were obtained from July 2017 to August 2019 in our hospital,including 71 benign nodules,51 non?invasive nodules and 67 invasive nodules. Three radiomics models were established. Model 1 was established to distinguish benign and malignant nodules(including non?invasive and invasive nodules);model 2 was established to differentiate non?invasive and invasive nodules;model 3 was established to distinguish benign,non?invasive and invasive nodules. The high throughput features from the region of interests(ROIs)within the radiologist?drawn contour were extracted for classification analysis by use of a radiomics software. The classification model was established by selecting features and classifiers intelligently. The prediction performances were evaluated with ROC analysis and AUC. The sensitivity,specificity,accuracy,positive predictive value(PPV),and negative predictive value(NPV)in classification of three models were calculated. Results:Twenty radiomics features were screened out in model 1. Classification test results of the model 1 showed the AUC of 0.85,accuracy of 79.66%,sensitivity of 70.42%,specificity of 84.59%,PPV of 81.74%,NPV of 67.57%,respectively. Two radiomics features were selected in model 2. Classification test results of the model 2 showed the AUC of 0.89,accuracy of 88.06%,sensitivity of 74.51%,specificity of 82.2%,PPV of 81.94%,NPV of 82.61%,respectively. In model 3,20 radiomics features were selected,and classification test results of the model 3 showed the AUC of 0.84,accuracy of 71.34%,sensitivity of 85.05%,specificity of 70.37%,PPV of 83.2%,NPV of 76.3%,respectively. Conclusion:CT radiomics has high value in the identification of benign,non?invasive and invasive nodules.
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