目的探讨能谱电子计算机断层扫描(CT)成像定量分析对肺结节、肿块诊断的应用价值。方法选取129例肺结节/肿块的患者,进行宝石能谱成像模式三期增强扫描,利用其后处理功能,分别测量动脉期(30 s)、静脉期(60 s)及延迟期(90 s)病灶的标准...目的探讨能谱电子计算机断层扫描(CT)成像定量分析对肺结节、肿块诊断的应用价值。方法选取129例肺结节/肿块的患者,进行宝石能谱成像模式三期增强扫描,利用其后处理功能,分别测量动脉期(30 s)、静脉期(60 s)及延迟期(90 s)病灶的标准化碘浓度(NIC)、(40 ke V)CT值以及能谱曲线斜率,比较各参数间的差异并进行统计学分析。结果 125例患者经手术或纤维支气管镜病理证实,4例炎性病变患者由随访证实;共分为3组,肺癌99例(肺癌组),炎性病变19例(炎性组),肺结核11例(结核组)。动脉期、静脉期及延迟期3组病变NIC值、(40 ke V)CT值以及能谱曲线斜率(40~80 ke V)基本为炎性组最高,均为结核组最低。结核组与其他两组比较,病灶在三期扫描中NIC值、(40 ke V)CT值及能谱曲线斜率差异均有统计学意义(P<0.05);炎性组与肺癌组比较,仅在延迟期NIC值及(40 ke V)CT值差异有统计学意义(P<0.05)。结论能谱CT成像定量分析对肺结节、肿块的鉴别诊断有较大应用价值。展开更多
Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important phy...Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer.In this study,we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.Methods:A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography(CT)examinations between June 2013 and June 2018.We assigned 612 patients to a training cohort and 263 patients to a validation cohort.Radiomics features were extracted from the CT images of each patient.Least absolute shrinkage and selection operator(LASSO)was used for radiomics feature selection and radiomics score calculation.Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram.Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model.The performance of the radiomics nomogram was evaluated by the area under the curve(AUC),calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.Results:A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort.The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’age.Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort(AUC,0.836;95%confidence interval[CI]:0.793-0.879)and validation cohort(AUC,0.809;95%CI:0.745-0.872).The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort(P=0.765)and validation cohort(P=0.064).Good alignment with the calibration curve indicated the good performance of the nomogram展开更多
文摘目的探讨能谱电子计算机断层扫描(CT)成像定量分析对肺结节、肿块诊断的应用价值。方法选取129例肺结节/肿块的患者,进行宝石能谱成像模式三期增强扫描,利用其后处理功能,分别测量动脉期(30 s)、静脉期(60 s)及延迟期(90 s)病灶的标准化碘浓度(NIC)、(40 ke V)CT值以及能谱曲线斜率,比较各参数间的差异并进行统计学分析。结果 125例患者经手术或纤维支气管镜病理证实,4例炎性病变患者由随访证实;共分为3组,肺癌99例(肺癌组),炎性病变19例(炎性组),肺结核11例(结核组)。动脉期、静脉期及延迟期3组病变NIC值、(40 ke V)CT值以及能谱曲线斜率(40~80 ke V)基本为炎性组最高,均为结核组最低。结核组与其他两组比较,病灶在三期扫描中NIC值、(40 ke V)CT值及能谱曲线斜率差异均有统计学意义(P<0.05);炎性组与肺癌组比较,仅在延迟期NIC值及(40 ke V)CT值差异有统计学意义(P<0.05)。结论能谱CT成像定量分析对肺结节、肿块的鉴别诊断有较大应用价值。
基金Key R&D project of Shandong Province,Grant/Award Number:2018GSF118152
文摘Background:Lung cancer is the most commonly diagnosed cancer worldwide.Its survival rate can be significantly improved by early screening.Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer.In this study,we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.Methods:A retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography(CT)examinations between June 2013 and June 2018.We assigned 612 patients to a training cohort and 263 patients to a validation cohort.Radiomics features were extracted from the CT images of each patient.Least absolute shrinkage and selection operator(LASSO)was used for radiomics feature selection and radiomics score calculation.Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram.Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model.The performance of the radiomics nomogram was evaluated by the area under the curve(AUC),calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.Results:A radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort.The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients’age.Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort(AUC,0.836;95%confidence interval[CI]:0.793-0.879)and validation cohort(AUC,0.809;95%CI:0.745-0.872).The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort(P=0.765)and validation cohort(P=0.064).Good alignment with the calibration curve indicated the good performance of the nomogram
