Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance ...Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.Methods:This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group.The radiomic features of the whole lung volume were extracted.Least absolute shrinkage and selection operator(LASSO)logistic regression was applied for feature selection and radiomic signature construction.A radiomic nomogram was established by combining the radiomic score and clinical factors.Receiver operating characteristic(ROC)curve analysis and decision curve analysis(DCA)were used to evaluate the predictive performance of the radiomic nomogram in the training,internal validation,and independent external validation cohorts.Results:Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model.The area under the curve(AUC)of the radiomic model in the training,internal,and independent external validation cohorts were 0.888[95%confidence interval(CI)0.869–0.906],0.874(95%CI 0.844–0.904),and 0.846(95%CI 0.822–0.870),respectively.All were higher than the clinical model(AUC were 0.732,0.714,and 0.777,respectively,P<0.001).DCA demonstrated that the nomogram constructed by combining radiomic score,age,sex,height,and smoking status was superior to the clinical factor model.Conclusions:The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.展开更多
Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients sta...Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients stagedⅢor stageⅣwere included in this retrospective study who received ^(18)F-FDG PET/CT before treatment.All patients were treated with standardized chemotherapy after PET/CT examination and were divided into training group and validation group in an 8:2 ratio randomly.Radiomics features were extracted.In the training group,the minimum absolute contraction and selection operator(LASSO)algorithm and Cox risk proportional regression model were used to screen radiomics and clinical prognostic factors of progression-free survival(PFS).The radiomic model,clinical model and complex model were established respectively.The corresponding scores were calculated,then verified in the validation group.Results:The LASSO algorithm finally screened four radiomics features.ROC results showed that in the training group,the AUC of PFS predicted by the radiomics model was 0.746,and that in the verification group was 0.622.COX multivariate analysis finally included three clinical features related to PFS in NSCLC patients,namely pathological type,clinical stage and MTV30.The AUC for predicting PFS by clinical model,radiomics model and composite model were 0.746,0.753 and 0.716,respectively.The radiomics model had the highest diagnostic efficacy,and its sensitivity and specificity were 0.663 and 0.833,respectively.Delong test verified that there was no statistical difference in the predictive efficacy between the radiomics model and the composite model(Z=1.777,P=0.076)and the clinical imaging model(Z=0.323,P=0.747).Conclusion:The radiomics model based on PET/CT has a good predictive value for the prognosis of advanced NSCLC treated with chemotherapy,but it needs further validation before it can be widely used in clinical practice.展开更多
背景老年男性恶性肿瘤中,前列腺癌的发病率已位居第2位,其早期筛查有重要临床意义。目的探索前列腺影像报告和数据系统(Prostate Imaging Reporting and Data System,PI-RADS)评分联合^(18)F-PSMA PET/CT评分对前列腺癌的诊断价值。方...背景老年男性恶性肿瘤中,前列腺癌的发病率已位居第2位,其早期筛查有重要临床意义。目的探索前列腺影像报告和数据系统(Prostate Imaging Reporting and Data System,PI-RADS)评分联合^(18)F-PSMA PET/CT评分对前列腺癌的诊断价值。方法回顾性分析2019年1月—2022年11月在解放军总医院行前列腺穿刺活检或手术的124例患者的前列腺磁共振检查(magnetic resonance imaging,MRI)及^(18)F-PSMA PET/CT影像资料,2名影像医师和2名核医学科医师分别对MRI图像和PET/CT图像进行PI-RADS评分和分子成像前列腺特异性膜抗原(molecular imaging prostate specific membrane antigen,miPSMA)评分。以病理结果为金标准,分别以PI-RADS≥3分、miPSMA≥2分以及PI-RADS≥3分联合miPSMA≥2分作为前列腺癌的不同诊断模型,评估各模型的诊断效能。结果124例受试者平均年龄(77.74±10.30)岁,100例被确诊为前列腺癌。以PI-RADS≥3分、miPSMA≥2分以及PI-RADS≥3分联合miPSMA≥2分为标准诊断前列腺癌的敏感度分别为99.0%、96.0%和95.0%;特异度分别为33.3%、83.3%和83.3%;准确度分别为86.3%、93.5%和92.7%;AUC分别为0.662(0.523~0.800)、0.897(0.806~0.987)和0.892(0.801~0.983)。结论前列腺PI-RADS评分≥3分联合miPSMA≥2分有着较高的诊断效能,可作为前列腺癌的精准诊断方案应用于临床。展开更多
基金supported by the National Key Research and Development Program of China(2022YFC2010002,2022YFC2010000 and 2022YFC2010005)the National Natural Science Foundation of China(82171926,81930049 and 82202140)+3 种基金the Medical Imaging Database Construction Program of National Health Commission(YXFSC2022JJSJ002)the Clinical Innovative Project of Shanghai Changzheng Hospital(2020YLCYJ-Y24)the Program of Science and Technology Commission of Shanghai Municipality(21DZ2202600)the Shanghai Sailing Program(20YF1449000).
文摘Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.Methods:This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group.The radiomic features of the whole lung volume were extracted.Least absolute shrinkage and selection operator(LASSO)logistic regression was applied for feature selection and radiomic signature construction.A radiomic nomogram was established by combining the radiomic score and clinical factors.Receiver operating characteristic(ROC)curve analysis and decision curve analysis(DCA)were used to evaluate the predictive performance of the radiomic nomogram in the training,internal validation,and independent external validation cohorts.Results:Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model.The area under the curve(AUC)of the radiomic model in the training,internal,and independent external validation cohorts were 0.888[95%confidence interval(CI)0.869–0.906],0.874(95%CI 0.844–0.904),and 0.846(95%CI 0.822–0.870),respectively.All were higher than the clinical model(AUC were 0.732,0.714,and 0.777,respectively,P<0.001).DCA demonstrated that the nomogram constructed by combining radiomic score,age,sex,height,and smoking status was superior to the clinical factor model.Conclusions:The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.
基金Research and Cultivation Foundation of Hainan Medical College(HYPY2020022)Hainan Natural Science Foundation Youth fund(822QN482)+1 种基金Doctoral Research Fund project of Hainan Cancer Hospital(2022BS04)Key R&D projects in Hainan Province(ZDYF2021SHFZ244)。
文摘Objective:To investigate the prognostic value of radiomics features based on ^(18)F-FDG PET/CT imaging for advanced non-small cell lung cancer(NSCLC)treated with chemotherapy.Methods:A sample of 146 NSCLC patients stagedⅢor stageⅣwere included in this retrospective study who received ^(18)F-FDG PET/CT before treatment.All patients were treated with standardized chemotherapy after PET/CT examination and were divided into training group and validation group in an 8:2 ratio randomly.Radiomics features were extracted.In the training group,the minimum absolute contraction and selection operator(LASSO)algorithm and Cox risk proportional regression model were used to screen radiomics and clinical prognostic factors of progression-free survival(PFS).The radiomic model,clinical model and complex model were established respectively.The corresponding scores were calculated,then verified in the validation group.Results:The LASSO algorithm finally screened four radiomics features.ROC results showed that in the training group,the AUC of PFS predicted by the radiomics model was 0.746,and that in the verification group was 0.622.COX multivariate analysis finally included three clinical features related to PFS in NSCLC patients,namely pathological type,clinical stage and MTV30.The AUC for predicting PFS by clinical model,radiomics model and composite model were 0.746,0.753 and 0.716,respectively.The radiomics model had the highest diagnostic efficacy,and its sensitivity and specificity were 0.663 and 0.833,respectively.Delong test verified that there was no statistical difference in the predictive efficacy between the radiomics model and the composite model(Z=1.777,P=0.076)and the clinical imaging model(Z=0.323,P=0.747).Conclusion:The radiomics model based on PET/CT has a good predictive value for the prognosis of advanced NSCLC treated with chemotherapy,but it needs further validation before it can be widely used in clinical practice.
文摘背景老年男性恶性肿瘤中,前列腺癌的发病率已位居第2位,其早期筛查有重要临床意义。目的探索前列腺影像报告和数据系统(Prostate Imaging Reporting and Data System,PI-RADS)评分联合^(18)F-PSMA PET/CT评分对前列腺癌的诊断价值。方法回顾性分析2019年1月—2022年11月在解放军总医院行前列腺穿刺活检或手术的124例患者的前列腺磁共振检查(magnetic resonance imaging,MRI)及^(18)F-PSMA PET/CT影像资料,2名影像医师和2名核医学科医师分别对MRI图像和PET/CT图像进行PI-RADS评分和分子成像前列腺特异性膜抗原(molecular imaging prostate specific membrane antigen,miPSMA)评分。以病理结果为金标准,分别以PI-RADS≥3分、miPSMA≥2分以及PI-RADS≥3分联合miPSMA≥2分作为前列腺癌的不同诊断模型,评估各模型的诊断效能。结果124例受试者平均年龄(77.74±10.30)岁,100例被确诊为前列腺癌。以PI-RADS≥3分、miPSMA≥2分以及PI-RADS≥3分联合miPSMA≥2分为标准诊断前列腺癌的敏感度分别为99.0%、96.0%和95.0%;特异度分别为33.3%、83.3%和83.3%;准确度分别为86.3%、93.5%和92.7%;AUC分别为0.662(0.523~0.800)、0.897(0.806~0.987)和0.892(0.801~0.983)。结论前列腺PI-RADS评分≥3分联合miPSMA≥2分有着较高的诊断效能,可作为前列腺癌的精准诊断方案应用于临床。
