[Objective]To investigate the value of radiomics based on contrast-enhanced computed tomography(CT)image in predicting the traditional Chinese medicine(TCM)differentiation typing of primary non-small cell lung cancer(NSCLC).[Methods]A total of 130 patients diagnosed as NSCLC by pathology from July 2018 to October 2023 in Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were retrospectively analyzed.According to the diagnostic criteria of TCM,all the enrolled patients were divided into deficiency syndrome group(67 cases)and excess syndrome group(63 cases),and then assigned to training cohort(91 cases)and validation cohort(39 cases)in a ratio of 7:3.The largest diameter slice of lesion on cross-sectional images was selected and the regions of interest were contoured at unenhanced,arterial and venous phases respectively,and then the radiomics features were extracted.The linear correlation among features and L1 regularization were used for feature selection,and then logistic regression was used to construct the radiomics model based on radiomics features of each phase.The receiver operating characteristic(ROC)curve was used to evaluate the effectiveness of the model in predicting deficiency and excess syndromes of NSCLC.The Delong test was used for comparison of area under curve(AUC)between the two models.[Results]In the training cohort,a total of 7 radiomics models were constructed,including three single-phase radiomics models,three two-phase combination radiomics models and one three-phase combination radiomics model.The AUC of combination radiomics model was higher than that of the single-phase radiomics model.The AUC of three-phase combination radiomics model was the largest,which was 0.876[95%confidence interval(CI)(0.807～0.945)]and 0.755[95%CI(0.603～0.908)]in the training cohort and validation cohort respectively.[Conclusion]The radiomics model based on contrast-enhanced CT image has high efficacy in predicting the TCM differentiation typing of NSCLC,and the three-phase combination radiomics model demonstrates the best diagnostic efficacy.

[Objective]To investigate the value of radiomics based on contrast-enhanced computed tomography(CT)image in predicting the traditional Chinese medicine(TCM)differentiation typing of primary non-small cell lung cancer(NSCLC).[Methods]A total of 130 patients diagnosed as NSCLC by pathology from July 2018 to October 2023 in Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were retrospectively analyzed.According to the diagnostic criteria of TCM,all the enrolled patients were divided into deficiency syndrome group(67 cases)and excess syndrome group(63 cases),and then assigned to training cohort(91 cases)and validation cohort(39 cases)in a ratio of 7:3.The largest diameter slice of lesion on cross-sectional images was selected and the regions of interest were contoured at unenhanced,arterial and venous phases respectively,and then the radiomics features were extracted.The linear correlation among features and L1 regularization were used for feature selection,and then logistic regression was used to construct the radiomics model based on radiomics features of each phase.The receiver operating characteristic(ROC)curve was used to evaluate the effectiveness of the model in predicting deficiency and excess syndromes of NSCLC.The Delong test was used for comparison of area under curve(AUC)between the two models.[Results]In the training cohort,a total of 7 radiomics models were constructed,including three single-phase radiomics models,three two-phase combination radiomics models and one three-phase combination radiomics model.The AUC of combination radiomics model was higher than that of the single-phase radiomics model.The AUC of three-phase combination radiomics model was the largest,which was 0.876[95%confidence interval(CI)(0.807～0.945)]and 0.755[95%CI(0.603～0.908)]in the training cohort and validation cohort respectively.[Conclusion]The radiomics model based on contrast-enhanced CT image has high efficacy in predicting the TCM differentiation typing of NSCLC,and the three-phase combination radiomics model demonstrates the best diagnostic efficacy.

Objective To explore the value of artificial intelligence(AI)in the diagnosis of pulmonary nodules in terms of consistency and efficiency compared with two radiologists(physician 1 is a chief physician and physician 2 is a deputy chief physician)in the diagnosis of benign and malignant pulmonary nodules using computed tomography(CT).Methods Retrospective analysis of 201 patients with pulmonary nodules confirmed by surgery pathology at Hangzhou Municipal Hospital affiliated to Zhejiang Chinese Medical University from January 2021 to October 2022,including a total of 229 pulmonary nodules,of which 74 were benign and 155 were malignant.The consistency of AI diagnosis with two radiologists was evaluated by weighted Kappa test,and the diagnostic performance of AI with the two radiologists was evaluated by the receiver operating characteristic curve(ROC).Results In the diagnosis of the benign and malignant nature of partial solid nodules,ground-glass nodules,solid nodules,and partial ground-glass and solid plus ground-glass nodules,the consistency between AI and physician 2 was higher than that between AI and physician 1.Additionally,the area under the curve(AUC)of physician 2 was higher than that of AI and physician 1 with statistically significant differences between the AUCs of ground-glass nodules,solid nodules,and partial ground-glass and solid plus ground-glass nodules(P<0.05).In the diagnosis of the benign and malignant nature of partial solid nodules and ground-glass nodules,the AUC of physician 1 was higher than that of AI,but there was no statistically significant difference between the two(P>0.05).In the diagnosis of the benign and malignant nature of solid nodules and partial ground-glass and solid plus ground-glass nodules,the AUC of AI was higher than that of physician 1 with statistically significant differences between the two(P<0.05).In the diagnosis of the benign and malignant nature of ground-glass nodules,solid nodules,and partial ground-glass and solid plus ground-glass nodules,AI's sensitivity(97%,92%,and 94%)was higher than that of physician 1(58%,89%,and 72%)and physician 2(83%,84%,and 85%).Conclusion AI has a certain diagnostic efficacy in the diagnosis of pulmonary nodules malignancy.The overall diagnostic efficacy of the AI system used in this study is between that of physician 1 and physician 2,but its sensitivity is higher than that of the latter two.

Objective To evaluate the diagnostic value of the spectral CT imaging on the complications after breast augmentations with silicone implants.Methods A lot of 22 cases with breast implants were scanned by the Gemstone spectral imaging (GSI) CT.The original data were loaded in processed workstation, analyzed by GSI analysis software, and then stored the series we needed.The features of the complications after breast augmentations with silicone implants were reviewed retrospectively on the spectral CT images.Results A lot of 44 breast implants scanned in our study and there were 23 breast implants with complications, including 7 breast implants with capsular rupture (6 breast implants with endometrial rupture detected by spectral CT, including 4 breast implants with endometrial rupture, 3 breast implants with outer membrane rupture and leakage), 13 breast implants with capsular contracture (classification according to Baker's classification, 6 in grade Ⅰand 7 in grade Ⅱ), 8 breast implants were moved and 2 breasts had foci of calcification.Conclusions The spectral CT imaging play important role in detecting the complications after breast augmentations with silicone implants by GSI analysis software.