Purpose To investigate the predictive value of nomogram based on preoperative CT imaging for predicting programmed cell death receptor ligand 1(PD-L1)expression in patient with lung adenocarcinoma.Materials and Methods A total of 158 patients with lung adenocarcinoma were enrolled in Shenzhen people's Hospital from January 2021 to July 2022,of which 82 were negative for PD-L1 and 76 were positive for PD-L1.They were randomly divided into training set(n=119)and verification set(n=39)according to the proportion of 7:3.The significant characteristics between PD-L1 negative and PD-L1 positive were screened by univariate and multivariate Logistic regression to construct a clinical model.Radiomics features were extracted from preoperative CT images,and then features were screened and modeled.Finally,the combined model was established by clinical factors and radiomics features,which was visualized by nomogram.The diagnostic performance of the model was evaluated using receiver operating characteristic curves and area under the curve(AUC).Results The area under the curve(AUC)of the clinical model composed of carcinoembryonic antigen and vascular convergence sign was 0.774(95%CI 0.687-0.860)and 0.808(95%CI 0.670-0.947)in the training set and validation set,respectively.Through feature screening,the radiomics model was composed of 17 radiomics features,and the AUC of the training and validation sets was 0.837(95%CI 0.764-0.910)and 0.778(95%CI 0.633-0.923).The training set and validation set of the combined model composed of carcinoembryonic antigen,vascular convergence sign and radiomics score were AUC 0.892(95%CI 0.832-0.952)and 0.853(95%CI 0.737-0.968).In the training set,the AUC of the combined model was higher than that of the other two models(Z=-2.640,-2.855,P＜0.05).Conclusion Based on preoperative CT radiomics nomogram,it had high predictive efficacy on the expression of PD-L1 in lung adenocarcinoma and could provide decision-making support for the selection of clinical treatment regimens for lung adenocarcinoma patients.

Purpose To investigate the predictive value of nomogram based on preoperative CT imaging for predicting programmed cell death receptor ligand 1(PD-L1)expression in patient with lung adenocarcinoma.Materials and Methods A total of 158 patients with lung adenocarcinoma were enrolled in Shenzhen people's Hospital from January 2021 to July 2022,of which 82 were negative for PD-L1 and 76 were positive for PD-L1.They were randomly divided into training set(n=119)and verification set(n=39)according to the proportion of 7:3.The significant characteristics between PD-L1 negative and PD-L1 positive were screened by univariate and multivariate Logistic regression to construct a clinical model.Radiomics features were extracted from preoperative CT images,and then features were screened and modeled.Finally,the combined model was established by clinical factors and radiomics features,which was visualized by nomogram.The diagnostic performance of the model was evaluated using receiver operating characteristic curves and area under the curve(AUC).Results The area under the curve(AUC)of the clinical model composed of carcinoembryonic antigen and vascular convergence sign was 0.774(95%CI 0.687-0.860)and 0.808(95%CI 0.670-0.947)in the training set and validation set,respectively.Through feature screening,the radiomics model was composed of 17 radiomics features,and the AUC of the training and validation sets was 0.837(95%CI 0.764-0.910)and 0.778(95%CI 0.633-0.923).The training set and validation set of the combined model composed of carcinoembryonic antigen,vascular convergence sign and radiomics score were AUC 0.892(95%CI 0.832-0.952)and 0.853(95%CI 0.737-0.968).In the training set,the AUC of the combined model was higher than that of the other two models(Z=-2.640,-2.855,P＜0.05).Conclusion Based on preoperative CT radiomics nomogram,it had high predictive efficacy on the expression of PD-L1 in lung adenocarcinoma and could provide decision-making support for the selection of clinical treatment regimens for lung adenocarcinoma patients.

Objective To explore the value of nomogram model based on clinical data and MRI findings for predicting simultaneous liver metastasis(SLM)of rectal cancer.Methods Clinical and MRI data of 356 patients with rectal cancer were randomly divided into training set(n=249,45 cases of SLM)and validation set(n=107,27 cases of SLM)at a ratio of 7∶3.Logistic regression analysis were used to screen the independent factors for predicting SLM of rectal cancer.The nomogram model was then constructed,and the efficacy of this model was evaluated.Results Tumor N-stage,serum carcinoembryonic antigen,carbohydrate antigen 19-9 and involvement of mesorectal fascia(MRF)or not were all independent factors for predicting SLM of rectal cancer.The area under the curve(AUC)of this nomogram model for predicting rectal cancer SLM in training set and validation set was 0.834(95％CI[0.776,0.893])and 0.769(95％CI[0.662,0.877]),respectively.The calibration curve showed good consistency between the predicted values and the measured values,and the decision curve analysis showed that the nomogram model had good clinical practicality.Conclusion The nomogram model based on clinical data and MRI findings could be used to predict SLM of rectal cancer.