Objective: To investigate the value of CT radiomics model for predicting pathologic types of adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) among lung adenocarinoma. Methods: Data of 542 patients with pathologically confirmed lung adenocarcinoma and clear subtypes were retrospective analyzed. AIS and MIA were classified as group 1 while IAC as group 2. The gender and age were compared between 2 groups. Feature extraction software was used to extract three-dimensional texture feature parameters of each lesion, and the imaging omics features obviously different between 2 groups were retained, then the optimal imaging omics features were selected to build a predictive model. All the data were divided into training set and validation set in a ratio of 2: 1. Six machine learning algorithms were used to classify the five-fold cross-training sets to select the best classifier. Then, the five-fold cross-training data set, training set and validation set were analyzed with the prediction model to obtain the ROC curves of the model in predicting pathological subtypes of lung adenocarcinoma as well as the relative AUC, accuracy, specificity and sensitivity. Results: There were 235 patients in group 1 and 307 in group 2. No statistical difference of gender nor age was found between 2 groups (χ2=0.56, t=-0.19, P=0.63, 0.98). A total of 1 766 three-dimensional texture feature parameters were extracted from the lesions, including 988 imaging omics features significantly different between 2 groups. Finally, 10 optimal imaging omics features were retained to construct the prediction model. Perceptron classifier was the best classifier. AUC of the predictive model in predicting pathological subtypes of validation set was 0.95, and the relative accuracy, specificity and sensitivity was 0.88, 0.87 and 0.84, respectively. Conclusion: CT radiomics medel could effectively predict pathological subtypes of AIS, MIA and IAC among lung adenocarcinoma.

OBJECTIVETo investigate the effects of curcumin on the expression of syndecan-4 protein and p44/42 mitogen- activated protein kinase(MAPK) phosphorylation in rat vascular smooth muscle cells (VSMCs) induced by tumor necrosis factor-α (TNF-α) in vitro.

METHODSRat VSMCs cultured in vitro were stimulated for 24 h by 20 ng/ml TNF-α, 20 µmol/L curcumin, or 20 ng/ml TNF-α plus 20 µmol/lL curcumin. /assay was adopted to evaluate the proliferation of the VSMCs, and the expression of syndecan-4 protein and phosphorylated p44/42 MAPK were determined by Western blotting.

RESULTSCompared with the normal control cells, VSMCs exposed to TNF-α showed significantly enhanced proliferation (P/0.01). Curcumin treatment did not obviously affect the growth of otherwise untreated VSMCs(P>0.05), but could significantly suppress TNF-α-induced proliferation of VSMCs (P/0.01). TNF-α treatment also significantly increased the expression of syndecan-4 protein and phosphorylated p44/42 MAPK (P<0.01), which was markedly lowered by treatment with curcumin (P/0.01). Curcumin alone did not produce any obvious effects on the expression of syndecan-4 protein or phosphorylated p44/42 MAPK (P>0.05).

CONCLUSIONCurcumin can suppress the proliferation of rat VSMCs and lower the expression of syndecan-4 protein and phosphorylated p44/42 MAPK in TNF-α-induced VSMCs.

Animals ; Cells, Cultured ; Curcumin ; pharmacology ; Mitogen-Activated Protein Kinase 3 ; metabolism ; Muscle, Smooth, Vascular ; drug effects ; metabolism ; Phosphorylation ; drug effects ; Rats ; Rats, Sprague-Dawley ; Syndecan-4 ; metabolism ; Tumor Necrosis Factor-alpha ; pharmacology

Objective To investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model.Methods Data from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospective analyzed.The enrolled cases were divided into poorly differentiation group and moderate-to-high differentiation group based on the grading criteria.CT image features were extracted,and seven machine learning algorithms were used to construct prediction models to obtain the AUC,accuracy,specificity,and sensitivity.Results The poorly differentiation group consisted of 175 cases,while the moderate-to-high differentiation group had 332 cases.The XGBoost model demonstrated the best performance,with the AUC,accuracy,specificity,and sensitivity of this model on the validation set being 0.878,0.829,0.667,and 0.727,respectively.Conclusion CT radiomics model can effectively predict the differentiation level of poorly differentiation and moderate-to-high differentiation in lung adenocarcinoma.