Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years a...Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.展开更多
Beta Ti−35Nb sandwich-structured composites with various reinforcing layers were designed and produced using additive manufacturing(AM)to achieve a balance between light weight and high strength.The impact of reinforc...Beta Ti−35Nb sandwich-structured composites with various reinforcing layers were designed and produced using additive manufacturing(AM)to achieve a balance between light weight and high strength.The impact of reinforcing layers on the compressive deformation behavior of porous composites was investigated through micro-computed tomography(Micro-CT)and finite element method(FEM)analyses.The results indicate that the addition of reinforcement layers to sandwich structures can significantly enhance the compressive yield strength and energy absorption capacity of porous metal structures;Micro-CT in-situ observation shows that the strain of the porous structure without the reinforcing layer is concentrated in the middle region,while the strain of the porous structure with the reinforcing layer is uniformly distributed;FEM analysis reveals that the reinforcing layers can alter stress distribution and reduce stress concentration,thereby promoting uniform deformation of the porous structure.The addition of reinforcing layer increases the compressive yield strength of sandwich-structured composite materials by 124%under the condition of limited reduction of porosity,and the yield strength increases from 4.6 to 10.3 MPa.展开更多
文摘Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.
基金the Hunan Young Scientific Innovative Talents Program,China(No.2020RC3040)Outstanding Youth Fund of Hunan Natural Science Foundation,China(Nos.2021JJ20011,2021JJ40600,2021JJ40590)the National Natural Science Foundation of China(Nos.52001030,52204371)..
文摘Beta Ti−35Nb sandwich-structured composites with various reinforcing layers were designed and produced using additive manufacturing(AM)to achieve a balance between light weight and high strength.The impact of reinforcing layers on the compressive deformation behavior of porous composites was investigated through micro-computed tomography(Micro-CT)and finite element method(FEM)analyses.The results indicate that the addition of reinforcement layers to sandwich structures can significantly enhance the compressive yield strength and energy absorption capacity of porous metal structures;Micro-CT in-situ observation shows that the strain of the porous structure without the reinforcing layer is concentrated in the middle region,while the strain of the porous structure with the reinforcing layer is uniformly distributed;FEM analysis reveals that the reinforcing layers can alter stress distribution and reduce stress concentration,thereby promoting uniform deformation of the porous structure.The addition of reinforcing layer increases the compressive yield strength of sandwich-structured composite materials by 124%under the condition of limited reduction of porosity,and the yield strength increases from 4.6 to 10.3 MPa.
