BACKGROUND Noninvasive,practical,and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed.AIM To develop a precise noninvasive test to stage liver fibrosis and c...BACKGROUND Noninvasive,practical,and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed.AIM To develop a precise noninvasive test to stage liver fibrosis and cirrhosis.METHODS With liver biopsy as the gold standard,we established a new index,[alkaline phosphatase(U/L)+gamma-glutamyl transpeptidase(U/L)/platelet(109/L)(AGPR)],to predict liver fibrosis and cirrhosis.In addition,we compared the area under the receiver operating characteristic curve(AUROC)of AGPR,gammaglutamyl transpeptidase to platelet ratio,aspartate transaminase to platelet ratio index,and FIB-4 and evaluated the accuracy of these routine laboratory indices in predicting liver fibrosis and cirrhosis.RESULTS Correlation analysis revealed a significant positive correlation between AGPR and liver fibrosis stage(P<0.001).In the training cohort,the AUROC of AGPR was 0.83(95%CI:0.78-0.87)for predicting fibrosis(≥F2),0.84(95%CI:0.79-0.88)for predicting extensive fibrosis(≥F3),and 0.87(95%CI:0.83-0.91)for predicting cirrhosis(F4).In the validation cohort,the AUROCs of AGPR to predict≥F2,≥F3 and F4 were 0.83(95%CI:0.77-0.88),0.83(95%CI:0.77-0.89),and 0.84(95%CI:0.78-0.89),respectively.CONCLUSION The AGPR index should become a new,simple,accurate,and noninvasive marker to predict liver fibrosis and cirrhosis in chronic hepatitis B patients.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine lea...BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer,which can assist with cancer diagnosis,therapeutic decision-making and prognosis improvement.AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival(OS)of HCC patients after radical hepatectomy.METHODS A total of 150 HCC patients were randomly divided into a training cohort(n=107)and a validation cohort(n=43).Radiomics features were extracted from the entire tumour lesion.The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature.Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram,incorporating clinicopathological characteristics and the radiomics signature.The accuracy of the nomogram was assessed with the concordance index,receiver operating characteristic(ROC)curve and calibration curve.The clinical utility was evaluated by decision curve analysis(DCA).Kaplan–Meier methodology was used to compare the survival between the low-and high-risk subgroups.RESULTS In total,seven radiomics features were selected to construct the radiomics signature.According to the results of univariate and multivariate Cox regression analyses,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR)and radiomics signature were included to build the nomogram.The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774,respectively.ROC curve analysis for predicting 1-,3-,and 5-year OS confirmed satisfactory accuracy[training cohort,area under the curve(AUC)=0.850,0.791 and 0.823,respectively;validation cohort,AUC=0.905,0.884 and 0.911,respectively].The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival.DCA curves suggested that the nomogram had more benefit than traditional staging system models.Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival(all P<0.0001).CONCLUSION The nomogram containing the radiomics signature,NLR and AFP is a reliable tool for predicting the OS of HCC patients.展开更多
基金Supported by the National Natural Science Foundation of China,No.81372163the Natural Science Foundation of Guangxi,No.2018GXNSFDA138001+2 种基金the Science and Technology Planning Project of Guilin,No.20190218-1the Opening Project of Key laboratory of High-Incidence-Tumor Prevention&Treatment(Guangxi Medical University),Ministry of Education,No.GKE-KF202101the Program of Guangxi Zhuang Autonomous Region Health and Family Planning Commission,No.Z20210706 and No.Z20190665。
文摘BACKGROUND Noninvasive,practical,and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed.AIM To develop a precise noninvasive test to stage liver fibrosis and cirrhosis.METHODS With liver biopsy as the gold standard,we established a new index,[alkaline phosphatase(U/L)+gamma-glutamyl transpeptidase(U/L)/platelet(109/L)(AGPR)],to predict liver fibrosis and cirrhosis.In addition,we compared the area under the receiver operating characteristic curve(AUROC)of AGPR,gammaglutamyl transpeptidase to platelet ratio,aspartate transaminase to platelet ratio index,and FIB-4 and evaluated the accuracy of these routine laboratory indices in predicting liver fibrosis and cirrhosis.RESULTS Correlation analysis revealed a significant positive correlation between AGPR and liver fibrosis stage(P<0.001).In the training cohort,the AUROC of AGPR was 0.83(95%CI:0.78-0.87)for predicting fibrosis(≥F2),0.84(95%CI:0.79-0.88)for predicting extensive fibrosis(≥F3),and 0.87(95%CI:0.83-0.91)for predicting cirrhosis(F4).In the validation cohort,the AUROCs of AGPR to predict≥F2,≥F3 and F4 were 0.83(95%CI:0.77-0.88),0.83(95%CI:0.77-0.89),and 0.84(95%CI:0.78-0.89),respectively.CONCLUSION The AGPR index should become a new,simple,accurate,and noninvasive marker to predict liver fibrosis and cirrhosis in chronic hepatitis B patients.
基金Supported by the National Natural Science Foundation of China,No.81372163the Science and Technology Planning Project of Guilin,No.20190218-1+2 种基金the Openin Project of Key laboratory of High-Incidence-Tumor Prevention&Treatment(Guangxi Medical University),Ministry of Education,No.GKE-KF202101the Program of Guangxi Zhuang Autonomous Region health and Family Planning Commission,No.Z20210706the Innovation and Entrepreneurship Project of University Students in Guangxi,No.202110601002.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is the most common primary liver malignancy with a rising incidence worldwide.The prognosis of HCC patients after radical resection remains poor.Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer,which can assist with cancer diagnosis,therapeutic decision-making and prognosis improvement.AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival(OS)of HCC patients after radical hepatectomy.METHODS A total of 150 HCC patients were randomly divided into a training cohort(n=107)and a validation cohort(n=43).Radiomics features were extracted from the entire tumour lesion.The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature.Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram,incorporating clinicopathological characteristics and the radiomics signature.The accuracy of the nomogram was assessed with the concordance index,receiver operating characteristic(ROC)curve and calibration curve.The clinical utility was evaluated by decision curve analysis(DCA).Kaplan–Meier methodology was used to compare the survival between the low-and high-risk subgroups.RESULTS In total,seven radiomics features were selected to construct the radiomics signature.According to the results of univariate and multivariate Cox regression analyses,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR)and radiomics signature were included to build the nomogram.The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774,respectively.ROC curve analysis for predicting 1-,3-,and 5-year OS confirmed satisfactory accuracy[training cohort,area under the curve(AUC)=0.850,0.791 and 0.823,respectively;validation cohort,AUC=0.905,0.884 and 0.911,respectively].The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival.DCA curves suggested that the nomogram had more benefit than traditional staging system models.Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival(all P<0.0001).CONCLUSION The nomogram containing the radiomics signature,NLR and AFP is a reliable tool for predicting the OS of HCC patients.
