BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiolo...BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiologies of chronic liver disease.AIM To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.METHODS Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled.After excluding dual infection,alcoholism,autoimmune hepatitis,and others with incomplete data,this retrospective cohort were divided into hepatitis B(HBV,n=1064),hepatitis C(HCV,n=507),and non-HBV,non-HCV(NBNC,n=391)groups.The indexed cases were linked to cancer registration(1987-2020)and national mortality databases.The differences in morbidity and mortality among the groups were analyzed.RESULTS At the enrollment,the HBV group showed more males(77.5%),a higher prevalence of prediagnosed hepatocellular carcinoma(HCC),and a lower prevalence of comorbidities than the other groups(P<0.001).The HCV group was older and had a lower platelet count and higher ARFI score than the other groups(P<0.001).The NBNC group showed a higher body mass index and platelet count,a higher prevalence of pre-diagnosed non-HCC cancers(P<0.001),especially breast cancer,and a lower prevalence of cirrhosis.Male gender,ARFI score,and HBV were independent predictors of HCC.The 5-year risk of HCC was 5.9%and 9.8%for those ARFI-graded with severe fibrosis and cirrhosis.ARFI alone had an area under the receiver operating characteristic curve(AUROC)of 0.742 for prediction of HCC in 5 years.AUROC increased to 0.828 after adding etiology,gender,age,and platelet score.No difference was found in mortality rate among the groups.CONCLUSION The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups.Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.展开更多
BACKGROUND Hepatic steatosis is a major cause of chronic liver disease.Two-dimensional(2D)ultrasound is the most widely used non-invasive tool for screening and monitoring,but associated diagnoses are highly subjectiv...BACKGROUND Hepatic steatosis is a major cause of chronic liver disease.Two-dimensional(2D)ultrasound is the most widely used non-invasive tool for screening and monitoring,but associated diagnoses are highly subjective.AIM To develop a scalable deep learning(DL)algorithm for quantitative scoring of liver steatosis from 2D ultrasound images.METHODS Using multi-view ultrasound data from 3310 patients,19513 studies,and 228075 images from a retrospective cohort of patients received elastography,we trained a DL algorithm to diagnose steatosis stages(healthy,mild,moderate,or severe)from clinical ultrasound diagnoses.Performance was validated on two multiscanner unblinded and blinded(initially to DL developer)histology-proven cohorts(147 and 112 patients)with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses.We also quantified reliability across scanners and viewpoints.Results were evaluated using Bland-Altman and receiver operating characteristic(ROC)analysis.RESULTS The DL algorithm demonstrated repeatable measurements with a moderate number of images(three for each viewpoint)and high agreement across three premium ultrasound scanners.High diagnostic performance was observed across all viewpoints:Areas under the curve of the ROC to classify mild,moderate,and severe steatosis grades were 0.85,0.91,and 0.93,respectively.The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter(CAP)with statistically significant improvements for all levels on the unblinded histology-proven cohort and for“=severe”steatosis on the blinded histology-proven cohort.CONCLUSION The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts.Diagnostic performance was high with comparable or better performance than the CAP.展开更多
基金Supported by the Chang Gung Memorial Hospital and PAII Inc.(a United States subsidiary company of Ping An Insurance Group),No.SMRPG3I0011.
文摘BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiologies of chronic liver disease.AIM To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.METHODS Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled.After excluding dual infection,alcoholism,autoimmune hepatitis,and others with incomplete data,this retrospective cohort were divided into hepatitis B(HBV,n=1064),hepatitis C(HCV,n=507),and non-HBV,non-HCV(NBNC,n=391)groups.The indexed cases were linked to cancer registration(1987-2020)and national mortality databases.The differences in morbidity and mortality among the groups were analyzed.RESULTS At the enrollment,the HBV group showed more males(77.5%),a higher prevalence of prediagnosed hepatocellular carcinoma(HCC),and a lower prevalence of comorbidities than the other groups(P<0.001).The HCV group was older and had a lower platelet count and higher ARFI score than the other groups(P<0.001).The NBNC group showed a higher body mass index and platelet count,a higher prevalence of pre-diagnosed non-HCC cancers(P<0.001),especially breast cancer,and a lower prevalence of cirrhosis.Male gender,ARFI score,and HBV were independent predictors of HCC.The 5-year risk of HCC was 5.9%and 9.8%for those ARFI-graded with severe fibrosis and cirrhosis.ARFI alone had an area under the receiver operating characteristic curve(AUROC)of 0.742 for prediction of HCC in 5 years.AUROC increased to 0.828 after adding etiology,gender,age,and platelet score.No difference was found in mortality rate among the groups.CONCLUSION The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups.Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.
基金Supported by the Maintenance Project of the Center for Artificial Intelligence,No.CLRPG3H0012 and No.SMRPG3I0011.
文摘BACKGROUND Hepatic steatosis is a major cause of chronic liver disease.Two-dimensional(2D)ultrasound is the most widely used non-invasive tool for screening and monitoring,but associated diagnoses are highly subjective.AIM To develop a scalable deep learning(DL)algorithm for quantitative scoring of liver steatosis from 2D ultrasound images.METHODS Using multi-view ultrasound data from 3310 patients,19513 studies,and 228075 images from a retrospective cohort of patients received elastography,we trained a DL algorithm to diagnose steatosis stages(healthy,mild,moderate,or severe)from clinical ultrasound diagnoses.Performance was validated on two multiscanner unblinded and blinded(initially to DL developer)histology-proven cohorts(147 and 112 patients)with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses.We also quantified reliability across scanners and viewpoints.Results were evaluated using Bland-Altman and receiver operating characteristic(ROC)analysis.RESULTS The DL algorithm demonstrated repeatable measurements with a moderate number of images(three for each viewpoint)and high agreement across three premium ultrasound scanners.High diagnostic performance was observed across all viewpoints:Areas under the curve of the ROC to classify mild,moderate,and severe steatosis grades were 0.85,0.91,and 0.93,respectively.The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter(CAP)with statistically significant improvements for all levels on the unblinded histology-proven cohort and for“=severe”steatosis on the blinded histology-proven cohort.CONCLUSION The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts.Diagnostic performance was high with comparable or better performance than the CAP.