Original Article
Intravoxel incoherent motion derived liver perfusion/diffusion readouts can be reliable biomarker for the detection of viral hepatitis B induced liver fibrosis
Abstract
Background: Recent two studies reported that intravoxel incoherent motion (IVIM) analysis can separate healthy livers and viral hepatitis B (VHB) induced liver fibrosis. However, in these two studies the starting b-value for bi-exponential decay analysis was b =10 and b =15 s/mm2 respectively. The current study has two primary aims. The first is to further confirm the diagnostic value of IVIM in detecting liver fibrosis. The second is to test whether by sampling very low b-value densely, then b =0 s/mm2 image could be included to improve IVIM’s diagnostic performance.
Methods: This was a prospective study with data acquired at the Third Xiangya Hospital of Central South University, Changsha, China. Healthy volunteers and patients suspected of VHB induced liver fibrosis with liver biopsy performed, as well as hepatocellular carcinoma patients scheduled for surgery, were recruited. All the hepatocellular carcinoma patients had liver fibrosis. After exclusions based on predefined criteria for image data quality, for IVIM analysis this study included 20 healthy volunteers; 4 chronic VHB patients with biopsy showing no liver fibrosis; 11 stage-1 liver fibrosis patients, 10 stage-2 liver fibrosis patients, 2 stage-3 liver fibrosis patients, and 5 stage-4 liver fibrosis patients. In the liver fibrosis patients, 1, 19, and 8 cases had inflammation grade-0, grade-1, and grade-2 respectively. The reference IVIM bi-exponential decay curve fitting analysis was segmented fitting performed with b =2 s/mm2 image as the starting point and a threshold-b of 60 s/mm2. This reference fitting method was compared with threshold-b of 40 s/mm2, full fitting, fitting starting from b =0, b =5, and b =10 s/mm2 respectively. The potential correlation between IVIM readouts and liver function was assessed for the liver fibrosis patients.
Results: Based on the smaller coefficient of variation (CoV) for the volunteer group and the smaller patient/volunteer ratios [= (mean measurement for patient groups)/(mean measurement for healthy volunteers)], the comparison of fitting methods favored the reference approach starting from b =2 s/mm2 with a threshold-b of 60 s/mm2. The IVIM measures of four patients without liver fibrosis resembled those of healthy subjects. PF offered the best diagnostic value for separating healthy livers and fibrotic livers, and a threshold of PF =0.1406 separated all fibrotic livers and healthy livers with an exception of one hepatocellular carcinoma patient (fibrosis grade-2/inflammation grade-2). The correlation between fibrosis grading and inflammation grading was weakly positive; while compared with fibrotic livers with inflammation grade-1, fibrotic livers with inflammation grade-2 showed a trend of higher Dfast. A weak correlation is shown with lower PF and lower Dfast associated with lower total protein, lower albumin; higher alanine transaminase, higher aspartate transaminase; higher total bilirubin, and higher direct bilirubin.
Conclusions: Segmented-fitting with threshold-b =60 s/mm2 and starting from non-zero very low b-value outperforms other methods. IVIM has high sensitivity in detecting liver fibrosis, and PF and Dfast have potential correlation with serum liver function biomarkers. IVIM measures and liver fibrosis grading are not in a linear relationship.
Methods: This was a prospective study with data acquired at the Third Xiangya Hospital of Central South University, Changsha, China. Healthy volunteers and patients suspected of VHB induced liver fibrosis with liver biopsy performed, as well as hepatocellular carcinoma patients scheduled for surgery, were recruited. All the hepatocellular carcinoma patients had liver fibrosis. After exclusions based on predefined criteria for image data quality, for IVIM analysis this study included 20 healthy volunteers; 4 chronic VHB patients with biopsy showing no liver fibrosis; 11 stage-1 liver fibrosis patients, 10 stage-2 liver fibrosis patients, 2 stage-3 liver fibrosis patients, and 5 stage-4 liver fibrosis patients. In the liver fibrosis patients, 1, 19, and 8 cases had inflammation grade-0, grade-1, and grade-2 respectively. The reference IVIM bi-exponential decay curve fitting analysis was segmented fitting performed with b =2 s/mm2 image as the starting point and a threshold-b of 60 s/mm2. This reference fitting method was compared with threshold-b of 40 s/mm2, full fitting, fitting starting from b =0, b =5, and b =10 s/mm2 respectively. The potential correlation between IVIM readouts and liver function was assessed for the liver fibrosis patients.
Results: Based on the smaller coefficient of variation (CoV) for the volunteer group and the smaller patient/volunteer ratios [= (mean measurement for patient groups)/(mean measurement for healthy volunteers)], the comparison of fitting methods favored the reference approach starting from b =2 s/mm2 with a threshold-b of 60 s/mm2. The IVIM measures of four patients without liver fibrosis resembled those of healthy subjects. PF offered the best diagnostic value for separating healthy livers and fibrotic livers, and a threshold of PF =0.1406 separated all fibrotic livers and healthy livers with an exception of one hepatocellular carcinoma patient (fibrosis grade-2/inflammation grade-2). The correlation between fibrosis grading and inflammation grading was weakly positive; while compared with fibrotic livers with inflammation grade-1, fibrotic livers with inflammation grade-2 showed a trend of higher Dfast. A weak correlation is shown with lower PF and lower Dfast associated with lower total protein, lower albumin; higher alanine transaminase, higher aspartate transaminase; higher total bilirubin, and higher direct bilirubin.
Conclusions: Segmented-fitting with threshold-b =60 s/mm2 and starting from non-zero very low b-value outperforms other methods. IVIM has high sensitivity in detecting liver fibrosis, and PF and Dfast have potential correlation with serum liver function biomarkers. IVIM measures and liver fibrosis grading are not in a linear relationship.
