Background: The liver iron concentration (LICF) provided by FerriScan is already certified. But there are some restrictive factors. Therefore, we explored the relationship curves of LICF and MRI liver T2 *, and constructed the equations for both. Methods: Liver MRI T2* values of 273 thalassemia patients were measured by CMRtools/Thalassemia Tools (CMRtools) and divided into test and verification groups. The T2* values of the test group and LICF were used to build the equation, through which the T2* values of the validation group were converted to the liver iron concentration(LICe). The relationship between LICe and LICF was explored. According to the clinical liver iron concentration grading, LICF and LICe were grouped to explore the relationship between the them in the validation group. Results: The equation built by the test group was LICF=37.393T2*^(-1.22)(R2=0.971,P<0.05). There was no statistical difference between LICe and LICF in the validation group(P>0.05); There was significant consistency(W=0.991, P<0.05)and significant correlation(rs=0.983,P<0.05) between them .There was no statistical difference in the clinical grading between LICe and LICF in the validation group (P>0.05). There was significant consistency between the clinical grading results(K=0.943,P<0.05. Conclusion: Through the equation LICF=37.393T2*^(-1.22), after measuring the liver T2* value, the liver iron concentration (LIC) equivalent to LICF can be accurately calculated.
ObjectiveTo investigate the application value of 3T MRI qDixon-WIP technique in the quantitative measurement of pancreatic fat content in patients with type 2 diabetes mellitus (T2DM).MethodsThe 3T MRI qDixon-WIP sequence was used to scan the livers and the pancreas of 47 T2DM patients (experimental group) and 48 healthy volunteers (control group). Pancreatic fat fraction (PFF), hepatic fat fraction (HFF), Body mass index (BMI) ratio of pancreatic volume to body surface area (PVI) were measured. Total cholesterol (TC), subcutaneous fat area (SA), triglyceride (TG), abdominal visceral fat area (VA), high density lipoprotein (HDL-c), fasting blood glucose (FPC) and low-density lipoprotein (LDL-c) were collected. The relationship between the experimental group and the control group and between PFF and other indicators was compared. The differences of PFF between the control group and different disease course subgroups were also explored.ResultsThere was no significant difference in BMI between the experimental group and the control group (P=0.231). PVI, SA, VA, PFF and HFF had statistical differences (P<0.05). In the experimental group, PFF was highly positively correlated with HFF (r=0.964, P<0.001), it was moderately positively correlated with TG and abdominal fat area (r=0.676, 0.591, P<0.001), and it was weakly positively correlated with subcutaneous fat area (r=0.321, P=0.033). And it had no correlation with FPC, PVI, HDL-c, TC and LDL-c (P>0.05). There were statistical differences in PFF between the control group and the patients with different course of T2DM (P<0.05). There was no significant difference in PFF between T2DM patients with a disease course ≤1 year and those with a disease course <5 years (P>0.05). There were significant differences in PFF between the groups with a disease course of 1-5 years and those with a disease course of more than 5 years (P<0.001).ConclusionPVI of T2DM patients is lower than normal, but SA, VA, PFF, HFF are higher than normal. The degree of pancreatic fat accumulation in T2DM patients with long disease course was higher than that in patients with short disease course. The qDixon-WIP sequence can provide an important reference for clinical quantitative evaluation of fat content in T2DM patients.
Objective: To explore the relationship between the liver T2* values of thalassemia patients measured by Circle Cardiovascular Imaging CVI42 (CVI42), CMRtools / Thalassemia Tools (CMRtools) and Excel spreadsheet (Excel), and the three software's accuracy in clinical grading of liver iron concentration (LIC). Methods: The liver T2* images thalassemia patients were imported into CVI42 and CMRtools to measure the T2* value. And the signal intensity (SI) of the T2* image, measured in the MR scanning equipment, was input into Excel to calculate T2* value. The relationship of the T2* values measured by the above three software were compared. And LIC clinical grading was performed on the three measured T2* value results, and the LIC (LICF) provided by FerriScan was used as the reference standard to compare the accuracy of the three grading results. Results: There was no statistical difference between the T2* values measured by CVI42, CMRtools and Excel (P>0.05), but there was a high degree of consistency between them (P<0.05), and there was a high linear positive correlation between the them (P<0.05). There was no statistical difference between the LIC grading results of CVI42 and CMRtools and the LICF grading results (P>0.05). There is a significant difference between the LIC grading results of Excel and the LICF grading results (P<0.05). Conclusion: The liver T2* values measured by CVI42, CMRtools and Excel are equivalent. However, CVI42 and CMRtools have better diagnostic accuracy for LIC clinical grade, while Excel has worse diagnostic accuracy. Keywords: THALASSEMIA; lIVER T2 VALUE;
ObjectiveTo investigate the feasibility and accuracy of quantifying liver iron concentration (LIC) in patients with thalassemia (TM) using 1.5T and 3T T2* MRI.Methods1.5T MRI T2* values were measured in 391 TM patients from three medical centers: the T2* values of the test group were combined with the LIC (LICF) provided by FerriScan to construct the curve equation. In addition, the liver 3T MRI liver T2* data of 55 TM patients were measured as the 3T group: the curve equation of 3T T2* value and LICF was constructed.ResultsBased on the test group LICF (0.6–43 mg/g dw) and the corresponding 1.5T T2* value, the equation was LICF = 37.393T2*∧(−1.22) (R2 = 0.971; P < 0.001). There was no significant difference between LICe − 1.5T and LICF in each validation group (Z = −1.269, −0.977, −1.197; P = 0.204, 0.328, 0.231). There was significant consistency (Kendall's W = 0.991, 0.985, 0.980; all P < 0.001) and high correlation (rs = 0.983, 0.971, 0.960; all P < 0.001) between the two methods. There was no significant difference between the clinical grading results of LICe − 1.5T and LICF in each validation group (χ2 = 3.0, 4.0, 2.0; P = 0.083, 0.135, 0.157), and there was significant consistency between the clinical grading results (Kappa's K = 0.943, 0.891, 0.953; P < 0.001). There was no statistical correlation between the LICF (≥14 mg/g dw) and the 3T T2* value of severe iron overload (P = 0.085). The LICF (2–14 mg/g dw) in mild and moderate iron overload was significantly correlated with the corresponding T2* value (rs = −0.940; P < 0.001). The curve equation constructed from LICF and corresponding 3T T2* values in this range is LICF = 18.463T2*∧(−1.142) (R2 = 0.889; P < 0.001). There was no significant difference between LICF and LICe − 3T in the mild to moderate range (Z = −0.523; P = 0.601), and there was a significant correlation (rs = 0.940; P < 0.001) and significant consistency (Kendall's W = 0.970; P = 0.008) between them. LICe − 3T had high diagnostic efficiency in the diagnosis of severe, moderate, and mild liver iron overload (specificity = 1.000, 0.909; sensitivity = 0.972, 1.000).ConclusionThe liver iron concentration can be accurately quantified based on the 1.5T T2* value of the liver and the specific LIC-T2* curve equation. 3T T2* technology can accurately quantify mild-to-moderate LIC, but it is not recommended to use 3T T2* technology to quantify higher iron concentrations.
Background: So far, there is no non-invasive method that can popularize the genetic testing of thalassemia (TM) patients on a large scale. The purpose of the study was to investigate the value of predicting the α- and β- genotypes of TM patients based on a liver MRI radiomics model. Methods: Radiomics features of liver MRI image data and clinical data of 175 TM patients were extracted using Analysis Kinetics (AK) software. The radiomics model with optimal predictive performance was combined with the clinical model to construct a joint model. The predictive performance of the model was evaluated in terms of AUC, accuracy, sensitivity, and specificity. Results: The T2 model showed the best predictive performance: the AUC, accuracy, sensitivity, and specificity of the validation group were 0.88, 0.865, 0.875, and 0.833, respectively. The joint model constructed from T2 image features and clinical features showed higher predictive performance: the AUC, accuracy, sensitivity, and specificity of the validation group were 0.91, 0.846, 0.9, and 0.667, respectively. Conclusion: The liver MRI radiomics model is feasible and reliable for predicting α- and β-genotypes in TM patients.
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