Assessing the Accuracy of CMRtools Software for Diagnosing Liver Iron Overload in Thalassemia Patients: Influencing Factors and Optimisation Strategies

Overview

Journal Front Med (Lausanne)

Specialty General Medicine

Date 2024 Oct 7

PMID 39371340

Authors

Chaotian Luo

Fei Peng

Fengming Xu

Cheng Tang

Yanyan Zhang

Chaojie Huang

Linlin Liang

Xiaojing Ning

Peng Peng

Affiliations

Soon will be listed here.

Abstract

Background: CMRtools is a software package that can be used to measure T2* values to diagnose liver iron overload, however, its accuracy in terms is affected by multiple factors, including goodness-of-fit (R value), the number of echo time (TE) images, and the liver iron concentration (LIC). To investigate the effects of the R value, the number of TE images, and the LIC on the accuracy of CMRtools software for measuring T2* values to diagnose liver iron overload (LIO).

Materials And Methods: CMRtools software was used to measure liver T2* values among 108 thalassemia patients via the truncation method, and the R values, the number of TE images, and T2* values were recorded. These values were subsequently converted into liver iron concentration (LIC) values. The LIC (derived from MRI-R2/FerriScan) was used as a reference, and the diagnostic accordance rate (DAR) was compared between R value subgroups, between TE image number subgroups, and between LIC subgroups.

Results: The greater the R value was, the greater the standardized DAR (SDAR) was ( < 0.05). The SDAR are not identical between each TE image number subgroup ( > 0.05). However, the relationship between TE image number subgroups and SDAR was analysed using Spearman's correlation, and it was found to be positively correlated ( = 0.729, = 0.017). The SDAR are not identical between each LIC subgroup ( > 0.05), furthermore, the relationship between LIC subgroup and SDAR was found irrelevant ( = 0.747).

Conclusion: The accuracy of CMRtools software for diagnosing LIO in patients with thalassemia can be improved by artificially controlling the number of TE images to be fitted and selecting higher R values.

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