Pancreatic fatty replacement as risk marker for altered glucose metabolism and cardiac iron and complications in thalassemia major.

Objectives: This multicenter study assessed the extent of pancreatic fatty replacement and its correlation with demographics, iron overload, glucose metabolism, and cardiac complications in a cohort of well-treated patients with thalassemia major (TM).

Methods: We considered 308 TM patients (median age: 39.79 years; 182 females) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia Network.

Myocardial tissue characterization by segmental T2 mapping in thalassaemia major: detecting inflammation beyond iron.

Aims: We measured myocardial T2 values by a segmental approach in thalassaemia major (TM) patients, comparing such values against T2* values for the detection of myocardial iron overload (MIO), evaluating their potential in detecting subclinical inflammation, and correlating with clinical status.

Methods And Results: One-hundred and sixty-six patients (102 females, 38.29 ± 11.

Increased myocardial extracellular volume is associated with myocardial iron overload and heart failure in thalassemia major.

Objectives: Myocardial extracellular volume (ECV) by cardiovascular magnetic resonance (CMR) is a surrogate marker of diffuse fibrosis. We evaluated the association between ECV and demographics, CMR findings, and cardiac involvement in patients with thalassemia major (TM).

Methods: A total of 108 β-TM patients (62 females, 40.

Gender Differences in Knowledge and Perception of Cardiovascular Disease among Italian Thalassemia Major Patients.

We evaluated gender differences in knowledge and perception of cardiovascular disease (CVD) among Italian thalassemia major (TM) patients. An anonymous questionnaire was completed by 139 β-TM patients (87 (62.7%) females, 40.

Deep Learning Staging of Liver Iron Content From Multiecho MR Images.

Background: MRI represents the most established liver iron content (LIC) evaluation approach by estimation of liver T2* value, but it is dependent on the choice of the measurement region and the software used for image analysis.

Purpose: To develop a deep-learning method for unsupervised classification of LIC from magnitude T2* multiecho MR images.

Study Type: Retrospective.