Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients.

Objectives: To develop a deep-learning model for supervised classification of myocardial iron overload (MIO) from magnitude T2* multi-echo MR images.
Materials and Methods: Eight hundred twenty-three cardiac magnitude T2* multi-slice, multi-echo MR images from 496 thalassemia major patients (285 females, 57%), labeled for MIO level (normal: T2* > 20 ms, moderate: 10 ≤ T2* ≤ 20 ms, severe: T2* < 10 ms), were retrospectively studied. Two 2D convolutional neural networks (CNN) developed for multi-slice (MS-HippoNet) and single-slice (SS-HippoNet) analysis were trained using 5-fold cross-validation. Performance was assessed using micro-average, multi-class accuracy, and single-class accuracy, sensitivity, and specificity. CNN performance was compared with inter-observer agreement between radiologists on 20% of the patients. The agreement between patients' classifications was assessed by the inter-agreement Kappa test.
Results: Among the 165 images in the test set, a multi-class accuracy of 0.885 and 0.836 was obtained for MS- and SS-Hippo-Net, respectively. Network performances were confirmed on external test set analysis (0.827 and 0.793 multi-class accuracy, 29 patients from the CHMMOTv1 database). The agreement between automatic and ground truth classification was good (MS: κ = 0.771; SS: κ = 0.614), comparable with the inter-observer agreement (MS: κ = 0.872, SS: κ = 0.907) evaluated on the test set.
Conclusion: The developed networks performed classification of MIO level from multiecho, bright-blood, and T2* images with good performances.
Key Points: Question MRI T2* represents the established clinical tool for MIO assessment. Quality control of the image analysis is a problem in small centers. Findings Deep learning models can perform MIO staging with good accuracy, comparable to inter-observer variability of the standard procedure. Clinical relevance CNN can perform automated staging of cardiac iron overload from multiecho MR sequences facilitating non-invasive evaluation of patients with various hematologic disorders.
Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Dr Filippo Cademartiri. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: One of the authors (Antonella Meloni) has significant statistical expertise. Informed consent: Written informed consent was obtained from all subjects (patients) in this study. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: The image data used in the study represents part of the MIOT/eMIOT database, which was previously involved in several studies of thalassemia major patients with different scientific objectives. Methodology: Retrospective Observational Performed at one institution
(© 2024. The Author(s), under exclusive licence to European Society of Radiology.)