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

• Tác giả: Zelia Borsellino, Filippo Cademartiri, Alberto Cossu, Lisa Anita De Santi, Francesco Massei, Antonella Meloni, Laura Pistoia, Vincenzo Positano, Paola Maria Grazia Sanna, Maria Filomena Santarelli
• Ngôn ngữ: eng
• Thông tin xuất bản: Germany : European radiology , 2025
• Bộ sưu tập: NCBI
• ID: 188810

Tóm tắt

 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.