Deep Learning Staging of Liver Iron Content From Multiecho MR Images

Autor: Vincenzo Positano, Antonella Meloni, Maria Filomena Santarelli, Laura Pistoia, Anna Spasiano, Liana Cuccia, Tommaso Casini, Maria Rita Gamberini, Massimo Allò, Pier Paolo Bitti, Alessia Pepe, Filippo Cademartiri
Rok vydání: 2022
Předmět:
Zdroj: Journal of Magnetic Resonance Imaging. 57:472-484
ISSN: 1522-2586
1053-1807
Popis: 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.To develop a deep-learning method for unsupervised classification of LIC from magnitude T2* multiecho MR images.Retrospective.A total of 1069 thalassemia major patients enrolled in the core laboratory of the Myocardial Iron Overload in Thalassemia (MIOT) network, which were included in the training (80%) and test (20%) sets. Twenty patients from different MRI vendors included in the external test set.A5 T, T2* multiecho magnitude images.Four deep-learning convolutional neural networks (HippoNet-2D, HippoNet-3D, HippoNet-LSTM, and an ensemble network HippoNet-Ensemble) were used to achieve unsupervised staging of LIC using five classes (normal, borderline, middle, moderate, severe). The training set was employed to construct the deep-learning model. The performance of the LIC staging model was evaluated in the test set and in the external test set. The model's performances were assessed by evaluating the accuracy, sensitivity, and specificity with respect to the ground truth labels obtained by T2* measurements and by comparison with operator-induced variability originating from different region of interest (ROI) placements.The network's performances were evaluated by single-class accuracy, specificity, and sensitivity and compared by one-way repeated measures analysis of variance (ANOVA) and one-way ANOVA.HippoNet-Ensemble reached an accuracy significantly higher than the other networks, and a sensitivity and specificity higher than HippoNet-LSTM. Accuracy, sensitivity, and specificity values for the LIC stages were: normal: 0.96/0.93/0.97, borderline: 0.95/0.85/0.98, mild: 0.96/0.88/0.98, moderate: 0.95/0.89/0.97, severe: 0.97/0.95/0.98. Correctly staging of cases was in the range of 85%-95%, depending on the LIC class. Multiclass accuracy was 0.90 against 0.92 for the interobserver variability.The proposed HippoNet-Ensemble network can perform unsupervised LIC staging and achieves good prognostic performance.4 TECHNICAL EFFICACY: Stage 2.
Databáze: OpenAIRE
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