2022 article

Deep Learning Staging of Liver Iron Content From Multiecho MR Images

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

16Citations signalées, ce qui n’est pas une note de qualité

8Institutions déclarées

1Pays d’affiliation déclarés

Rattachement africain : it. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

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. POPULATION/SUBJECTS: 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. FIELD STRENGTH/SEQUENCE: A5 T, T2* multiecho magnitude images. ASSESSMENT: 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. STATISTICAL TESTS: 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. RESULTS: 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. DATA CONCLUSION: The proposed HippoNet-Ensemble network can perform unsupervised LIC staging and achieves good prognostic performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref: Deep Learning Staging of Liver Iron Content From Multiecho MR Images
Date Crossref: 17/06/2022
Éditeur: Wiley
Type: journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

Fondazione Toscana Gabriele Monasterio pays non établi dans la notice
Institution
Regione Toscana pays non établi dans la notice
Organisme public
Istituto di Fisiologia Clinica pays non établi dans la notice
Structure de recherche
Azienda di Rilievo Nazionale ed Alta Specializzazione pays non établi dans la notice
Établissement de santé
Meyer Children's Hospital pays non établi dans la notice
Établissement de santé
Ospedale Sant'Anna pays non établi dans la notice
Établissement de santé
Presidio Ospedaliero pays non établi dans la notice
Établissement de santé
University of Padua Institute of Radiology pays non établi dans la notice
Université ou école supérieure
Department of Radiology pays non établi dans la notice
Institution
U.O.C. Bioingegneria pays non établi dans la notice
Institution
CNR Institute of Clinical Physiology Pisa Italy pays non établi dans la notice
Structure de recherche
Unità Operativa Semplice Dipartimentale Malattie Rare del Globulo Rosso Azienda Ospedaliera di Rilievo Nazionale “A. Cardarelli” Napoli Italy pays non établi dans la notice
Institution

Fondazione Toscana Gabriele Monasterio, Regione Toscana et Istituto di Fisiologia Clinica, avec 9 autres affiliations.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Hemoglobinopathies and Related DisordersIron Metabolism and DisordersErythropoietin and Anemia Treatment

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