A deep learning tool without muscle-by-muscle grading to differentiate myositis from facio-scapulo-humeral dystrophy using MRI - 02/07/22
, Franck Mamalet b, 1, Anne Laforet a, Mikael Capelle b, Blandine Acket a, Coralie Sengenes c, Pascal Cintas a, 1, Marie Faruch-Bilfeld d, 1
Bienvenue sur EM-consulte, la référence des professionnels de santé.
Article gratuit.
Connectez-vous pour en bénéficier!
Highlights |
• | A deep learning tool was developed to differentiate facioscapulohumeral muscular dystrophy from myositis using muscle MRI. |
• | This deep learning tool does not require segmentation or manual grading of signal changes in each muscle. |
• | The diagnostic performance of the deep learning tool is equivalent to that of two radiologists. |
Abstract |
Purpose |
The purpose of this study was to assess the capabilities of a deep learning (DL) tool to discriminate between type 1 facioscapulo-humeral dystrophy (FSHD1) and myositis using whole-body muscle magnetic resonance imaging (MRI) examination without the need for visual grading of muscle signal changes.
Materials and methods |
A total of 40 patients who underwent whole-body MRI examination that included T1-weighted and STIR sequences were included. There were 19 patients with proven FSHD1 (9 men, 10 women; mean age, 47.7 ± 18.0 [SD] years; age range: 20–72 years) and 21 patients with myositis fulfilling European Neuromuscular Centre criteria and European League Against Rheumatism and American College of Rheumatology criteria (11 men, 10 women; mean age, 59.3 ± 17.0 [SD]; age range: 19–78 years). Based on thigh, calf, and shoulder sections a supervised training of a neural network was performed and its diagnostic performance was studied using a 5-fold cross validation method and compared to the results obtained by two radiologists specialized in musculoskeletal imaging.
Results |
The DL tool was able to differentiate FSHD1 from myositis with a correct classification percentage respectively of 69 % (95% CI: 39–99), 75% (95% CI: 48–100) and 77% (95% CI: 60–94) when thigh only, thigh and calf or the thigh, calf, and shoulder MR images were analyzed. The percentages of correct classification of the two radiologists for these later MR images were 38/40 (95%) and 35/40 (87.5%), respectively; with no differences with DL tool correct classification (P = 0.41 and P > 0.99, respectively). Among the seven patients who were misclassified by the radiologists, the DL tool correctly classified six of them.
Conclusion |
A DL tool was developed to discriminate between FSHD1 and myositis using whole-body MRI with performances equivalent to those achieved by two radiologists. This study provides a proof of concept of the effectiveness of a DL approach to distinguish between two myopathies using MRI with a small amount of data, and no prior muscle signal changes grading.
Le texte complet de cet article est disponible en PDF.Keywords : Artificial intelligence, Magnetic resonance imaging, Facioscapulohumeral muscular dystrophy, Myositis, Deep learning
Abbreviations : AUC, CI, FSHD1, LGMD, mRS, OR, ROC, SD, STIR, WES
Plan
Vol 103 - N° 7-8
P. 353-359 - juillet 2022 Retour au numéro
Article précédent
- Left ventricular diastolic early and late filling quantified from 4D flow magnetic resonance imaging
- Yousef Alattar, Gilles Soulat, Umit Gencer, Emmanuel Messas, Emilie Bollache, Nadjia Kachenoura, Elie Mousseaux
- Comparison of MRI-based response criteria and radiomics for the prediction of early response to transarterial radioembolization in patients with hepatocellular carcinoma
- Godefroy Aujay, Christèle Etchegaray, Jean-Frederic Blanc, Bruno Lapuyade, Panteleimon Papadopoulos, Marie-Anaïg Pey, Laurence Bordenave, Hervé Trillaud, Olivier Saut, Jean-Baptiste Pinaquy
Bienvenue sur EM-consulte, la référence des professionnels de santé.