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Objective
Multipartite epicondyles may mimic fractures in the setting of pediatric elbow trauma. This study examines the prevalence of multipartite epicondyles during skeletal development and their association with pediatric elbow fractures.
Materials and methods
In this retrospective analysis, 4282 elbow radiographs of 1265 elbows of 1210 patients aged 0–17 years were reviewed. The radiographs were analyzed by two radiologists in consensus reading, and the number of visible portions of the medial and lateral epicondyles was noted. For elbows in which epicondylar ossification was not yet visible, the epicondyles were already fused with the humerus or could not be sufficiently evaluated due to projection issues or because osteosynthesis material was excluded. In total, 187 elbows were included for the lateral and 715 for the medial epicondyle analyses.
Results
No multipartite medial epicondyles were found in patients without history of elbow fracture, whereas 9% of these patients had multipartite lateral epicondyles (p < 0.01). Current or previous elbow fractures increased the prevalence of multipartite epicondyles, with significant lateral predominance (medial epicondyle + 9% vs. lateral + 24%, p < 0.0001). Including all patients regardless of a history of elbow fracture, multipartite medial epicondyles were observed in 3% and multipartite lateral epicondyles in 18% (p < 0.0001). There was no gender difference in the prevalence of multipartition of either epicondyle, regardless of a trauma history.
Conclusion
Multipartite medial epicondyles occur in patients with current or previous elbow fractures only, whereas multipartite lateral epicondyles may be constitutional. Elbow fractures increase the prevalence of multipartite epicondyles on both sides, with significant lateral predominance.
Key Points
• Multipartite medial epicondyles should be considered of traumatic origin.
• Multipartite lateral epicondyles may be constitutional.
• Elbow fractures increase the prevalence of multipartite epicondyles on both sides with lateral predominance.
Background
Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters.
Methods
In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis.
To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer.
Results
The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L.
Conclusions
Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.