Clinical Issues for Pediatric Pulmonologists Managing Children With Thoracic Insufficiency Syndrome

Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks

Purpose

Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem.

Material & Methods

Considering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results.

Results

The proposed system yielded mean Dice coefficients of 0.96±0.02 and 0.89±0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations.

Conclusions

The proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

Do Rib-Based Anchors Impair Chest Wall Motion in Early Onset Scoliosis (EOS)?

Purpose

There is a concern in pediatric surgery practice that rib-based fixation may limit chest wall motion in early onset scoliosis (EOS). The purpose of this study is to address the above concern by assessing the contribution of chest wall excursion to respiration before and after surgery.

Methods

Quantitative dynamic magnetic resonance imaging (QdMRI) is performed on EOS patients (before and after surgery) and normal children in this retrospective study. QdMRI is purely an image-based approach and allows free breathing image acquisition. Tidal volume parameters for chest walls (CWtv) and hemi-diaphragms (Dtv) were analyzed on concave and convex sides of the spinal curve. EOS patients (1-14 years) and normal children (5-18 years) were enrolled, with an average interval of two years for dMRI acquisition before and after surgery.

Results

CWtv significantly increased after surgery in the global comparison including all EOS patients (p < 0.05). For main thoracic curve (MTC) EOS patients, CWtv significantly improved by 50.24% (concave side) and 35.17% (convex side) after age correction (p < 0.05) after surgery. The average ratio of Dtv to CWtv on the convex side in MTC EOS patients was not significantly different from that in normal children (p=0.78), although the concave side showed the difference to be significant.

Conclusion

Chest wall component tidal volumes in EOS patients measured via QdMRI did not decrease after rib-based surgery, suggesting that rib-based fixation does not impair chest wall motion in pediatric patients with EOS.

A porcine model of early-onset scoliosis combined with thoracic insufficiency syndrome: Construction and transcriptome analysis

BACKGROUND

Early-onset scoliosis (EOS) is a scoliosis deformity caused by various reasons before the age of 10 years and is often combined with thoracic insufficiency syndrome (TIS) causing patients with difficulty in securing lung growth in the thoracic cage. Currently, there is a shortage of effective large animal models for evaluating EOS + TIS in therapeutic studies. Consequently, we propose to construct a porcine EOS + TIS model and evaluate its transcriptome changes by RNA sequencing.

METHODS

Piglets were constructed using unilateral posterior spine-tethering and ipsilateral rib-tethering in the EOS + TIS model, and X-ray and computed tomography (CT) were performed to assess growth changes in the spine, thoracic cage and lungs. The H&E and Masson staining was performed for pathological analysis of lung tissue. After RNA sequencing of lung tissues, data were analyzed for differential expression of mRNA, functional enrichment analysis (GO, KEGG and GSEA) and protein-protein interaction (PPI) network construction, and differential expression of hub gene was verified by RT-qPCR.

RESULTS

In the model group, growth (body weight and length) of piglets was significantly delayed; fusion of ribs occurred and cobb angle changes in the coronal and sagittal planes were significantly enlarged; total lung volume (TLV) was significantly reduced, especially at the T7-T10 level. Pathological analysis revealed that, in the model lung tissue, the alveolar wall of was poorly perfused, the alveolar space was enlarged, the number and size of alveoli were significantly reduced, and it was accompanied by collagen fiber deposition. Moreover, a total of 432 differentially expressed mRNAs (DE-mRNAs) were identified in model lung tissues, which contained 262 down-regulated and 170 up-regulated DE-mRNAs, and they were mainly involved in the regulation of immunity, inflammation, cell cycle and extracellular matrix. A PPI network containing 71 nodes and 158 edges was constructed based on all DE-mRNAs, and JUN, CCL2, EGR1, ATF3, BTG2, DUSP1 and THBS1 etc. were hub gene.

CONCLUSIONS

Overall, we constructed a porcine model that was capable of replicating the common clinical features of EOS + TIS such as rib fusion, asymmetric thoracic cage, increased cobb angle, decreased TLV, and pulmonary hypoplasia. Also, we revealed transcriptomic changes in the EOS + TIS model that may cause pulmonary hypoplasia.