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Quantifying Lung, Diaphragm & Thoracospinal Radiographic Parameters to Predict Lung Volume/Function
Start Date: 6/16/2022Start Time: 4:00 PM
End Date: 6/16/2022End Time: 6:00 PM
Event Description
BIOMED Master's Thesis Defense

Quantifying Lung, Diaphragm, and Thoracospinal Radiographic Parameters to Predict Lung Volume and Function in Pediatric Normative and Early Onset Scoliosis Subjects

Mattan R. Orbach, Master’s Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Sriram Balasubramanian, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Lung volume, a clinical indicator of lung growth and function, is commonly quantified using pulmonary function tests (PFTs) such as spirometry and body plethysmography which measure lung volumes, airflow, and gas exchange. In addition to monitoring lung growth and aiding in the diagnoses of obstructive and restrictive lung diseases, PFTs are routinely used in the treatment of the over 100,000 pediatric children in the United States diagnosed each year with scoliosis, a complex 3D spine and ribcage deformity. PFTs are especially important to monitor and grade surgical outcomes in early onset scoliosis (EOS), defined as a lateral spinal curvature ≥ 10 degrees diagnosed before 10 years of age. This is because the earlier developed thoracospinal deformities in EOS more often lead to thoracic insufficiency syndrome (TIS), defined as the inability of the thorax to maintain normal lung growth and function. Although cost-effective and noninvasive, PFT data from young patients may be challenging to collect and skewed due to poor patient cooperation. In such cases, CT-based methods to calculate lung volume are viable alternatives as they require less patient cooperation. However, such methods are technically demanding and involve extensive radiation exposure. Thus, there is an interest in estimating lung volume directly from radiographs which are routinely used diagnostic tools for the assessment of pulmonary disorders and qualitative evaluation of lung volume in pediatric subjects.

Digitally reconstructed radiographs (DRRs) were created using chest CT scans from 77 normative pediatric subjects ages birth to 19 years. 2D lung and diaphragm measurements were made on the DRRs using custom code, and linear regression analysis was used to correlate the 2D measurements with age. Additionally, left and right 3D lung volumes were segmented using CT scans, and power regression equations were used to predict CT-derived lung volumes from 2D lung measurements. The same 2D radiographic lung and diaphragm measurements, as well as 20 literature-based thoracospinal deformity parameters, were then measured on frontal and lateral radiographs from 41 EOS subjects using a custom MATLAB graphical user interface (GUI). After accounting for multicollinearity and rigorously testing assumptions of regression, the backward elimination feature selection method was used to develop multiple linear regression (MLR) models to predict spirometry-derived percent predicted forced vital capacity (%FVC) and forced expiratory volume in one second (%FEV1) from these parameters. The coefficient of determination (R2) and standard error of the estimate (SEE) were used to assess the precision of the predictive equations with p < .05 indicating statistical significance.

In normative pediatric subjects, 2D radiographic lung and diaphragm measurements showed statistically significant (p < .005) positive correlations with age and left and right CT-derived lung volumes were precisely (R2 = 0.99) estimated using simple 2D radiographic lung measurements. Although the same lung and diaphragm morphological parameters failed to produce MLR models to predict PFT, the additive contributions of multiple thoracospinal deformity parameters yielded significant (p < .001) MLR models that can predict %FVC and %FEV1 with clinically relevant precision (R2 ≥ 0.65) in EOS subjects.

Such data may serve as reference values for understanding age-related changes in normative radiographic lung and diaphragm morphology, and the regression equations developed in this study can help in the rapid clinical assessment of lung volume. The additive contributions of multiple radiographic parameters that encompass thoracospinal deformity in both the coronal and sagittal planes were able to predict PFT outcomes in EOS subjects with the highest reported precision. Additionally, the findings support that the decline of pulmonary function in EOS is associated more with the altered thoracospinal skeletal deformations that cause altered breathing mechanics rather than the alterations in lung field and diaphragm morphology.
Contact Information:
Name: Natalia Broz
Mattan Orbach
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