Summary:
UCLA researchers have developed a novel method to characterize lung tissue biomechanics and applied it to the creation of a computational model with which to guide lung elastography.
Background:
Lung cancer is the leading cause of death from cancer in people in the US. With an increasing focus on improving the patient’s quality of life during therapy, there is a greater need to spare the normal lung regions and more precisely target the tumor. Approaches such as hypo-fractionated therapy, a method that drastically decreases treatment timelines by increasing individual doses, have been adopted to improve patient outcomes. These therapeutic approaches require an ability to precisely distinguish the regions inside the lung that function normally from those regions that are diseased. Elastography is a non-invasive way to image the local elastic properties of soft tissue to assess mechanical distress commonly associated with many lung diseases. Lung elastography is complicated by the fact that the air in the lungs renders conventional elastography techniques such as ultrasound difficult to perform. However, advances in endobronchial ultrasound elastography have resulted in diagnostic accuracies around 80% and significantly improved malignant vs benign tissue characterization. There remains a growing need to develop new computational models to improve the accuracy and efficiency of elastography measurements using these conventional imaging methods.
Innovation:
UCLA researchers in the Department of Radiation Oncology have developed a novel method to perform lung elastography using deformation information that would be available from four-dimensional computed tomography (4DCT) data acquired during radiotherapy. The advantage of this approach is that the model is formulated from well-established physical laws and requires no additional imaging or processing. Other comorbidities such as chronic obstructive pulmonary disease (COPD) can also be precisely characterized to further improve treatment plans. Unlike conventional elastography methods, this method directly correlates elasticity with the displacement of lung tissue in a clinically relevant manner. This new method has the potential to improve patient treatment plans by allowing radiologists to efficiently identify healthy vs diseased lung tissue for targeted radiotherapy.
Potential Applications:
• Higher-efficacy lung cancer treatment.
• Personalized medicine.
• Respiratory illness diagnosis and treatment.
• Critical care settings
• Hypo-fractionated therapy
Advantages:
• Non-invasive modeling.
• Increased tumor removal success.
• More accurate elasticity characterization.
State of Development:
The inventors have developed a methodology for facile determination of lung tissue elasticity. The procedure was assessed using a virtual lung phantom, and indicated high accuracy can be obtained using the method.
Related Papers:
1. Santhanam, A. P., Stiehl, B., Lauria, M., Hasse, K., Barjaktarevic, I., Goldin, J., & Low, D. A. (2021). An adversarial machine learning framework and biomechanical model‐guided approach for computing 3D lung tissue elasticity from end‐expiration 3DCT. Medical Physics, 48(2), 667-675.
2. Hasse, K., Neylon, J., & Santhanam, A. P. (2017). Feasibility and quantitative analysis of a biomechanical model-guided lung elastography for radiotherapy. Biomedical Physics & Engineering Express, 3(2), 025006.
Reference:
UCLA Case No. 2022-329
Lead Inventor:
Anand Santhanam, Associate Professor in UCLA’s Department of Radiation Oncology.