Computational Fluid Dynamics (CFD) Analysis of Subject-specific Bronchial Tree Models in Lung Cancer Patients

Area of Science:

• Pulmonary Medicine
• Biomedical Engineering
• Medical Imaging

Background:

• Lung resection for cancer necessitates accurate prediction of postoperative lung function.
• Current clinical methods using pulmonary function tests (PFTs) offer averaged evaluations and neglect local airway function.
• Computational Fluid Dynamics (CFD) shows potential for patient-specific, quantitative analysis of airflow dynamics.

Purpose of the Study:

• To apply CFD to characterize airflow dynamics in lung cancer patients.
• To evaluate the impact of tumoral masses on flow parameters and lobar distribution.
• To compare CFD-derived predicted postoperative forced expiratory volume in 1s (ppoFEV1) with existing clinical algorithms.

Main Methods:

• Patient-specific airway models were created from CT images for 12 lung cancer patients.
• Tumor segmentation and measurements of lung and tumor volumes were performed.
• CFD simulations were conducted to analyze flow parameters and lobar volume flow rate (VFR).

Main Results:

• CFD revealed significantly lower airflow to the lung affected by the tumor compared to the contralateral lung (p=0.026).
• No significant lobar alterations in flow parameters were observed.
• CFD-based ppoFEV1 estimation showed high correlation (ρ=0.993, p<0.001) with the clinical formula.

Conclusions:

• CFD is effective and applicable for pre-operative characterization in patients undergoing lobectomy.
• CFD provides valuable local functionality and flow dynamics data to aid surgical decisions.
• This technique enhances the assessment of patient operability for lung cancer surgery.