NUMERICAL STUDY AND FRACTAL ANALYSIS ON EARLY DIAGNOSIS OF LUNG CANCER

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, and early detection is key to improving survival rates. However, current diagnostic methods, such as CT scans and MRIs, often involve radiation exposure or invasive procedures, and may not provide an accurate diagnosis at the early stages of lung cancer. There is an urgent need for non-invasive diagnostic methods that can detect lung cancer at the early stages with high sensitivity. This work proposes a new non-invasive method for early lung cancer detection by employing computational fluid dynamics (CFD) and fractal analysis. Through CFD simulations, we study the airflow patterns in the lungs, such as turbulence, velocity changes, and flow path deviations, as potential indicators of cancer presence and progression. Besides, fractal theory is utilized to evaluate the complexity of lung structures and cancer regions. By analyzing fractal features in exhaled aerosol images, we can identify evidence of cancer presence at the early stages. The results indicate that the changes in fractal dimensions are closely related to the presence of lung cancers. When particles are released at symmetric positions in both left and right lungs, the exhaled aerosol images are almost mirror-symmetric, and the fractal dimensions of exhaled aerosol are also nearly identical. When particles are released in the same bronchial generation in one side of the lung, although the exhaled aerosol images are different, the fractal dimensions of the exhaled aerosol are very consistent. When particles are released, positions move progressively into higher-generation bronchi, and both the exhaled aerosol images and the average fractal dimensions of exhaled aerosol exhibit significant differences. These results show that the combination of CFD and fractal analysis can provide a powerful non-invasive approach for early lung cancer detection, eliminating the need for invasive biopsies and high-radiation imaging techniques.