Abstract
Lung cancer is the leading cause of cancer-related deaths. Many methods and devices help acquire more accurate clinical and localization information during lung interventions and may impact the death rate for lung cancer. However, there is a learning curve for operating these tools due to the complex structure of the airway. In this study, we first discuss the creation of a lung phantom model from medical images, which is followed by a comparison of 3D printing in terms of quality and consistency. Two tests were conducted to test the performance of the developed phantom, which was designed for training simulations of the target and ablation processes in endochonchial interventions. The target test was conducted through an electromagnetic tracking catheter with navigation software. An ablation catheter with a recently developed thermochromic ablation gel conducted the ablation test. The results of two tests show that the phantom was very useful for target and ablation simulation. In addition, the thermochromic gel allowed doctors to visualize the ablation zone. Many lung interventions may benefit from custom training or accuracy with the proposed low-cost and patient-specific phantom.
Highlights
Lung cancer is the second most common cancer in both men and women [1,2,3,4,5,6]
We used 3D printing technology to build our lung phantom, which was designed to meet the special requirements set by our pulmonologist: low-cost, patient-specific, and easy visualization of catheter and ablated spots, which are comparable to the human adult size
Even though phantom ablation indicator we developed areelastic not material such as silicathe gel couldairway allow the effect ofand respiratory motion in the phantom and result in material such as silica gel could allow the effect of respiratory motion in the phantom and result in perfect, an they can still used for general training practicing at aoflow cost
Summary
More than one million lung cancer cases are diagnosed worldwide each year [3,7]. It has the highest death rate among all types of cancers in the United States and worldwide [2,5,6,7,8,9]. Detection and diagnosis as well as an accurate localization during lung intervention may help reduce the death rate for lung cancer [7]. An accurate localization of medical devices can significantly improve the accuracy of the diagnosis, which further helps clinicians make the optimal decision via a lung treatment decision tree
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