Leveraging 3-D Surface Guidance and Deep Inspiration Breath Hold Protocol to Optimize Simulations of Inhalable Nanoparticle Therapy for Radio-Enhanced Lung Cancer Treatments
Abstract
Purpose
To leverage 3-D surface guidance (SGRT) to investigate the relationship between lung volume and tracked surface area to be used for personalization and optimization of inhalation-based solid nanoparticle treatment simulations.
Methods
Previous simulations have demonstrated the dependence of inhaled NP behavior on lung volume, breath hold length, NP size and initial number density. Until now, parameters used have been average lung volumes and breath hold lengths dictated by previously published experiments. To advance this previous work towards personalized medicine, the recently installed LAP-Luna 3-D SGRT will be used in junction with free breathing and deep inspiration breath hold (DIBH) CT scans. After treatment delivery, SGRT recorded data is exported from the system including time, the monitored surface area parameters. Lung volumes measured from CT are compared with the lung volumes used previously. This data will be collected throughout the spring of 2026 from patients treated under DIBH protocol. Patient information will be anonymized and stored securely.
Results
Preliminary data (n=2) indicates lung volume and breath holding ability vary between patients. Lung volumes during DIBH (5288.63cc; 2996.79cc) measured less than the volume of 5600cc used previously. The ability to hold breath without slowly leaking air out varied between patients, and breathing cycles ranged between 15 to 41 seconds, both higher than used for previous simulations. Surface areas were computed by assuming patients are cylindrical and found to increase during DIBH by ~0.3cm2 and ~1.5cm2 on average for each patient, respectively.
Conclusion
Inter patient variability of lung volumes, breath holding ability and breathing cycle lengths deviate significantly from parameters used during previous simulations, demonstrating the need for increased personalization. The ability to monitor the patient’s surface while undergoing treatment provides a further avenue for simulation personalization, so long that the relationship between lung volume and patient surface area can be elucidated.