Development and Application of a Simplified Mathematical Model for the Estimation of Tumor Dose of Non-Small Cell Lung Cancer in BNCT.

PURPOSE: Lung cancer has exhibited a sustained increase in both incidence and mortality rates in recent years. This study aimed to develop and apply a simplified mathematical model for estimating tumor dose in boron neutron capture therapy for non-small cell lung cancer, to facilitate clinical treatment planning.

METHODS AND MATERIALS: We analyzed the effects of 6 key parameters on dose distribution based on experimental data and established a simplified mathematical model to estimate tumor dose. The model was validated in selected patients with lung cancer under both fixed and random irradiation conditions. Patients were categorized according to whether dose limits of critical organs were exceeded, and the model's accuracy was further assessed in these subgroups. In addition, the surface-to-tumor center distance was defined to evaluate its correlation with the minimum prescribed dose for a single SBRT fraction. The study also investigated the effects of respiratory motion and setup errors on dose accuracy.

RESULTS

The simplified mathematical model achieved estimated dose errors of 4.0% ± 2.8% under fixed irradiation and 4.4% ± 3.2% under random irradiation. For patients categorized based on critical organ dose limits, estimated errors were 9.3% ± 12.2% and 4.6% ± 9.8%, respectively. These results demonstrated the model's high accuracy. Analysis of surface-to-tumor center distance values revealed that 6.66 cm may serve as a threshold for patient selection to ensure the minimum prescribed dose. Respiratory motion introduced a dose estimation error within ±10%, and each 5-mm setup error caused <1% deviation, indicating that these effects are negligible.

CONCLUSIONS

The proposed simplified mathematical model offered accurate and efficient tumor dose estimation for patients with non-small cell lung cancer undergoing boron neutron capture therapy. It served as a valuable reference in clinical practice, enabling physicians to rapidly assess tumor dose using medical imaging data, thus improving the efficiency and precision of treatment planning.