Automated robust SBPT planning using EUD-based prediction of SBRT plan for patients with lung cancer

To evaluate the quality of robust stereotactic body proton therapy (RSBPT) plans generated by one-clicking scripting method for patients with lung cancer.

Retrospective analysis was performed on fifty lung cancer patients whose plan with robustly stereotactic body radiation therapy (SBRT). Thirty out of fifty patients were used for training to build a regression model, based on robust SBRT reference doses, to predict EUD values of ROIs for robust SBPT planning. Thereafter, robust SBPT plans with both automated EUD-Based mimicking (Automated Robust Proton ARP) and manual (Manual Robust Proton MRP) methods were evaluated in the remaining 20 patients. Plans were compared in terms of dosimetric parameters and planning time.

A statistically significantly improvement in target dose fall off was observed for ARP plans compare to MRP plans (Dose fall off: 135 for MRP and 88 for ARP, p < 0.01), while no differences in target coverage and conformity. A statistically significantly reduce in normal lung tissue were observed for ARP plans compare to MRP plans (Lung [D_mean cGy (RBE)]: MRP: 478 vs. ARP: 351, p < 0.01; Lung [V_{5Gy (RBE)} (%)]: MRP: 16.1 vs. ARP: 12.1, p < 0.01; Lung [V_{20Gy (RBE)} (%)]: MRP: 8.5 vs. ARP: 6.8, p < 0.01). Planning time was reduced for ARP plans compare to MRP plans (optimization time: 12 min for MRP vs. 8 min for ARP; total plan time: 23 min for MRP vs. 18 min for ARP).

The automated robust SBPT plans using EUD-Based mimicking of SBRT reference dose improve target dose fall off, reduced the radiation doses to the lungs, reduce planning time, which might be beneficial for patient with lung cancer in clinical.