Paper detail

Clinically applicable Monte Carlo-based biological dose optimization for the treatment of head and neck cancers with spot-scanning proton therapy

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods: Recently, a fast and accurate Graphics Processor Unit (GPU)-based MC simulation of proton transport was developed and used as the dose calculation engine in a GPU-accelerated IMPT optimizer. Besides dose, the dose-averaged linear energy transfer (LETd) can be simultaneously scored, which makes biological dose (BD) optimization possible. To convert from LETd to BD, a linear relation was assumed. Using this novel optimizer, inverse biological planning was applied to 4 patients: 2 small and 1 large thyroid tumor targets, and 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional IMRT and IMPT plans were created for each case in Eclipse (Varian, Inc). The same critical structure PD constraints were used for the IMRT, IMPT and bio-optimized plans. The BD for the IMPT plans were obtained through MC re-calculations. Results: Compared to standard IMPT, the bio-optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared to both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid case, and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the bio-optimized plans can be completed in a clinically viable time (<30 minutes) on a 24-GPU system. Conclusion: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small-target tumor patients. This optimizer will be used in an upcoming feasibility trial on LETd painting for radio-resistant tumors.