  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.
