TH-A-19A-03: Impact of Proton Dose Calculation Method On Delivered Dose to Lung Tumors: Experiments in Thorax Phantom and Planning Study in Patient Cohort

Authors


Abstract

Purpose:

Evaluate Monte Carlo (MC) dose calculation and the prediction of the treatment planning system (TPS) in a lung phantom and compare them in a cohort of 20 lung patients treated with protons.

Methods:

A 2-dimensional array of ionization chambers was used to evaluate the dose across the target in a lung phantom. 20 lung cancer patients on clinical trials were re-simulated using a validated Monte Carlo toolkit (TOPAS) and compared to the TPS.

Results:

MC increases dose calculation accuracy in lung compared to the clinical TPS significantly and predicts the dose to the target in the phantom within ±2%: the average difference between measured and predicted dose in a plane through the center of the target is 5.6% for the TPS and 1.6% for MC. MC recalculations in patients show a mean dose to the clinical target volume on average 3.4% lower than the TPS, exceeding 5% for small fields. The lower dose correlates significantly with aperture size and the distance of the tumor to the chest wall (Spearman's p=0.0002/0.004). For large tumors MC also predicts consistently higher V5 and V10 to the normal lung, due to a wider lateral penumbra, which was also observed experimentally. Critical structures located distal to the target can show large deviations, though this effect is very patient-specific.

Conclusion:

Advanced dose calculation techniques, such as MC, would improve treatment quality in proton therapy for lung cancer by avoiding systematic overestimation of target dose and underestimation of dose to normal lung. This would increase the accuracy of the relationships between dose and effect, concerning tumor control as well as normal tissue toxicity. As the role of proton therapy in the treatment of lung cancer continues to be evaluated in clinical trials, this is of ever-increasing importance.

This work was supported by National Cancer Institute Grant R01CA111590.

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