A dosimetric and robustness analysis of proton arc therapy with early energy layer and spot assignment for lung cancer versus conventional intensity modulated proton therapy.

Abstract

Background and purpose: Intensity Modulated Proton Therapy (IMPT) faces challenges in lung cancer treatment, like maintaining plan robustness for moving tumors against setup, range errors, and interplay effects. Proton Arc Therapy (PAT) is an alternative to maintain target coverage, potentially improving organ at risk (OAR) sparing, reducing beam delivery time (BDT), and enhancing patient experience. We aim to perform a systematic plan comparison study between IMPT and energy layer (EL) and spot assignment algorithm - Proton Arc Therapy (ELSA-PAT) to assess its potential for lung cancer treatment.

Material and methods: A total of 14 Lung ELSA-PAT plans were compared retrospectively with IMPT plans. 4D worst-case minimax robust optimization was performed, including 84 scenarios (3%, 3 mm). Dosimetry assessment included target (clinical tumor volume [CTV]) and important OARs, on nominal and worst-case scenarios. Most relevant normal tissue complication probabilities (NTCP), target coverage robustness against interplay effect, and BDT were evaluated.

Results: CTV D95% and D98% showed no significant difference in comparison. PAT demonstrated better conformality by 66% (p = 0.00012) but delivered a higher heart mean dose (HMD, 23%). There was a 2% increase in NTCP 2-year mortality risk with PAT. Total BDT was comparable among techniques. IMPT was more robust than PAT against interplay effect, considering both D1% (1.0 ± 0.8 Gy vs 1.1 ± 1.4 Gy) and D98% bandwidths (0.9 ± 0.9 Gy vs 1.1 ± 1.3 Gy).

Interpretation: Both techniques provide a similar level of dose coverage to the target volume. Although PAT improved dose conformality, higher HMD translated into increased heart toxicity, presumably due to chosen planning methodology and OAR proximity to target. Increased ELs and spots raised PAT BDT, although it could improve daily treatment workflow.