Evaluation of fluence-smoothing feature for three IMRT planning systems

Abstract

Commercially available intensity-modulated radiation therapy (IMRT) inverse treatment planning systems (ITPS) typically include a smoothing function which allows the user to vary the complexity of delivered beam fluence patterns. This study evaluated the behavior of three ITPSs when varying smoothing parameters. We evaluated four cases treated with IMRT in our clinic: sinonasal carcinoma (SNC), glioblastoma multiforme (GBM), base of tongue carcinoma (BOT), and prostate carcinoma (PST). Varian Eclipse v6.5, BrainLAB BrainScan v5.31, and Nomos Corvus v6.2 ITPSs were studied for the SNC, GBM, and PST sites. Only Eclipse and Corvus were studied for BOT due to field size constraints of the BrainLAB MM3 collimator. For each ITPS, plans were first optimized using vendor-recommended default "smoothing" values. Treatment plans were then reoptimized, exploring various smoothing values. Key metrics recorded included a delivery complexity (DC) metric and the Ian Paddick Conformality Index (IPCI). Results varied widely by vendor with regard to the impact of smoothing on complexity and conformality. Plans run on the Corvus ITPS showed the logically anticipated increase in DC as smoothing was decreased, along with associated improved organ-at-risk (OAR) sparing. Both Eclipse and BrainScan experienced an expected trend for increased DC as smoothing was decreased. However, this increase did not typically result in appreciably improved OAR sparing. For Eclipse and Corvus, and to a much lesser extent BrainScan, increases in smoothing decreased DC but eventually caused unacceptable losses in plan conformality. Depending on the ITPS, potential benefits from optimizing fluence smoothing levels can be significant, allowing for increases in either efficiency or conformality. Because of variability in smoothing function behavior by ITPS, it is important that users familiarize themselves with the effects of varying smoothing parameters for their respective ITPS. Based on the experience gained here, we provide recommended workflows for each ITPS to best exploit the fluence-smoothing features of the system.