title: Practical methods for improving dose distributions in Monte Carlo-based IMRT planning of lung wall-seated tumors treated with SBRT.
abstract: Current commercially available planning systems with Monte Carlo (MC)-based final dose calculation in IMRT planning employ pencil-beam (PB) algorithms in the optimization process. Consequently, dose coverage for SBRT lung plans can feature cold-spots at the interface between lung and tumor tissue. For lung wall (LW)-seated tumors, there can also be hot spots within nearby normal organs (example: ribs). This study evaluated two different practical approaches to limiting cold spots within the target and reducing high doses to surrounding normal organs in MC-based IMRT planning of LW-seated tumors. First, "iterative reoptimization", where the MC calculation (with PB-based optimization) is initially performed. The resultant cold spot is then contoured and used as a simultaneous boost volume. The MC-based dose is then recomputed. The second technique uses noncoplanar beam angles with limited path through lung tissue. Both techniques were evaluated against a conventional coplanar beam approach with a single MC calculation. In all techniques the prescription dose was normalized to cover 95% of the PTV. Fifteen SBRT lung cases with LW-seated tumors were planned. The results from iterative reoptimization showed that conformity index (CI) and/or PTV dose uniformity (UPTV) improved in 12/15 plans. Average improvement was 13%, and 24%, respectively. Nonimproved plans had PTVs near the skin, trachea, and/or very small lung involvement. The maximum dose to 1cc volume (D1cc) of surrounding OARs decreased in 14/15 plans (average 10%). Using noncoplanar beams showed an average improvement of 7% in 10/15 cases and 11% in 5/15 cases for CI and UPTV, respectively. The D1cc was reduced by an average of 6% in 10/15 cases to surrounding OARs. Choice of treatment planning technique did not statistically significantly change lung V5. The results showed that the proposed practical approaches enhance dose conformity in MC-based IMRT planning of lung tumors treated with SBRT, improving target dose coverage and potentially reducing toxicities to surrounding normal organs.
authors.affiliation: Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA. maltman@radonc.wustl.edu
authors.forename: Michael B
authors.initials: MB
authors.lastname: Altman
authors.forename: Jian-Yue
authors.initials: JY
authors.lastname: Jin
authors.forename: Sangroh
authors.initials: S
authors.lastname: Kim
authors.forename: Ning
authors.initials: N
authors.lastname: Wen
authors.forename: Dezhi
authors.initials: D
authors.lastname: Liu
authors.forename: M Salim
authors.initials: MS
authors.lastname: Siddiqui
authors.forename: Munther I
authors.initials: MI
authors.lastname: Ajlouni
authors.forename: Benjamin
authors.initials: B
authors.lastname: Movsas
authors.forename: Indrin J
authors.initials: IJ
authors.lastname: Chetty
mesh_terms: D000465:Algorithms; D056973:Four-Dimensional Computed Tomography; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000601:surgery*; D009010:Monte Carlo Method; D009068:Movement; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D016634:Radiosurgery; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated