title: Novel lung IMRT planning algorithms with nonuniform dose delivery strategy to account for respiratory motion.
abstract: To effectively deliver radiation dose to lung tumors, respiratory motion has to be considered in treatment planning. In this paper we first present a new lung IMRT planning algorithm, referred as the dose shaping (DS) method, that shapes the dose distribution according to the probability distribution of the tumor over the breathing cycle to account for respiratory motion. In IMRT planning a dose-based convolution method was generally adopted to compensate for random organ motion by performing 4-D dose calculations using a tumor motion probability density function. We modified the CON-DOSE method to a dose volume histogram based convolution method (CON-DVH) that allows nonuniform dose distribution to account for respiratory motion. We implemented the two new planning algorithms on an in-house IMRT planning system that uses the Eclipse (Varian, Palo Alto, CA) planning workstation as the dose calculation engine. The new algorithms were compared with (1) the conventional margin extension approach in which margin is generated based on the extreme positions of the tumor, (2) the dose-based convolution method, and (3) gating with 3 mm residual motion. Dose volume histogram, tumor control probability, normal tissue complication probability, and mean lung dose were calculated and used to evaluate the relative performance of these approaches at the end-exhale phase of the respiratory cycle. We recruited six patients in our treatment planning study. The study demonstrated that the two new methods could significantly reduce the ipsilateral normal lung dose and outperformed the margin extension method and the dose-based convolution method. Compared with the gated approach that has the best performance in the low dose region, the two methods we proposed have similar potential to escalate tumor dose, but could be more efficient because dose is delivered continuously.
authors.affiliation: Department of Radiation Oncology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15232, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Pengpeng
authors.initials: P
authors.lastname: Zhang
authors.forename: Dennis
authors.initials: D
authors.lastname: Mah
authors.forename: Richard
authors.initials: R
authors.lastname: Gewanter
authors.forename: Gerald
authors.initials: G
authors.lastname: Kutcher
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001822:Body Burden; D003198:Computer Simulation; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000532:radiotherapy*; D008954:Models, Biological; D009068:Movement; D011859:Radiography; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D012062:Relative Biological Effectiveness; D015656:Respiratory Mechanics