title: Monte Carlo-based lung cancer treatment planning incorporating PET-defined target volumes.
abstract: Despite the well-known benefits of positron emission tomography (PET) imaging in lung cancer diagnosis and staging, the poor spatial resolution of PET has limited its use in radiotherapy planning. Methods used for segmenting tumor from normal tissue, such as threshold boundaries using a fraction of the standardized uptake value (SUV), are subject to uncertainties. The issue of respiratory motion in the thorax confounds the problem of accurate target definition. In this work, we evaluate how changing the PET-defined target volume by varying the threshold value in the segmentation process impacts target and normal lung tissue doses. For each of eight lung cancer patients we retrospectively generated multiple PET-target volumes; each target volume corresponds to those voxels with intensities above a given threshold level, defined by a percentage of the maximum voxel intensity. PET-defined targets were compared to those from CT; CT targets comprise a composite volume generated from breath-hold inhale and exhale datasets; the CT dataset therefore also includes the extents of tumor motion. Treatment plans using Monte Carlo dose calculation were generated for all targets; the dose uniformity was approximately 100+/-5% within the internal target volume (ITV) (formed by a uniform 8-mm expansion of the composite gross target volume (GTV)). In all cases differences were observed in the generalized equivalent uniform doses (gEUDs) to the targets and in the mean lung doses (MLDs) and normal tissue complication probabilities (NTCPs) to the normal lung tissues. The magnitudes of the dose differences were found to depend on the target volume, location, and amount of irradiated normal lung tissue, and in many instances were clinically meaningful (greater than a single 2 Gy fraction). For those patients studied, results indicate that accurate dosimetry using PET volumes is highly dependent on accurate target segmentation. Further study with correlation to clinical outcome will be helpful in determining how to apply these various PET and CT volumes in treatment planning, to potentially improve local tumor control and reduce normal tissue toxicities.
authors.affiliation: Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109-0010, USA. indrin@med.umich.edu
authors.forename: Indrin J
authors.initials: IJ
authors.lastname: Chetty
authors.forename: Shaneli
authors.initials: S
authors.lastname: Fernando
authors.forename: Marc L
authors.initials: ML
authors.lastname: Kessler
authors.forename: Daniel L
authors.initials: DL
authors.lastname: McShan
authors.forename: Cassandra
authors.initials: C
authors.lastname: Brooks
authors.forename: Randall K
authors.initials: RK
authors.lastname: Ten Haken
authors.forename: Feng-Ming Spring
authors.initials: FM
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D049268:Positron-Emission Tomography; D011859:Radiography; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity