title: Still equivalent for dose calculation in the Monte Carlo era? A comparison of free breathing and average intensity projection CT datasets for lung SBRT using three generations of dose calculation algorithms.
abstract: OBJECTIVE
  Inhomogeneity dose modeling and respiratory motion description are two critical technical challenges for lung stereotactic body radiotherapy, an important treatment modality for small size primary and secondary lung tumors. Recent studies revealed lung density-dependent target dose differences between Monte Carlo (Type-C) algorithm and earlier algorithms. Therefore, this study aimed to investigate the equivalence of the two most popular CT datasets for treatment planning, free breathing (FB) and average intensity projection (AIP) CTs, using Type-C algorithms, and comparing with two older generation algorithms (Type-A and Type-B).
  
  
  METHODS
  Twenty patients (twenty-one lesions) were planned using a Type-A algorithm on the FB CT. Lung was contoured separately on FB and AIP CTs and compared. Dose comparison was obtained between the two CTs using four commercial dose algorithms including one Type-A (Pencil Beam Convolution - PBC), one Type-B (Analytical Anisotropic Algorithm - AAA), and two Type-C algorithms (Voxel Monte Carlo - VMC and Acuros External Beam - AXB). For each algorithm, the dosimetric parameters of the target (PTV, Dmin, Dmax, Dmean, D95, and D90) and lung (V5, V10, V20, V30, V35, and V40) were compared between the two CTs using the Wilcoxon signed rank test. Correlation between dosimetric differences and density differences for each algorithm were studied using linear regression and Spearman correlation, in which both global and local density differences were evaluated.
  
  
  RESULTS
  Although the lung density differences on FB and AIP CTs were statistically significant (P = 0.003), the magnitude was small at 1.21 ± 1.45%. Correspondingly, for the two Type-C algorithms, target and lung dosimetric differences were small in magnitude and statistically insignificant (P > 0.05) for all but one instance, similar to the findings for the older generation algorithms. Nevertheless, a significant correlation was shown between the dosimetric and density differences for Type-C and Type-B algorithms, but not for the Type-A algorithm.
  
  
  CONCLUSIONS
  With the capability to more accurately model inhomogeneity, Monte Carlo (Type-C) algorithms are sensitive to respiration-induced local and global tissue density changes and exhibit a strong correlation between dosimetric and density differences. However, FB and AIP CTs may still be considered equivalent for dose calculation in the Monte Carlo era, due to the small magnitude of lung density differences between these two datasets.
authors.affiliation: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA.
authors.forename: Kristina
authors.initials: K
authors.lastname: Zvolanek
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Rongtao
authors.initials: R
authors.lastname: Ma
authors.affiliation: School of Biological Sciences, University of Chicago, Chicago, IL, 60637, USA.
authors.forename: Christina
authors.initials: C
authors.lastname: Zhou
authors.affiliation: University of Florida Health Proton Therapy Institute, Jacksonville, FL, 32206, USA.
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Liang
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Shuo
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Vivek
authors.initials: V
authors.lastname: Verma
authors.affiliation: Department of Radiation Oncology, Georgetown University Hospital, Washington, DC, 20007, USA.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Department of Radiation Medicine, Northwell Health, New York, NY, 10040, USA.
authors.forename: Qinghui
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology, Nebraska Methodist Hospital, Omaha, NE, 68114, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Driewer
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Chi
authors.initials: C
authors.lastname: Lin
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Weining
authors.initials: W
authors.lastname: Zhen
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Wahl
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Su-Min
authors.initials: SM
authors.lastname: Zhou
authors.affiliation: Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
authors.forename: Dandan
authors.initials: D
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000532:radiotherapy*; D009010:Monte Carlo Method; D016634:Radiosurgery; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D014057:Tomography, X-Ray Computed
keywords: 
  CT
  ; algorithm; density; dose; lung SBRT