title: Use of 4-dimensional computed tomography-based ventilation imaging to correlate lung dose and function with clinical outcomes.
abstract: OBJECTIVE
  Four-dimensional computed tomography (4DCT)-based ventilation is an emerging imaging modality that can be used in the thoracic treatment planning process. The clinical benefit of using ventilation images in radiation treatment plans remains to be tested. The purpose of the current work was to test the potential benefit of using ventilation in treatment planning by evaluating whether dose to highly ventilated regions of the lung resulted in increased incidence of clinical toxicity.
  
  
  METHODS
  Pretreatment 4DCT data were used to compute pretreatment ventilation images for 96 lung cancer patients. Ventilation images were calculated using 4DCT data, deformable image registration, and a density-change based algorithm. Dose-volume and ventilation-based dose function metrics were computed for each patient. The ability of the dose-volume and ventilation-based dose-function metrics to predict for severe (grade 3+) radiation pneumonitis was assessed using logistic regression analysis, area under the curve (AUC) metrics, and bootstrap methods.
  
  
  RESULTS
  A specific patient example is presented that demonstrates how incorporating ventilation-based functional information can help separate patients with and without toxicity. The logistic regression significance values were all lower for the dose-function metrics (range P=.093-.250) than for their dose-volume equivalents (range, P=.331-.580). The AUC values were all greater for the dose-function metrics (range, 0.569-0.620) than for their dose-volume equivalents (range, 0.500-0.544). Bootstrap results revealed an improvement in model fit using dose-function metrics compared to dose-volume metrics that approached significance (range, P=.118-.155).
  
  
  CONCLUSIONS
  To our knowledge, this is the first study that attempts to correlate lung dose and 4DCT ventilation-based function to thoracic toxicity after radiation therapy. Although the results were not significant at the .05 level, our data suggests that incorporating ventilation-based functional imaging can improve prediction for radiation pneumonitis. We present an important first step toward validating the use of 4DCT-based ventilation imaging in thoracic treatment planning.
authors.affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado, USA. yevgeniy.vinogradskiy@ucdenver.edu
authors.forename: Yevgeniy
authors.initials: Y
authors.lastname: Vinogradskiy
authors.forename: Richard
authors.initials: R
authors.lastname: Castillo
authors.forename: Edward
authors.initials: E
authors.lastname: Castillo
authors.forename: Susan L
authors.initials: SL
authors.lastname: Tucker
authors.forename: Zhongxing
authors.initials: Z
authors.lastname: Liao
authors.forename: Thomas
authors.initials: T
authors.lastname: Guerrero
authors.forename: Mary K
authors.initials: MK
authors.lastname: Martel
mesh_terms: D019540:Area Under Curve; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging / Q000503:physiopathology / Q000532:radiotherapy*; D016002:Discriminant Analysis; D004307:Dose-Response Relationship, Radiation; D056973:Four-Dimensional Computed Tomography; D006801:Humans; D016015:Logistic Models; D008168:Lung / Q000000981:diagnostic imaging / Q000528:radiation effects*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000503:physiopathology / Q000532:radiotherapy*; D012123:Pulmonary Ventilation / Q000502:physiology*; D017564:Radiation Pneumonitis / Q000175:diagnosis / Q000209:etiology*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D016896:Treatment Outcome