title: Using generalized equivalent uniform dose atlases to combine and analyze prospective dosimetric and radiation pneumonitis data from 2 non-small cell lung cancer dose escalation protocols.
abstract: OBJECTIVE
  To demonstrate the use of generalized equivalent uniform dose (gEUD) atlas for data pooling in radiation pneumonitis (RP) modeling, to determine the dependence of RP on gEUD, to study the consistency between data sets, and to verify the increased statistical power of the combination.
  
  
  METHODS
  Patients enrolled in prospective phase I/II dose escalation studies of radiation therapy of non-small cell lung cancer at Memorial Sloan-Kettering Cancer Center (MSKCC) (78 pts) and the Netherlands Cancer Institute (NKI) (86 pts) were included; 10 (13%) and 14 (17%) experienced RP requiring steroids (RPS) within 6 months after treatment. gEUD was calculated from dose-volume histograms. Atlases for each data set were created using 1-Gy steps from exact gEUDs and RPS data. The Lyman-Kutcher-Burman model was fit to the atlas and exact gEUD data. Heterogeneity and inconsistency statistics for the fitted parameters were computed. gEUD maps of the probability of RPS rate≥20% were plotted.
  
  
  RESULTS
  The 2 data sets were homogeneous and consistent. The best fit values of the volume effect parameter a were small, with upper 95% confidence limit around 1.0 in the joint data. The likelihood profiles around the best fit a values were flat in all cases, making determination of the best fit a weak. All confidence intervals (CIs) were narrower in the joint than in the individual data sets. The minimum P value for correlations of gEUD with RPS in the joint data was .002, compared with P=.01 and .05 for MSKCC and NKI data sets, respectively. gEUD maps showed that at small a, RPS risk increases with gEUD.
  
  
  CONCLUSIONS
  The atlas can be used to combine gEUD and RPS information from different institutions and model gEUD dependence of RPS. RPS has a large volume effect with the mean dose model barely included in the 95% CI. Data pooling increased statistical power.
authors.affiliation: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Liu
authors.forename: Ellen D
authors.initials: ED
authors.lastname: Yorke
authors.forename: José S A
authors.initials: JS
authors.lastname: Belderbos
authors.forename: Gerben R
authors.initials: GR
authors.lastname: Borst
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Rosenzweig
authors.forename: Joos V
authors.initials: JV
authors.lastname: Lebesque
authors.forename: Andrew
authors.initials: A
authors.lastname: Jackson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002173:Cancer Care Facilities; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology / Q000532:radiotherapy*; D016001:Confidence Intervals; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D008488:Medical Illustration; D008875:Middle Aged; D009426:Netherlands; D009519:New York City; D011446:Prospective Studies; D017564:Radiation Pneumonitis / Q000188:drug therapy / Q000209:etiology*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D013256:Steroids / Q000627:therapeutic use
chemical_list: D013256:Steroids