title: Predicting the probability of abnormal stimulated growth hormone response in children after radiotherapy for brain tumors.
abstract: OBJECTIVE
  To develop a mathematical model utilizing more readily available measures than stimulation tests that identifies brain tumor survivors with high likelihood of abnormal growth hormone secretion after radiotherapy (RT), to avoid late recognition and a consequent delay in growth hormone replacement therapy.
  
  
  METHODS
  We analyzed 191 prospectively collected post-RT evaluations of peak growth hormone level (arginine tolerance/levodopa stimulation test), serum insulin-like growth factor 1 (IGF-1), IGF-binding protein 3, height, weight, growth velocity, and body mass index in 106 children and adolescents treated for ependymoma (n=72), low-grade glioma (n=28) or craniopharyngioma (n=6), who had normal growth hormone levels before RT. Normal level in this study was defined as the peak growth hormone response to the stimulation test≥7 ng/mL.
  
  
  RESULTS
  Independent predictor variables identified by multivariate logistic regression with high statistical significance (p<0.0001) included IGF-1 z score, weight z score, and hypothalamic dose. The developed predictive model demonstrated a strong discriminatory power with an area under the receiver operating characteristic curve of 0.883. At a potential cutoff point of probability of 0.3 the sensitivity was 80% and specificity 78%.
  
  
  CONCLUSIONS
  Without unpleasant and expensive frequent stimulation tests, our model provides a quantitative approach to closely follow the growth hormone secretory capacity of brain tumor survivors. It allows identification of high-risk children for subsequent confirmatory tests and in-depth workup for diagnosis of growth hormone deficiency.
authors.affiliation: Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee 38105-3678, USA. Chia-Ho.Hua@stjude.org
authors.forename: Chiaho
authors.initials: C
authors.lastname: Hua
authors.forename: Shengjie
authors.initials: S
authors.lastname: Wu
authors.forename: Wassim
authors.initials: W
authors.lastname: Chemaitilly
authors.forename: Renin C
authors.initials: RC
authors.lastname: Lukose
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Merchant
mesh_terms: D000293:Adolescent; D001120:Arginine; D015415:Biomarkers / Q000378:metabolism; D001932:Brain Neoplasms / Q000378:metabolism* / Q000532:radiotherapy*; D002648:Child; D002675:Child, Preschool; D003397:Craniopharyngioma / Q000378:metabolism / Q000532:radiotherapy; D004806:Ependymoma / Q000378:metabolism / Q000532:radiotherapy; D005240:Feasibility Studies; D005260:Female; D005910:Glioma / Q000378:metabolism / Q000532:radiotherapy; D019382:Human Growth Hormone / Q000172:deficiency / Q000378:metabolism*; D006801:Humans; D007031:Hypothalamus / Q000528:radiation effects; D007223:Infant; D018972:Insulin-Like Growth Factor Binding Protein 3 / Q000378:metabolism*; D007334:Insulin-Like Growth Factor I / Q000378:metabolism*; D007980:Levodopa; D016015:Logistic Models; D008297:Male; D008954:Models, Biological; D011237:Predictive Value of Tests; D011336:Probability; D011446:Prospective Studies; D012372:ROC Curve; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D018972:Insulin-Like Growth Factor Binding Protein 3; D019382:Human Growth Hormone; D007980:Levodopa; D007334:Insulin-Like Growth Factor I; D001120:Arginine