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Title: Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors

Abstract

Purpose: 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 and Materials: 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 providesmore » 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.« less

Authors:
 [1];  [2]; ;  [3]
  1. Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee (United States)
  2. Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, Tennessee (United States)
  3. Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee (United States)
Publication Date:
OSTI Identifier:
22149642
Resource Type:
Journal Article
Journal Name:
International Journal of Radiation Oncology, Biology and Physics
Additional Journal Information:
Journal Volume: 84; Journal Issue: 4; Other Information: Copyright (c) 2012 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0360-3016
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ARGININE; BRAIN; CHILDREN; DIAGNOSIS; EVALUATION; GLIOMAS; GROWTH FACTORS; HEALTH HAZARDS; INSULIN; MATHEMATICAL MODELS; MULTIVARIATE ANALYSIS; PEDIATRICS; PROBABILITY; RADIATION DOSES; RADIOTHERAPY; SECRETION; SENSITIVITY; SPECIFICITY; STH; STIMULATION

Citation Formats

Hua Chiaho, E-mail: Chia-Ho.Hua@stjude.org, Shengjie, Wu, Chemaitilly, Wassim, Lukose, Renin C., and Merchant, Thomas E. Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors. United States: N. p., 2012. Web. doi:10.1016/J.IJROBP.2012.01.049.
Hua Chiaho, E-mail: Chia-Ho.Hua@stjude.org, Shengjie, Wu, Chemaitilly, Wassim, Lukose, Renin C., & Merchant, Thomas E. Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors. United States. https://doi.org/10.1016/J.IJROBP.2012.01.049
Hua Chiaho, E-mail: Chia-Ho.Hua@stjude.org, Shengjie, Wu, Chemaitilly, Wassim, Lukose, Renin C., and Merchant, Thomas E. 2012. "Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors". United States. https://doi.org/10.1016/J.IJROBP.2012.01.049.
@article{osti_22149642,
title = {Predicting the Probability of Abnormal Stimulated Growth Hormone Response in Children After Radiotherapy for Brain Tumors},
author = {Hua Chiaho, E-mail: Chia-Ho.Hua@stjude.org and Shengjie, Wu and Chemaitilly, Wassim and Lukose, Renin C. and Merchant, Thomas E.},
abstractNote = {Purpose: 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 and Materials: 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.},
doi = {10.1016/J.IJROBP.2012.01.049},
url = {https://www.osti.gov/biblio/22149642}, journal = {International Journal of Radiation Oncology, Biology and Physics},
issn = {0360-3016},
number = 4,
volume = 84,
place = {United States},
year = {Thu Nov 15 00:00:00 EST 2012},
month = {Thu Nov 15 00:00:00 EST 2012}
}