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Title: Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters

Abstract

Purpose: To determine the clinical, dosimetric, and spatial parameters that correlate with radiation pneumonitis. Methods and Materials: Patients treated with high-dose radiation for non-small-cell lung cancer with three-dimensional treatment planning were reviewed for clinical information and radiation pneumonitis (Rp) events. Three-dimensional treatment plans for 219 eligible patients were recovered. Treatment plan information, including parameters defining tumor position and dose-volume parameters, was extracted from non-heterogeneity-corrected dose distributions. Correlation to RP events was assessed by Spearman's rank correlation coefficient (R). Mathematical models were generated that correlate with RP. Results: Of 219 patients, 52 required treatment for RP (median interval, 142 days). Tumor location was the most highly correlated parameter on univariate analysis (R = 0.24). Multiple dose-volume parameters were correlated with RP. Models most frequently selected by bootstrap resampling included tumor position, maximum dose, and D{sub 35} (minimum dose to the 35% volume receiving the highest doses) (R 0.28). The most frequently selected two- or three-parameter models outperformed commonly used metrics, including V{sub 2} (fractional volume of normal lung receiving >20 Gy) and mean lung dose (R = 0.18). Conclusions: Inferior tumor position was highly correlated with pneumonitis events within our population. Models that account for inferior tumor position and dosimetric information,more » including both high- and low-dose regions (D{sub 35}, International Commission on Radiation Units and Measurements maximum dose), risk-stratify patients more accurately than any single dosimetric or clinical parameter.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [2]
  1. Department of Radiation Oncology, Washington University School of Medicine, Siteman Cancer Center, St. Louis, Missouri (United States)
  2. Department of Radiation Oncology, Washington University School of Medicine, Siteman Cancer Center, St. Louis, Missouri (United States). E-mail: jdeasy@radonc.wustl.edu
Publication Date:
OSTI Identifier:
20793464
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 65; Journal Issue: 1; Other Information: DOI: 10.1016/j.ijrobp.2005.11.046; PII: S0360-3016(05)03075-0; Copyright (c) 2006 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; CARCINOMAS; HEALTH HAZARDS; INFORMATION; LUNGS; METRICS; PATIENTS; PLANNING; PNEUMONITIS; RADIATION DOSE DISTRIBUTIONS; RADIATION DOSES; RADIOTHERAPY; SIMULATION

Citation Formats

Hope, Andrew J., Lindsay, Patricia E., El Naqa, Issam, Alaly, James R., Vicic, Milos, Bradley, Jeffrey D., and Deasy, Joseph O.. Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. United States: N. p., 2006. Web. doi:10.1016/J.IJROBP.2005.1.
Hope, Andrew J., Lindsay, Patricia E., El Naqa, Issam, Alaly, James R., Vicic, Milos, Bradley, Jeffrey D., & Deasy, Joseph O.. Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. United States. doi:10.1016/J.IJROBP.2005.1.
Hope, Andrew J., Lindsay, Patricia E., El Naqa, Issam, Alaly, James R., Vicic, Milos, Bradley, Jeffrey D., and Deasy, Joseph O.. Mon . "Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters". United States. doi:10.1016/J.IJROBP.2005.1.
@article{osti_20793464,
title = {Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters},
author = {Hope, Andrew J. and Lindsay, Patricia E. and El Naqa, Issam and Alaly, James R. and Vicic, Milos and Bradley, Jeffrey D. and Deasy, Joseph O.},
abstractNote = {Purpose: To determine the clinical, dosimetric, and spatial parameters that correlate with radiation pneumonitis. Methods and Materials: Patients treated with high-dose radiation for non-small-cell lung cancer with three-dimensional treatment planning were reviewed for clinical information and radiation pneumonitis (Rp) events. Three-dimensional treatment plans for 219 eligible patients were recovered. Treatment plan information, including parameters defining tumor position and dose-volume parameters, was extracted from non-heterogeneity-corrected dose distributions. Correlation to RP events was assessed by Spearman's rank correlation coefficient (R). Mathematical models were generated that correlate with RP. Results: Of 219 patients, 52 required treatment for RP (median interval, 142 days). Tumor location was the most highly correlated parameter on univariate analysis (R = 0.24). Multiple dose-volume parameters were correlated with RP. Models most frequently selected by bootstrap resampling included tumor position, maximum dose, and D{sub 35} (minimum dose to the 35% volume receiving the highest doses) (R 0.28). The most frequently selected two- or three-parameter models outperformed commonly used metrics, including V{sub 2} (fractional volume of normal lung receiving >20 Gy) and mean lung dose (R = 0.18). Conclusions: Inferior tumor position was highly correlated with pneumonitis events within our population. Models that account for inferior tumor position and dosimetric information, including both high- and low-dose regions (D{sub 35}, International Commission on Radiation Units and Measurements maximum dose), risk-stratify patients more accurately than any single dosimetric or clinical parameter.},
doi = {10.1016/J.IJROBP.2005.1},
journal = {International Journal of Radiation Oncology, Biology and Physics},
number = 1,
volume = 65,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}