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Title: Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms

Abstract

The aim of this study was to propose a method to predict the minimum achievable mean lung dose (MLD) and corresponding dosimetric parameters for organs-at-risk (OAR) based on individual patient anatomy. For each patient, the dose for 36 equidistant individual multileaf collimator shaped fields in the treatment planning system (TPS) was calculated. Based on these dose matrices, the MLD for each patient was predicted by the homemade DosePredictor software in which the solution of linear equations was implemented. The software prediction results were validated based on 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans previously prepared for 16 patients with stage III non–small-cell lung cancer (NSCLC). For each patient, dosimetric parameters derived from plans and the results calculated by DosePredictor were compared. The MLD, the maximum dose to the spinal cord (D{sub max} {sub cord}) and the mean esophageal dose (MED) were analyzed. There was a strong correlation between the MLD calculated by the DosePredictor and those obtained in treatment plans regardless of the technique used. The correlation coefficient was 0.96 for both 3D-CRT and VMAT techniques. In a similar manner, MED correlations of 0.98 and 0.96 were obtained for 3D-CRT and VMAT plans, respectively. The maximummore » dose to the spinal cord was not predicted very well. The correlation coefficient was 0.30 and 0.61 for 3D-CRT and VMAT, respectively. The presented method allows us to predict the minimum MLD and corresponding dosimetric parameters to OARs without the necessity of plan preparation. The method can serve as a guide during the treatment planning process, for example, as initial constraints in VMAT optimization. It allows the probability of lung pneumonitis to be predicted.« less

Authors:
 [1];  [2];  [3];  [2];  [1]
  1. Medical Physics Department, Centre of Oncology, Maria Sklodowska-Curie Memorial Cancer Center, Warsaw (Poland)
  2. Faculty of Physics, University of Warsaw, Warsaw (Poland)
  3. (Switzerland)
Publication Date:
OSTI Identifier:
22685183
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Dosimetry; Journal Volume: 42; Journal Issue: 1; Other Information: Copyright (c) 2017 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:
61 RADIATION PROTECTION AND DOSIMETRY; 62 RADIOLOGY AND NUCLEAR MEDICINE; ANATOMY; COLLIMATORS; COMPUTER CODES; CORRELATIONS; DOSES; ESOPHAGUS; FORECASTING; HAZARDS; LUNGS; NEOPLASMS; OPTIMIZATION; PATIENTS; PLANNING; PNEUMONITIS; RADIOTHERAPY; SPINAL CORD

Citation Formats

Zawadzka, Anna, E-mail: a.zawadzka@zfm.coi.pl, Nesteruk, Marta, Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Brzozowska, Beata, and Kukołowicz, Paweł F. Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms. United States: N. p., 2017. Web. doi:10.1016/J.MEDDOS.2016.12.001.
Zawadzka, Anna, E-mail: a.zawadzka@zfm.coi.pl, Nesteruk, Marta, Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Brzozowska, Beata, & Kukołowicz, Paweł F. Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms. United States. doi:10.1016/J.MEDDOS.2016.12.001.
Zawadzka, Anna, E-mail: a.zawadzka@zfm.coi.pl, Nesteruk, Marta, Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Brzozowska, Beata, and Kukołowicz, Paweł F. Sat . "Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms". United States. doi:10.1016/J.MEDDOS.2016.12.001.
@article{osti_22685183,
title = {Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms},
author = {Zawadzka, Anna, E-mail: a.zawadzka@zfm.coi.pl and Nesteruk, Marta and Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich and Brzozowska, Beata and Kukołowicz, Paweł F.},
abstractNote = {The aim of this study was to propose a method to predict the minimum achievable mean lung dose (MLD) and corresponding dosimetric parameters for organs-at-risk (OAR) based on individual patient anatomy. For each patient, the dose for 36 equidistant individual multileaf collimator shaped fields in the treatment planning system (TPS) was calculated. Based on these dose matrices, the MLD for each patient was predicted by the homemade DosePredictor software in which the solution of linear equations was implemented. The software prediction results were validated based on 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans previously prepared for 16 patients with stage III non–small-cell lung cancer (NSCLC). For each patient, dosimetric parameters derived from plans and the results calculated by DosePredictor were compared. The MLD, the maximum dose to the spinal cord (D{sub max} {sub cord}) and the mean esophageal dose (MED) were analyzed. There was a strong correlation between the MLD calculated by the DosePredictor and those obtained in treatment plans regardless of the technique used. The correlation coefficient was 0.96 for both 3D-CRT and VMAT techniques. In a similar manner, MED correlations of 0.98 and 0.96 were obtained for 3D-CRT and VMAT plans, respectively. The maximum dose to the spinal cord was not predicted very well. The correlation coefficient was 0.30 and 0.61 for 3D-CRT and VMAT, respectively. The presented method allows us to predict the minimum MLD and corresponding dosimetric parameters to OARs without the necessity of plan preparation. The method can serve as a guide during the treatment planning process, for example, as initial constraints in VMAT optimization. It allows the probability of lung pneumonitis to be predicted.},
doi = {10.1016/J.MEDDOS.2016.12.001},
journal = {Medical Dosimetry},
number = 1,
volume = 42,
place = {United States},
year = {Sat Apr 01 00:00:00 EDT 2017},
month = {Sat Apr 01 00:00:00 EDT 2017}
}