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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}
}
  • Purpose: The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. Methods and Materials: PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as 'eigenmodes,' which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). Themore » correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Results: Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe {approx}94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses ({approx}40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. Conclusions: PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches.« less
  • Purpose: Radiotherapy of solid tumors has been performed with various fractionation regimens such as multi- and hypofractionations. However, the ability to optimize the fractionation regimen considering the physical dose distribution remains insufficient. This study aims to optimize the fractionation regimen, in which the authors propose a graphical method for selecting the optimal number of fractions (n) and dose per fraction (d) based on dose–volume histograms for tumor and normal tissues of organs around the tumor. Methods: Modified linear-quadratic models were employed to estimate the radiation effects on the tumor and an organ at risk (OAR), where the repopulation of themore » tumor cells and the linearity of the dose-response curve in the high dose range of the surviving fraction were considered. The minimization problem for the damage effect on the OAR was solved under the constraint that the radiation effect on the tumor is fixed by a graphical method. Here, the damage effect on the OAR was estimated based on the dose–volume histogram. Results: It was found that the optimization of fractionation scheme incorporating the dose–volume histogram is possible by employing appropriate cell surviving models. The graphical method considering the repopulation of tumor cells and a rectilinear response in the high dose range enables them to derive the optimal number of fractions and dose per fraction. For example, in the treatment of prostate cancer, the optimal fractionation was suggested to lie in the range of 8–32 fractions with a daily dose of 2.2–6.3 Gy. Conclusions: It is possible to optimize the number of fractions and dose per fraction based on the physical dose distribution (i.e., dose–volume histogram) by the graphical method considering the effects on tumor and OARs around the tumor. This method may stipulate a new guideline to optimize the fractionation regimen for physics-guided fractionation.« less
  • Purpose: RapidPlan uses a library consisting of expert plans from different patients to create a model that can predict achievable dose-volume histograms (DVHs) for new patients. The goal of this study is to investigate the impacts of model library population (plan numbers) on the DVH prediction for rectal cancer patients treated with volumetric-modulated radiotherapy (VMAT) Methods: Ninety clinically accepted rectal cancer patients’ VMAT plans were selected to establish 3 models, named as Model30, Model60 and Model90, with 30,60, and 90 plans in the model training. All plans had sufficient target coverage and bladder and femora sparings. Additional 10 patients weremore » enrolled to test the DVH prediction differences with these 3 models. The predicted DVHs from these 3 models were compared and analyzed. Results: Predicted V40 (Vx, percent of volume that received x Gy for the organs at risk) and Dmean (mean dose, cGy) of the bladder were 39.84±13.38 and 2029.4±141.6 for the Model30,37.52±16.00 and 2012.5±152.2 for the Model60, and 36.33±18.35 and 2066.5±174.3 for the Model90. Predicted V30 and Dmean of the left femur were 23.33±9.96 and 1443.3±114.5 for the Model30, 21.83±5.75 and 1436.6±61.9 for the Model60, and 20.31±4.6 and 1415.0±52.4 for the Model90.There were no significant differences among the 3 models for the bladder and left femur predictions. Predicted V40 and Dmean of the right femur were 19.86±10.00 and 1403.6±115.6 (Model30),18.97±6.19 and 1401.9±68.78 (Model60), and 21.08±7.82 and 1424.0±85.3 (Model90). Although a slight lower DVH prediction of the right femur was found on the Model60, the mean differences for V30 and mean dose were less than 2% and 1%, respectively. Conclusion: There were no significant differences among Model30, Model60 and Model90 for predicting DVHs on rectal patients treated with VMAT. The impact of plan numbers for model library might be limited for cancers with similar target shape.« less
  • To predict the likelihood of success of a therapeutic strategy, one must be able to assess the effects of the treatment upon both diseased and healthy tissues. This paper proposes a method for determining the probability that a healthy organ that receives a non-uniform distribution of X-irradiation, heat, chemotherapy, or other agent will escape complications. Starting with any given dose distribution, a dose-cumulative-volume histogram for the organ is generated. This is then reduced by an interpolation scheme (involving the volume-weighting of complication probabilities) to a slightly different histogram that corresponds to the same overall likelihood of complications, but which containsmore » one less step. The procedure is repeated, one step at a time, until there remains a final, single-step histogram, for which the complication probability can be determined. The formalism makes use of a complication response function C(D, V) which, for the given treatment schedule, represents the probability of complications arising when the fraction V of the organ receives dose D and the rest of the organ gets none. Although the data required to generate this function are sparse at present, it should be possible to obtain the necessary information from in vivo and clinical studies. Volume effects are taken explicitly into account in two ways: the precise shape of the patient's histogram is employed in the calculation, and the complication response function is a function of the volume.« less
  • Purpose: A method is introduced to examine the influence of implant duration T, radionuclide, and radiobiological parameters on the biologically effective dose (BED) throughout the entire volume of regions of interest for episcleral brachytherapy using available radionuclides. This method is employed to evaluate a particular eye plaque brachytherapy implant in a radiobiological context. Methods: A reference eye geometry and 16 mm COMS eye plaque loaded with {sup 103}Pd, {sup 125}I, or {sup 131}Cs sources were examined with dose distributions accounting for plaque heterogeneities. For a standardized 7 day implant, doses to 90% of the tumor volume ( {sub TUMOR}D{sub 90})more » and 10% of the organ at risk volumes ( {sub OAR}D{sub 10}) were calculated. The BED equation from Dale and Jones and published {alpha}/{beta} and {mu} parameters were incorporated with dose volume histograms (DVHs) for various T values such as T = 7 days (i.e., {sub TUMOR} {sup 7}BED{sub 10} and {sub OAR} {sup 7}BED{sub 10}). By calculating BED throughout the volumes, biologically effective dose volume histograms (BEDVHs) were developed for tumor and OARs. Influence of T, radionuclide choice, and radiobiological parameters on {sub TUMOR}BEDVH and {sub OAR}BEDVH were examined. The nominal dose was scaled for shorter implants to achieve biological equivalence. Results: {sub TUMOR}D{sub 90} values were 102, 112, and 110 Gy for {sup 103}Pd, {sup 125}I, and {sup 131}Cs, respectively. Corresponding {sub TUMOR} {sup 7}BED{sub 10} values were 124, 140, and 138 Gy, respectively. As T decreased from 7 to 0.01 days, the isobiologically effective prescription dose decreased by a factor of three. As expected, {sub TUMOR} {sup 7}BEDVH did not significantly change as a function of radionuclide half-life but varied by 10% due to radionuclide dose distribution. Variations in reported radiobiological parameters caused {sub TUMOR} {sup 7}BED{sub 10} to deviate by up to 46%. Over the range of {sub OAR}{alpha}/{beta} values, {sub OAR} {sup 7}BED{sub 10} varied by up to 41%, 3.1%, and 1.4% for the lens, optic nerve, and lacrimal gland, respectively. Conclusions: BEDVH permits evaluation of the relative biological effectiveness for brachytherapy implants. For eye plaques, {sub TUMOR}BEDVH and {sub OAR}BEDVH were sensitive to implant duration, which may be manipulated to affect outcomes.« less