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Title: TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies

Abstract

Purpose: To set up a framework combining robust treatment planning with adaptive reoptimization in order to maintain high treatment quality, to respond to interfractional variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods: We propose adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan should be able to handle anticipated systematic and random errors and is applied during the first fractions. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient that receives a dose that does not satisfy specified plan quality criteria, the plan is reoptimized based on these individual measurements using one of three different adaptive strategies. The reoptimized plan is then applied during future fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. The focus of the second strategy lies on variation of the fraction of the worst scenarios taken into account during robust reoptimization. Inmore » the third strategy the uncertainty margins around the target are recalculated with the measured errors. Results: By studying the effect of the three adaptive strategies combined with various adaptation schedules on the same patient population, the group which benefits from adaptation is identified together with the most suitable strategy and schedule. Preliminary computational results indicate when and how best to adapt for the three different strategies. Conclusion: A workflow is presented that provides robust adaptation of the treatment plan throughout the course of treatment and useful measures to identify patients in need for an adaptive treatment strategy.« less

Authors:
 [1];  [2]; ;  [1];  [3]
  1. RaySearch Laboratories AB, Stockholm (Sweden)
  2. (Sweden)
  3. KTH Royal Institute of Technology, Stockholm (Sweden)
Publication Date:
OSTI Identifier:
22654073
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ERRORS; FRACTIONATION; PATIENTS; PLANNING; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY; STOCHASTIC PROCESSES

Citation Formats

Boeck, M, KTH Royal Institute of Technology, Stockholm, Eriksson, K, Hardemark, B, and Forsgren, A. TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies. United States: N. p., 2016. Web. doi:10.1118/1.4957699.
Boeck, M, KTH Royal Institute of Technology, Stockholm, Eriksson, K, Hardemark, B, & Forsgren, A. TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies. United States. doi:10.1118/1.4957699.
Boeck, M, KTH Royal Institute of Technology, Stockholm, Eriksson, K, Hardemark, B, and Forsgren, A. Wed . "TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies". United States. doi:10.1118/1.4957699.
@article{osti_22654073,
title = {TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies},
author = {Boeck, M and KTH Royal Institute of Technology, Stockholm and Eriksson, K and Hardemark, B and Forsgren, A},
abstractNote = {Purpose: To set up a framework combining robust treatment planning with adaptive reoptimization in order to maintain high treatment quality, to respond to interfractional variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods: We propose adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan should be able to handle anticipated systematic and random errors and is applied during the first fractions. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient that receives a dose that does not satisfy specified plan quality criteria, the plan is reoptimized based on these individual measurements using one of three different adaptive strategies. The reoptimized plan is then applied during future fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. The focus of the second strategy lies on variation of the fraction of the worst scenarios taken into account during robust reoptimization. In the third strategy the uncertainty margins around the target are recalculated with the measured errors. Results: By studying the effect of the three adaptive strategies combined with various adaptation schedules on the same patient population, the group which benefits from adaptation is identified together with the most suitable strategy and schedule. Preliminary computational results indicate when and how best to adapt for the three different strategies. Conclusion: A workflow is presented that provides robust adaptation of the treatment plan throughout the course of treatment and useful measures to identify patients in need for an adaptive treatment strategy.},
doi = {10.1118/1.4957699},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: With introduction of high-quality treatment imaging during radiation therapy (RT) delivery, e.g., MR-Linac, adaptive replanning of either online or offline becomes appealing. Dose accumulation of delivered fractions, a prerequisite for the adaptive replanning, can be cumbersome and inaccurate. The purpose of this work is to develop an automated process to accumulate daily doses and to assess the dose accumulation accuracy voxel-by-voxel for adaptive replanning. Methods: The process includes the following main steps: 1) reconstructing daily dose for each delivered fraction with a treatment planning system (Monaco, Elekta) based on the daily images using machine delivery log file and consideringmore » patient repositioning if applicable, 2) overlaying the daily dose to the planning image based on deformable image registering (DIR) (ADMIRE, Elekta), 3) assessing voxel dose deformation accuracy based on deformation field using predetermined criteria, and 4) outputting accumulated dose and dose-accuracy volume histograms and parameters. Daily CTs acquired using a CT-on-rails during routine CT-guided RT for sample patients with head and neck and prostate cancers were used to test the process. Results: Daily and accumulated doses (dose-volume histograms, etc) along with their accuracies (dose-accuracy volume histogram) can be robustly generated using the proposed process. The test data for a head and neck cancer case shows that the gross tumor volume decreased by 20% towards the end of treatment course, and the parotid gland mean dose increased by 10%. Such information would trigger adaptive replanning for the subsequent fractions. The voxel-based accuracy in the accumulated dose showed that errors in accumulated dose near rigid structures were small. Conclusion: A procedure as well as necessary tools to automatically accumulate daily dose and assess dose accumulation accuracy is developed and is useful for adaptive replanning. Partially supported by Elekta, Inc.« less
  • Purpose: To develop a new method which adaptively determines the optimal needle insertion sequence for HDR prostate brachytherapy involving divergent needle-by-needle dose delivery by e.g. a robotic device. A needle insertion sequence is calculated at the beginning of the intervention and updated after each needle insertion with feedback on needle positioning errors. Methods: Needle positioning errors and anatomy changes may occur during HDR brachytherapy which can lead to errors in the delivered dose. A novel strategy was developed to calculate and update the needle sequence and the dose plan after each needle insertion with feedback on needle positioning errors. Themore » dose plan optimization was performed by numerical simulations. The proposed needle sequence determination optimizes the final dose distribution based on the dose coverage impact of each needle. This impact is predicted stochastically by needle insertion simulations. HDR procedures were simulated with varying number of needle insertions (4 to 12) using 11 patient MR data-sets with PTV, prostate, urethra, bladder and rectum delineated. Needle positioning errors were modeled by random normally distributed angulation errors (standard deviation of 3 mm at the needle’s tip). The final dose parameters were compared in the situations where the needle with the largest vs. the smallest dose coverage impact was selected at each insertion. Results: Over all scenarios, the percentage of clinically acceptable final dose distribution improved when the needle selected had the largest dose coverage impact (91%) compared to the smallest (88%). The differences were larger for few (4 to 6) needle insertions (maximum difference scenario: 79% vs. 60%). The computation time of the needle sequence optimization was below 60s. Conclusion: A new adaptive needle sequence determination for HDR prostate brachytherapy was developed. Coupled to adaptive planning, the selection of the needle with the largest dose coverage impact increases chances of reaching the clinical constraints. M. Borot de Battisti is funded by Philips Medical Systems Nederland B.V.; M. Moerland is principal investigator on a contract funded by Philips Medical Systems Nederland B.V.; G. Hautvast and D. Binnekamp are fulltime employees of Philips Medical Systems Nederland B.V.« less
  • Purpose: To measure the increase in in vitro radiosensitivity for A549 lung carcinoma cells due to gold nanoparticle (GNP) radiation dose enhancement in both traditional monolayer and three dimensional (3D) cell culture models. Methods: A γH2AX immunofluorescence assay is performed on monolayer A549 cell culture and quantitatively analyzed to measure the increase in double strand breaks (DSBs) resulting from GNP dose enhancement. A clonogenic survival assay (CSA) is then performed on monolayer A549 cell culture to assess true viability after treatment. And lastly, another γH2AX assay is performed on 3D A549 multicellular nodules overlaid on a bed of growth factormore » reduced matrigel to measure dose response in a model that better recapitulates treatment response to actual tumors in vivo. Results: The first γH2AX assay performed on the monolayer cell culture shows a significant increase in DSBs due to GNP dose enhancement. The maximum average observed increase in normalized fluorescent intensity for monolayer cell culture is 171% for the 6Gy-treatment groups incubated in 0.556 mg Au/ml solution. The CSA performed on monolayer cell culture also shows considerable GNP dose enhancement. The maximum decrease in the normalized surviving fraction is 12% for the 4Gy-treatment group incubated in 0.556 mg Au/ml. And lastly, the GNP dose enhancement is confirmed to be mitigated in three dimensional cell culture models as compared to the traditional monolayer model. The maximum average observed dose enhancement for 3D cell culture is 19% for the 6Gy-treatment groups and incubated in 0.556 mg Au/ml. Conclusion: A marked increase in radiosensitivity is observed for A549 lung carcinoma cells when treated with GNPs plus radiation as opposed to radiation alone. Traditional monolayer cell culture also shows a much more pronounced radiation dose enhancement than 3D cell culture.« less
  • Purpose: Proton dose distribution is sensitive to tumor regression and tissue and normal anatomy changes. Replanning is sometimes necessary during treatment to ensure continue tumor coverage or avoid overtreatment of organs at risk (OARs). We investigated action thresholds for replanning and identified both dosimetric and non-dosimetric metrics that would predict a need for replan. Methods: All consecutive lung cancer patients (n = 188) who received definitive proton radiotherapy and had more than two evaluation CT scans at the Roberts Proton Therapy Center (Philadelphia, USA) from 2011 to 2015 were included in this study. The cohort included a variety of tumormore » sizes, locations, histology, beam angles, as well as radiation-induced tumor and lung change. Dosimetric changes during therapy were characterized by changes in the dose volume distribution of PTV, ITV, and OARs (heart, cord, esophagus, brachial plexus and lungs). Tumor and lung change were characterized by changes in sizes, and in the distribution of Hounsfield numbers and water equivalent thickness (WET) along the beam path. We applied machine learning tools to identify both dosimetric and non-dosimetric metrics that predicted a replan. Results: Preliminary data showed that clinical indicators (n = 54) were highly correlated; thus, a simple indicator may be derived to guide the action threshold for replanning. Additionally, tumor regression alone could not predict dosimetric changes in OARs; it required further information about beam angles and tumor locations. Conclusion: Both dosimetric and non-dosimetric factors are predictive of the need for replanning during proton treatment.« less
  • Purpose: To evaluate the use of post-irradiation changes in respiratory rate and CBCT-based morphology as predictors of survival in mice. Methods: C57L/J mice underwent whole-thorax irradiation with a Co-60 beam to four different doses [0Gy (n=3), 9Gy (n=5), 11Gy (n=7), and 13Gy (n=5)] in order to induce varying levels of pneumonitis. Respiratory rate measurements, breath-hold CBCTs, and free-breathing CBCTs were acquired pre-irradiation and at six time points between two and seven months post-irradiation. For respiratory rate measurements, we developed a novel computer-vision-based technique. We recorded mice sleeping in standard laboratory cages with a 30 fps, 1080p webcam (Logitech C920). Wemore » calculated respiratory rate using corner detection and optical flow to track cyclical motion in the fur in the recorded video. Breath-hold and free-breathing CBCTs were acquired on the X-RAD225Cx system. For breathhold imaging, the mice were intubated and their breath was held at full-inhale for 20 seconds. Healthy lung tissue was delineated in the scans using auto-threshold contouring (0–0.7 g/cm{sup 3}). The volume of healthy lung was measured in each of the scans. Next, lung density was measured in a 6-mm{sup 2} ROI in a fixed anatomic location in each of the scans. Results: Day-to-day variability in respiratory rate with our technique was 13%. All metrics except for breath-hold lung volume were correlated with survival: lung density on free-breathing (r=−0.7482,p<0.01) and breath-hold images (r=−0.5864,p<0.01), free-breathing lung volume (r=0.7179,p<0.01), and respiratory rate (r= 0.6953,p<0.01). Lung density on free-breathing scans was correlated with respiratory rate (r=0.7142,p<0.01) and lung density on breath-hold scans (r=0.5543,p<0.01). One significant practical hurdle in the CBCT measurements was that at least one lobe of the lung was collapsed in 36% of free-breathing scans and 45% of breath-hold scans. Conclusion: Lung density and lung volume on free-breathing CBCTs and respiratory rate outperform breath-hold CBCT measurements as indicators for survival from radiation-induced pneumonitis. This work was partially funded by Elekta.« less