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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}
}