Literature DB >> 3804804

Optimization of radiation therapy, III: A method of assessing complication probabilities from dose-volume histograms.

J T Lyman, A B Wolbarst.   

Abstract

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 contains 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.

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Year:  1987        PMID: 3804804     DOI: 10.1016/0360-3016(87)90266-5

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  32 in total

Review 1.  Uses of 3D planning in addition to creating a good treatment: ongoing studies at MGH/HCL.

Authors:  M M Urie
Journal:  Radiat Environ Biophys       Date:  1992       Impact factor: 1.925

2.  Incidence of late rectal bleeding in high-dose conformal radiotherapy of prostate cancer using equivalent uniform dose-based and dose-volume-based normal tissue complication probability models.

Authors:  Matthias Söhn; Di Yan; Jian Liang; Elisa Meldolesi; Carlos Vargas; Markus Alber
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-01-26       Impact factor: 7.038

3.  Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Authors:  N L Jain; M G Kahn
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1991

4.  Dosimetric comparison of free-breathing and deep inspiration breath-hold radiotherapy for lung cancer.

Authors:  V Marchand; S Zefkili; J Desrousseaux; L Simon; C Dauphinot; P Giraud
Journal:  Strahlenther Onkol       Date:  2012-05-17       Impact factor: 3.621

5.  Objective assessment of image quality VI: imaging in radiation therapy.

Authors:  Harrison H Barrett; Matthew A Kupinski; Stefan Müeller; Howard J Halpern; John C Morris; Roisin Dwyer
Journal:  Phys Med Biol       Date:  2013-11-21       Impact factor: 3.609

6.  [3-Dimensional irradiation planning in brain tumors. The advantages of the method and the clinical results].

Authors:  A L Grosu; H J Feldmann; C Albrecht; P Kneschaurek; R Wehrmann; M W Gross; F B Zimmermann; M Molls
Journal:  Strahlenther Onkol       Date:  1998-01       Impact factor: 3.621

7.  Investigation of bladder dose and volume factors influencing late urinary toxicity after external beam radiotherapy for prostate cancer.

Authors:  M Rex Cheung; Susan L Tucker; Lei Dong; Renaud de Crevoisier; Andrew K Lee; Steven Frank; Rajat J Kudchadker; Howard Thames; Radhe Mohan; Deborah Kuban
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-01-22       Impact factor: 7.038

8.  Radiotherapy of large target volumes in Hodgkin's lymphoma: normal tissue sparing capability of forward IMRT versus conventional techniques.

Authors:  Laura Cella; Raffaele Liuzzi; Mario Magliulo; Manuel Conson; Luigi Camera; Marco Salvatore; Roberto Pacelli
Journal:  Radiat Oncol       Date:  2010-05-11       Impact factor: 3.481

9.  A computational tool for the efficient analysis of dose-volume histograms from radiation therapy treatment plans.

Authors:  Anil Pyakuryal; W Kenji Myint; Mahesh Gopalakrishnan; Sunyoung Jang; Jerilyn A Logemann; Bharat B Mittal
Journal:  J Appl Clin Med Phys       Date:  2010-01-28       Impact factor: 2.102

10.  A neural network model to predict lung radiation-induced pneumonitis.

Authors:  Shifeng Chen; Sumin Zhou; Junan Zhang; Fang-Fang Yin; Lawrence B Marks; Shiva K Das
Journal:  Med Phys       Date:  2007-09       Impact factor: 4.071

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