Literature DB >> 10196397

Optimized radiation therapy based on radiobiological objectives.

A Brahme1.   

Abstract

In the broad field of radiation therapy optimization, both simple and complex problems have their origins in the interaction of the radiation beams with the biological structures of normal and malignant tissues of the human body. Therefore, it is no great surprise that many treatment optimization problems are best handled by the use of well-designed radiobiological models. The classic way of quantifying dose-response relations for tumors and normal tissues as well as their cross-correlation with each other and their dependence on the underlying genetic and molecular biology of the cell are first briefly reviewed. Radiobiological objective functions, such as the probability of achieving complication-free cure and its expectation value under influence of stochastic processes during the course of treatment, are defined and shown to solve many of the problems of radiation therapy planning. Finally, it is shown through the use of these quantifiers that, simply by introducing biologically optimal intensity modulated dose delivery, the treatment outcome can be improved by about 20% or more in cases with a complex spread of the disease. Once radiobiological optimal plans have been developed, they can be approximated by ordinary physical planning, but the biological objective functions are still needed to have a figure of merit for the quality of the treatment.

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Year:  1999        PMID: 10196397     DOI: 10.1016/s1053-4296(99)80053-8

Source DB:  PubMed          Journal:  Semin Radiat Oncol        ISSN: 1053-4296            Impact factor:   5.934


  10 in total

1.  On Voxel based Iso-Tumor Control Probabilty and Iso-Complication Maps for Selective Boosting and Selective Avoidance Intensity Modulated Radiotherapy.

Authors:  Yusung Kim; Wolfgang A Tomé
Journal:  Imaging Decis (Berl)       Date:  2008

2.  Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes.

Authors:  Yusung Kim; Wolfgang A Tomé
Journal:  Int J Radiat Oncol Biol Phys       Date:  2006-12-01       Impact factor: 7.038

3.  On the impact of functional imaging accuracy on selective boosting IMRT.

Authors:  Y Kim; W A Tomé
Journal:  Phys Med       Date:  2008-01-18       Impact factor: 2.685

4.  Is it beneficial to selectively boost high-risk tumor subvolumes? A comparison of selectively boosting high-risk tumor subvolumes versus homogeneous dose escalation of the entire tumor based on equivalent EUD plans.

Authors:  Yusung Kim; Wolfgang A Tome
Journal:  Acta Oncol       Date:  2008       Impact factor: 4.089

5.  Datamining approaches for modeling tumor control probability.

Authors:  Issam El Naqa; Joseph O Deasy; Yi Mu; Ellen Huang; Andrew J Hope; Patricia E Lindsay; Aditya Apte; James Alaly; Jeffrey D Bradley
Journal:  Acta Oncol       Date:  2010-03-02       Impact factor: 4.089

Review 6.  A systems biology approach to radiation therapy optimization.

Authors:  Anders Brahme; Bengt K Lind
Journal:  Radiat Environ Biophys       Date:  2010-02-27       Impact factor: 1.925

7.  Risk-optimized proton therapy to minimize radiogenic second cancers.

Authors:  Laura A Rechner; John G Eley; Rebecca M Howell; Rui Zhang; Dragan Mirkovic; Wayne D Newhauser
Journal:  Phys Med Biol       Date:  2015-04-28       Impact factor: 3.609

8.  Predicting radiotherapy outcomes using statistical learning techniques.

Authors:  Issam El Naqa; Jeffrey D Bradley; Patricia E Lindsay; Andrew J Hope; Joseph O Deasy
Journal:  Phys Med Biol       Date:  2009-08-18       Impact factor: 3.609

9.  Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters.

Authors:  Antonella Bufacchi; Barbara Nardiello; Roberto Capparella; Luisa Begnozzi
Journal:  Radiat Oncol       Date:  2013-07-04       Impact factor: 3.481

Review 10.  Intensity-modulated radiation therapy: a review with a physics perspective.

Authors:  Byungchul Cho
Journal:  Radiat Oncol J       Date:  2018-03-30
  10 in total

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