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Selecting Radiotherapy Dose Distributions by Means of Constrained Optimization Problems

Overview of attention for article published in Bulletin of Mathematical Biology, March 2014
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Title
Selecting Radiotherapy Dose Distributions by Means of Constrained Optimization Problems
Published in
Bulletin of Mathematical Biology, March 2014
DOI 10.1007/s11538-014-9945-7
Pubmed ID
Authors

J. C. L. Alfonso, G. Buttazzo, B. García-Archilla, M. A. Herrero, L. Núñez

Abstract

The main steps in planning radiotherapy consist in selecting for any patient diagnosed with a solid tumor (i) a prescribed radiation dose on the tumor, (ii) bounds on the radiation side effects on nearby organs at risk and (iii) a fractionation scheme specifying the number and frequency of therapeutic sessions during treatment. The goal of any radiotherapy treatment is to deliver on the tumor a radiation dose as close as possible to that selected in (i), while at the same time conforming to the constraints prescribed in (ii). To this day, considerable uncertainties remain concerning the best manner in which such issues should be addressed. In particular, the choice of a prescription radiation dose is mostly based on clinical experience accumulated on the particular type of tumor considered, without any direct reference to quantitative radiobiological assessment. Interestingly, mathematical models for the effect of radiation on biological matter have existed for quite some time, and are widely acknowledged by clinicians. However, the difficulty to obtain accurate in vivo measurements of the radiobiological parameters involved has severely restricted their direct application in current clinical practice.In this work, we first propose a mathematical model to select radiation dose distributions as solutions (minimizers) of suitable variational problems, under the assumption that key radiobiological parameters for tumors and organs at risk involved are known. Second, by analyzing the dependence of such solutions on the parameters involved, we then discuss the manner in which the use of those minimizers can improve current decision-making processes to select clinical dosimetries when (as is generally the case) only partial information on model radiosensitivity parameters is available. A comparison of the proposed radiation dose distributions with those actually delivered in a number of clinical cases strongly suggests that solutions of our mathematical model can be instrumental in deriving good quality tests to select radiotherapy treatment plans in rather general situations.

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Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 32%
Student > Ph. D. Student 4 21%
Student > Master 3 16%
Professor > Associate Professor 2 11%
Student > Doctoral Student 1 5%
Other 1 5%
Unknown 2 11%
Readers by discipline Count As %
Mathematics 6 32%
Agricultural and Biological Sciences 2 11%
Medicine and Dentistry 2 11%
Engineering 2 11%
Physics and Astronomy 1 5%
Other 2 11%
Unknown 4 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2016.
All research outputs
#18,366,246
of 22,747,498 outputs
Outputs from Bulletin of Mathematical Biology
#881
of 1,093 outputs
Outputs of similar age
#160,968
of 221,372 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#5
of 7 outputs
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