Linked Life Data

19962082

Source:http://linkedlifedata.com/resource/pubmed/id/19962082

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-12-7
pubmed:abstractText
It is still an unanswered question whether a relatively low dose of radiation to a large volume or a higher dose to a small volume produces the higher cancer incidence. This is of interest in view of modalities like IMRT or rotation therapy where high conformity to the target volume is achieved at the cost of a large volume of normal tissue exposed to radiation. Knowledge of the shape of the dose response for radiation-induced cancer is essential to answer the question of what risk of second cancer incidence is implied by which treatment modality. This study therefore models the dose response for radiation-induced second cancer after radiation therapy of which the exact mechanisms are still unknown. A second cancer risk estimation tool for treatment planning is presented which has the potential to be used for comparison of different treatment modalities, and risk is estimated on a voxel basis for different organs in two case studies. The presented phenomenological model summarises the impact of microscopic biological processes into effective parameters of mutation and cell sterilisation. In contrast to other models, the effective radiosensitivities of mutated and non-mutated cells are allowed to differ. Based on the number of mutated cells present after irradiation, the model is then linked to macroscopic incidence by summarising model parameters and modifying factors into natural cancer incidence and the dose response in the lower-dose region. It was found that all principal dose-response functions discussed in the literature can be derived from the model. However, from the investigation and due to scarcity of adequate data, rather vague statements about likelihood of dose-response functions can be made than a definite decision for one response. Based on the predicted model parameters, the linear response can probably be rejected using the dynamics described, but both a flattening response and a decrease appear likely, depending strongly on the effective cell sterilisation of the mutated cells. Thus insights could be gained into the impact of parameters describing the effective mutation or cell sterilisation of non-mutated as well as of mutated cells, which constitute precursors of cancer. The biggest drawbacks in the estimation of second cancer incidence remain the low statistical power of clinical studies on radiation induction of cancer and the inability to isolate the effect due to radiation alone - if the latter is possible at all. We conclude that at the present stage of knowledge, further investigations have to be carried out in order to really compare treatment modalities with respect to the second cancer risk they imply.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0939-3889
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
236-50
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Phenomenological modelling of second cancer incidence for radiation treatment planning.
pubmed:affiliation
Abteilung für Medizinische Physik in der Strahlentherapie, Deutsches Krebsforschungszentrum, Heidelberg, Germany. a.pfaffenberger@dkfz.de
pubmed:publicationType
Journal Article