title: Treatment planning optimisation based on imaging tumour proliferation and cell density.
abstract: Functional imaging could provide valuable information on the distribution of biological factors that influence the outcome of radiation therapy. Tumour proliferation and cell density in particular could be imaged with dedicated metabolic tracers and could thus be used for the biological optimisation of the treatment plans. The feasibility of individualising treatment planning using proliferation and density information has been investigated through simulations of heterogeneous tumours taking into account the cell density and proliferation rates. The predicted outcome was used to estimate the success of the individualisation of dose distributions. The results have shown that tumour control could be increased through the escalation of doses to proliferating foci with a relative reduction of doses to slowly proliferating regions of the tumour. This suggests that individualisation of treatment planning taking into account proliferation information creates the premises for further reduction of the doses to the surrounding regions which would consequently lead to an increased sparing of the normal tissues. Cell density has been shown to be another important factor that could be used for optimisation, albeit of a lower weight than proliferation. However, associated with proliferation it could lead to treatment failure if the trouble foci are underdosed. In conclusion, treatment optimisation based on imaged proliferation could improve both tumour control and normal tissue sparing.
authors.affiliation: Department of Radiation Physics, Norrlands University Hospital, 901 85 Umeå, Sweden. alexandru.dasu@radfys.umu.se
authors.forename: Alexandru
authors.initials: A
authors.lastname: Daşu
mesh_terms: D000465:Algorithms; D002452:Cell Count; D049109:Cell Proliferation; D008962:Models, Theoretical; D009369:Neoplasms / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*