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Optimal number and sizes of the doses in fractionated radiotherapy according to the LQ model

Articolo
Data di Pubblicazione:
2019
Abstract:
We address a non-linear programming problem to find the optimal scheme of dose fractionation in cancer radiotherapy. Using the LQ model to represent the response to radiation of tumour and normal tissues, we formulate a constrained non-linear optimization problem in terms of the variables number and sizes of the dose fractions. Quadratic constraints are imposed to guarantee that the damages to the early and late responding normal tissues do not exceed assigned tolerable levels. Linear constraints are set to limit the size of the daily doses. The optimal solutions are found in two steps: i) analytical determination of the optimal sizes of the fractional doses for a fixed, but arbitrary number of fractions n; ii) numerical simulation of a sequence of the previous optima for n increasing, and for specific tumour classes. We prove the existence of a finite upper bound for the optimal number of fractions. So, the optimum with respect to n is found by means of a finite number of comparisons amongst the optimal values of the objective function at the first step. In the numerical simulations, the radiosensitivity and repopulation parameters of the normal tissue are fixed, while we investigate the behaviour of the optimal solution for wide variations of the tumour parameters, relating our optima to real clinical protocols. We recognize that the optimality of hypo or equi-fractionated treatment schemes depends on the value of the tumour radiosensitivity ratio compared to the normal tissue radiosensitivity. Fast growing, radioresistant tumours may require particularly short optimal treatments.
Tipologia CRIS:
01.01 Articolo in rivista
Keywords:
non-linear programming; linear-quadratic LQ model; cancer radiotherapy
Elenco autori:
Conte, Federica; Sinisgalli, Carmela; Papa, Federico
Autori di Ateneo:
CONTE FEDERICA
PAPA FEDERICO
SINISGALLI CARMELA
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/354650
Pubblicato in:
MATHEMATICAL MEDICINE AND BIOLOGY
Journal
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