Optimization of Radiation Therapy Fractionation Schedules in the Presence of Tumor Repopulation

Bortfeld, Thomas; Ramakrishnan, Jayapradha; Tsitsiklis, John N.; Unkelbach, Jan

doi:10.1287/ijoc.2015.0659

Cited by 35 publications

(45 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6 Bortfeld et al studied the effect of accelerated tumor repopulation on the optimal fractionation schedule with a modification to the basic model. 7 The conclusions from the previous studies were similar in essence: the analytical solution to the basic problem and the related variations depends on the difference in (i) the α/ β ratios of the tumor and OAR and (ii) the relative dose received by them (s eff ). More specifically, if the α/ β ratio of the tumor is equal to or less than the α/ β ratio of OAR divided by the (effective) sparing factor, then a single fraction is optimal.…”

Section: Introductionmentioning

confidence: 75%

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

2015

View full text Add to dashboard Cite

Spatiotemporal optimization of patient plans has the potential to significantly improve local tumor control (larger BED/EUD) of patients with a favorable geometry, such as smaller tumors with larger distances between the tumor target and nearby OAR. In patients with a less favorable geometry and for fast growing tumors, plans optimized using spatiotemporal optimization and conventional (spatial-only) optimization are equivalent (negligible differences in tumor BED/EUD). However, spatiotemporal optimization yields shorter treatment courses than conventional spatial-only optimization. Personalized, spatiotemporal optimization of treatment schedules can increase patient convenience and help with the efficient allocation of clinical resources. Spatiotemporal optimization can also help identify a subset of patients that might benefit from nonconventional (large dose per fraction) treatments that are ineligible for the current practice of stereotactic body radiation therapy.

show abstract

Section: Introductionmentioning

confidence: 75%

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

2015

View full text Add to dashboard Cite

show abstract

“…It is common in the literature, however, to ignore one or both of these effects when performing modeling studies that compare or optimize different fractionation schedules. [1][2][3][4]9,12 Brenner et al 13 proposed a formula for including reoxygenation/ resensitization in the LQ model assuming a distribution of values of a, but the model was designed to describe surviving fraction changes with time between two fractions separated by a few hours rather than different fractionation schedules. Our model is different in that it neglects inter/intra patient variations among the LQ parameters a and b and assumes that a certain fraction of existing hypoxic cells take part in the TABLE II.…”

Section: Discussionmentioning

confidence: 99%

A radiobiological model of reoxygenation and fractionation effects

Guerrero

Carlson

2017

Medical Physics

View full text Add to dashboard Cite

Purpose: The purpose of this study was to develop a radiobiological model of reoxygenation that fulfills the following goals: (a) Quantify the reoxygenation effect for different fractionations (b) Model the hypoxic fraction in tumors as a function of the number of radiation treatments. (c) Develop a simple analytical expression for a reoxygenation term in biological effect calculations. Method: The model considers tumor cells in two compartments: an aerobic (or normoxic) population of cells and a hypoxic population including cells under a range of reduced oxygen concentrations. The surviving fraction is predicted using the linear-quadratic (LQ) model. A hypoxia reduction factor (HRF) is used to quantify reductions in radiosensitivity parameters a A and b A as cellular oxygen concentration decreases. The HRF is defined as the ratio of the dose at a specific level of hypoxia to the dose under fully aerobic conditions to achieve equal cell killing. The model assumes that a fraction of the hypoxic cells (D) moves from the hypoxic to the aerobic compartment after each daily fraction. As an example, we compare the effect of reoxygenation on biological response for a standard dose fractionation for nonsmall cell lung cancer (NSCLC) (d = 2 Gy, n = 33) to typical fractionations for stereotactic body radiotherapy (SBRT) and other nonstandard fractionations. Results: The reoxygenation effect is parameterized for biological effect calculations and an analytic expression for the surviving fraction after n daily treatments is derived. The hypoxic fraction either increases or decreases with n depending on the reoxygenation parameter D. For certain combinations of parameters, the biological effect of reoxygenation goes as À(nÀ1) Á ln(1ÀD) providing a simple expression that can be introduced in biologically effective dose (BED) calculations. The model is used to compare fractionation schedules and quantitatively interpret results from molecular imaging studies of hypoxia. Based on the comparison of conventional fractionation and hypo-and hyper-fractionation for NSCLC, the value of D is estimated to be between 0.1 and 0.2 assuming plausible radiobiological parameters from the literature. This value is consistent with the preliminary analysis of the molecular imaging studies. Conclusions: A novel radiobiological model was developed that can be used to evaluate the effect of reoxygenation in fractionated radiotherapy.

show abstract

“…The idea is to maximize the biological effect (BE) of radiation dose on the tumor subject to an upper bound constraint on the biologically effective dose (BED) delivered to the normal tissue [25]. This type of literature includes [3,4,14,15,16,17,19,32,35,42,58]. Table 1 below summarizes the contributions of these models.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporally Optimal Fractionation in Radiotherapy

Saberian

Ghate

Kim

2017

INFORMS Journal on Computing

View full text Add to dashboard Cite

We present a spatiotemporally integrated formulation of the optimal fractionation problem using the standard log-linear-quadratic survival model. Our objective is to choose a fluencemap and a number of fractions so as to maximize the biological effect of tumor dose averaged over its voxels subject to maximum dose, mean dose, and dose-volume constraints for various normal tissues. Constrains are expressed in biologically effective dose equivalents. We propose an efficient convex programming method to approximately solve the resulting computationally difficult model. Through extensive computer simulations on ten head-and-neck and prostate cancer test cases with a broad range of radiobiological parameters, we compare the biological effect on tumor obtained by our integrated approach relative to that from two other models. The first is a traditional IMRT fluence-map optimization model that does not optimize the number of fractions. The second assumes that a fluence-map is available a priori from a traditional IMRT optimization model and then optimizes the number of fractions, thus separating the spatial and temporal components. The improvements in tumor biological effect over IMRT were 9%-52% with average 22%, and 53%-108% with average 69%, for head-and-neck and prostate, respectively. The improvements in tumor biological effect over the spatiotemporally separated model were 15%-45% with average 27%, and 17%-23% with average 21%, for head-and-neck and prostate, respectively. This suggests that integrated optimization of the fluence-map and the number of fractions could improve treatment efficacy as measured within the linear-quadratic framework.

show abstract

Optimization of Radiation Therapy Fractionation Schedules in the Presence of Tumor Repopulation

Cited by 35 publications

References 56 publications

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

A feasibility study: Selection of a personalized radiotherapy fractionation schedule using spatiotemporal optimization

A radiobiological model of reoxygenation and fractionation effects

Spatiotemporally Optimal Fractionation in Radiotherapy

Contact Info

Product

Resources

About