An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy

Tian, Liheng; Hahn, Christian; Armin, Luehr,

doi:10.1016/j.radonc.2022.06.023

Cited by 10 publications

(16 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is an ongoing debate regarding whether the LQ model is appropriate to model high dose per fraction effects 15–17 . One key issue is whether the LQ model could be extrapolated and extended to a high dose per fraction as a predictive model.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, for ion‐beam RBE models that rely on the LQ model for cell survival modeling, because the LQ model is unable to handle these data, there was not much choice but to ignore them. For example, one recent work on the proton RBE model excluded 10% of data “where β ≤ 0”, 15 and another work included only “positive and finite α x /β x ” 16 . These examples highlighted the need for alternative cell survival modeling.…”

Section: Introductionmentioning

confidence: 99%

“…13,14 There is an ongoing debate regarding whether the LQ model is appropriate to model high dose per fraction effects. [15][16][17] One key issue is whether the LQ model could be extrapolated and extended to a high dose per fraction as a predictive model. Although most cell survival data are acquired with low to intermediate dose, there is an increasing need to extrapolate the data with the increasing use of hypofractionated treatment with high fractional dose.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival

2023

Cancer Science

View full text Add to dashboard Cite

The linear quadratic (LQ) model has been the dominant tool in preclinical radiobiological modeling of cell survival as a function of dose. However, as a second‐order polynomial approximation, it suffers from two well‐known pitfalls: nonmonotonic behavior and poor extrapolation. This study examined the raw data of 253 sets of photons and 943 sets of the ion beam from the Particle Irradiation Data Ensemble (PIDE) project to understand how often the LQ model could result in a negative β, which would give unrealistic predictions. Additionally, the predictive performance of the LQ model, the power model, and the linear model's predictive performance was studied using leave‐one‐out cross‐validation (LOOCV) and twofold cross‐validation. It was found that, when fitted to the LQ model, 7.5% of the photon and 29.8% of the ion beam dose–response data would result in negative β, compared to 0.77% and 2.0%, respectively, reported in published works. The LQ model performed poorly in LOOCV compared to the alternative power model, and performed the worst among the three models in twofold cross‐validation. The LQ model leads to unrealistic parameters, which are vastly under‐reported in published studies, and performs poorly in standard cross‐validation tests. Therefore, the LQ model is not a valid predictive dose–response model for cell survival. Alternative models need to be investigated.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival

2023

Cancer Science

View full text Add to dashboard Cite

show abstract

“…Q and Q eff are independent of both medium composition and density. They are also expected to better predict RBE over different ion species compared to LET 11,14,15 . While E is proportional to β for nonrelativistic speeds, the main difference between Q and Q eff is the behavior for very low energies where E or β approaches zero.…”

Section: Introductionmentioning

confidence: 99%

“…They are also expected to better predict RBE over different ion species compared to LET. 11 , 14 , 15 While E is proportional to β for nonrelativistic speeds, the main difference between Q and Q eff is the behavior for very low energies where E or β approaches zero. To avoid the expression for Q approaching infinity for small energies, a minimum energy threshold is applied, below which the value is no longer calculated, rendering Q numerically sensitive to the actual threshold applied (which was not defined in the original publication).…”

Section: Introductionmentioning

confidence: 99%

Modeling RBE with other quantities than LET significantly improves prediction of in vitro cell survival for proton therapy

et al. 2022

View full text Add to dashboard Cite

Background For proton therapy, a relative biological effectiveness (RBE) of 1.1 has broadly been applied clinically. However, as unexpected toxicities have been observed by the end of the proton tracks, variable RBE models have been proposed. Typically, the dose‐averaged linear energy transfer (LETd) has been used as an input variable for these models but the way the LETd was defined, calculated, or determined was not always consistent, potentially impacting the corresponding RBE value. Purpose This study compares consistently calculated LETd with other quantities as input variables for a phenomenological RBE model and attempts to determine which quantity that can best predicts proton RBE. The comparison was performed within the frame of introducing a new model for the proton RBE. Methods High‐throughput experimental setups of in vitro cell survival studies for proton RBE determination are simulated using the SHIELD‐HIT12A Monte Carlo particle transport code. Together with LET, z∗2/β2$z^{*2}/\beta ^2$, here called effective Q (Qeff), and Q are scored. Each quantity is calculated using the dose and track averaging methods, because the scoring includes all hadronic particles, all protons or only primaries. A phenomenological linear‐quadratic‐based RBE model is subsequently applied to the in vitro data with the various beam quality descriptors used as input variables and the goodness of fit is determined and compared using a bootstrapping approach. Both linear and nonlinear fit functions were tested. Results Versions of Qeff and Q outperform LET with a statistically significant margin, with the best nonlinear and linear fit having a relative root mean square error (RMSE) for RBE2Gy ± one standard error of 1.55 ± 0.04 (Qeff, t, primary) and 2.84 ± 0.07 (Qeff, d, primary), respectively. For comparison, the corresponding best nonlinear and linear fits for LETd, all protons had a relative RMSE of 2.07 ± 0.06 and 3.39 ± 0.08, respectively. Applying Welch's t‐test for comparing the calculated RMSE of RBE2Gy resulted in two‐tailed p‐values of <0.002 for all Q and Qeff quantities compared to LETd, all protons. Conclusions The study shows that Q or Qeff could be better RBE descriptors that dose averaged LET.

show abstract

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon‐like dose response

Heuchel,

Hahn,

Ödén

et al. 2023

Medical Physics

Self Cite

View full text Add to dashboard Cite

BackgroundApplying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1.PurposeThis work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon‐like dose response for OAR and, thus, less uncertainties when applying photon‐based tolerance doses.MethodsThe concept divides the 1.1‐weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon‐like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1‐weighted dose, variable RBE‐weighted dose using the Wedenberg RBE model and dose‐average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives.ResultsIn the water phantom studies, LET thresholds between 1.5 and 2.5 keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1‐weighted dose in the OARs was slightly increased (patient 1: 0.45 Gy(RBE), patient 2: 0.08 Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1‐weighted dose in healthy brain tissue was observed (patient 1: 0.81 Gy(RBE), patient 2: 0.53 Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3 Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp).ConclusionsThe novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon‐like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon‐based tolerance doses for OAR.

show abstract

An ion-independent phenomenological relative biological effectiveness (RBE) model for proton therapy

Cited by 10 publications

References 50 publications

Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival

Invalidity of, and alternative to, the linear quadratic model as a predictive model for postirradiation cell survival

Modeling RBE with other quantities than LET significantly improves prediction of in vitro cell survival for proton therapy

The dirty and clean dose concept: Towards creating proton therapy treatment plans with a photon‐like dose response

Contact Info

Product

Resources

About