Optimization of radiobiological effects in intensity modulated proton therapy

Biological optimization for hybrid proton-photon radiotherapy

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy

BACKGROUND

Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization.

PURPOSE

Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm.

METHODS

Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI = gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity.

RESULTS

Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio.

CONCLUSIONS

We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

Incorporating variable RBE in IMPT optimization for ependymoma

PURPOSE

To study the dosimetric impact of incorporating variable relative biological effectiveness (RBE) of protons in optimizing intensity-modulated proton therapy (IMPT) treatment plans and to compare it with conventional constant RBE optimization and linear energy transfer (LET)-based optimization.

METHODS

This study included 10 pediatric ependymoma patients with challenging anatomical features for treatment planning. Four plans were generated for each patient according to different optimization strategies: (1) constant RBE optimization (ConstRBEopt) considering standard-of-care dose requirements; (2) LET optimization (LETopt) using a composite cost function simultaneously optimizing dose-averaged LET (LETd ) and dose; (3) variable RBE optimization (VarRBEopt) using a recent phenomenological RBE model developed by McNamara et al.; and (4) hybrid RBE optimization (hRBEopt) assuming constant RBE for the target and variable RBE for organs at risk. By normalizing each plan to obtain the same target coverage in either constant or variable RBE, we compared dose, LETd , LET-weighted dose, and equivalent uniform dose between the different optimization approaches.

RESULTS

We found that the LETopt plans consistently achieved increased LET in tumor targets and similar or decreased LET in critical organs compared to other plans. On average, the VarRBEopt plans achieved lower mean and maximum doses with both constant and variable RBE in the brainstem and spinal cord for all 10 patients. To compensate for the underdosing of targets with 1.1 RBE for the VarRBEopt plans, the hRBEopt plans achieved higher physical dose in targets and reduced mean and especially maximum variable RBE doses compared to the ConstRBEopt and LETopt plans.

CONCLUSION

We demonstrated the feasibility of directly incorporating variable RBE models in IMPT optimization. A hybrid RBE optimization strategy showed potential for clinical implementation by maintaining all current dose limits and reducing the incidence of high RBE in critical normal tissues in ependymoma patients.

An iterative convex relaxation method for proton LET optimization

Objective:A constant relative biological effectiveness of 1.1 in current clinical practice of proton radiotherapy (RT) is a crude approximation and may severely underestimate the biological dose from proton RT to normal tissues, especially near the treatment target at the end of Bragg peaks that exhibits high linear energy transfer (LET). LET optimization can account for biological effectiveness of protons during treatment planning, for minimizing biological proton dose and hot spots to normal tissues. However, the LET optimization is usually nonlinear and nonconvex to solve, for which this work will develop an effective optimization method based on iterative convex relaxation (ICR).Approach: In contrast to the generic nonlinear optimization method, such as Quasi-Newton (QN) method, that does not account for specific characteristics of LET optimization, ICR is tailored to LET modeling and optimization in order to effectively and efficiently solve the LET problem. Specifically, nonlinear dose-averaged LET term is iteratively linearized and becomes convex during ICR, while nonconvex dose-volume constraint and minimum-monitor-unit constraint are also handled by ICR, so that the solution for LET optimization is obtained by solving a sequence of convex and linearized convex subproblems. Since the high LET mostly occurs near the target, a 1 cm normal-tissue expansion of clinical target volume (CTV) (excluding CTV), i.e. CTV1cm, is defined to as an auxiliary structure during treatment planning, where LET is minimized.Main results: ICR was validated in comparison with QN for abdomen, lung, and head-and-neck cases. ICR was effective for LET optimization, as ICR substantially reduced the LET and biological dose in CTV1cm the ring, with preserved dose conformality to CTV. Compared to QN, ICR had smaller LET, physical and biological dose in CTV1cm, and higher conformity index values; ICR was also computationally more efficient, which was about 3 times faster than QN.Significance: A LET-specific optimization method based on ICR has been developed for solving proton LET optimization, which has been shown to be more computationally efficient than generic nonlinear optimizer via QN, with better plan quality in terms of LET, biological and physical dose conformality.

Using 157Gd doped carbon and 157GdF4 nanoparticles in proton-targeted therapy for effectiveness enhancement and thermal neutron reduction: a simulation study

There are two major problems in proton therapy. (1) In comparison with the gamma-ray therapy, proton therapy has only ~ 10% greater biological effectiveness, and (2) the risk of the secondary neutrons in proton therapy is another unsolved problem. In this report, the increase of biological effectiveness in proton therapy has been evaluated with better performance than ¹¹B in the presence of two proposed nanomaterials of ¹⁵⁷GdF4 and ¹⁵⁷Gd doped carbon with the thermal neutron reduction due to the presence of ¹⁵⁷Gd isotope. The present study is based on the microanalysis calculations using GEANT4 Monte Carlo tool and GEANT4-DNA package for the strand breaks measurement. It was found that the proposed method will increase the effectiveness corresponding to the alpha particles by more than 100% and also, potentially will decrease the thermal neutrons fluence, significantly. Also, in this work, a discussion is presented on a significant contribution of the secondary alpha particles in total effectiveness in proton therapy.

Reformulated McNamara RBE-weighted beam orientation optimization for intensity modulated proton therapy

PURPOSE

Empirical relative biological effectiveness (RBE) models have been used to estimate the biological dose in proton therapy but do not adequately capture the factors influencing RBE values for treatment planning. We reformulate the McNamara RBE model such that it can be added as a linear biological dose fidelity term within our previously developed sensitivity-regularized and heterogeneity-weighted beam orientation optimization (SHBOO) framework.

METHODS

Based on our SHBOO framework, we formulated the biological optimization problem to minimize total McNamara RBE dose to OARs. We solve this problem using two optimization algorithms: FISTA (McNam-FISTA) and Chambolle-Pock (McNam-CP). We compare their performances with a physical dose optimizer assuming RBE = 1.1 in all structures (PHYS-FISTA) and an LET-weighted dose model (LET-FISTA). Three head and neck patients were planned with the four techniques and compared on dosimetry and robustness.

RESULTS

Compared to Phys-FISTA, McNam-CP was able to match CTV [HI, Dmax, D95%, D98%] by [0.00, 0.05%, 1.4%, 0.8%]. McNam-FISTA and McNam-CP were able to significantly improve overall OAR [Dmean, Dmax] by an average of [36.1%,26.4%] and [29.6%, 20.3%], respectively. Regarding CTV robustness, worst [Dmax, V95%, D95%, D98%] improvement of [-6.6%, 6.2%, 6.0%, 4.8%] was reported for McNam-FISTA and [2.7%, 2.7%, 5.3%, -4.3%] for McNam-CP under combinations of range and setup uncertainties. For OARs, worst [Dmax, Dmean] were improved by McNam-FISTA and McNam-CP by an average of [25.0%, 19.2%] and [29.5%, 36.5%], respectively. McNam-FISTA considerably improved dosimetry and CTV robustness compared to LET-FISTA, which achieved better worst-case OAR doses.

CONCLUSION

The four optimization techniques deliver comparable biological doses for the head and neck cases. Besides modest CTV coverage and robustness improvement, OAR biological dose and robustness were substantially improved with both McNam-FISTA and McNam-CP, showing potential benefit for directly incorporating McNamara RBE in proton treatment planning.

Linear Energy Transfer Incorporated Spot-Scanning Proton Arc Therapy Optimization: A Feasibility Study

Purpose

To integrate dose-averaged linear energy transfer (LET_d) into spot-scanning proton arc therapy (SPArc) optimization and to explore its feasibility and potential clinical benefits.

Methods

An open-source proton planning platform (OpenREGGUI) has been modified to incorporate LET_d into optimization for both SPArc and multi-beam intensity-modulated proton therapy (IMPT) treatment planning. SPArc and multi-beam IMPT plans with different beam configurations for a prostate patient were generated to investigate the feasibility of LET_d-based optimization using SPArc in terms of spatial LET_d distribution and plan delivery efficiency. One liver and one brain case were studied to further evaluate the advantages of SPArc over multi-beam IMPT.

Results

With similar dose distributions, the efficacy of spatially optimizing LET_d distributions improves with increasing number of beams. Compared with multi-beam IMPT plans, SPArc plans show substantial improvement in LET_d distributions while maintaining similar delivery efficiency. Specifically, for the liver case, the average LET_d in the GTV was increased by 124% for the SPArc plan, and only 9.6% for the 2-beam IMPT plan compared with the 2-beam non-LET_d optimized IMPT plan. In case of LET optimization for the brain case, the SPArc plan could effectively increase the average LET_d in the CTV and decrease the values in the critical structures while smaller improvement was observed in 3-beam IMPT plans.

Conclusion

This work demonstrates the feasibility and significant advantages of using SPArc for LET_d-based optimization, which could maximize the LET_d distribution wherever is desired inside the target and averts the high LET_d away from the adjacent critical organs-at-risk.

Future Perspectives of Proton Therapy in Minimizing the Toxicity of Breast Cancer Radiotherapy

The toxicity of radiotherapy is a key issue when analyzing the eligibility criteria for patients with breast cancer. In order to obtain better results, proton therapy is proposed because of the more favorable distribution of the dose in the patient’s body compared with photon radiotherapy. Scientific groups have conducted extensive research into the improved efficacy and lower toxicity of proton therapy for breast cancer. Unfortunately, there is no complete insight into the potential reasons and prospects for avoiding undesirable results. Cardiotoxicity is considered challenging; however, researchers have not presented any realistic prospects for preventing them. We compared the clinical evidence collected over the last 20 years, providing the rationale for the consideration of proton therapy as an effective solution to reduce cardiotoxicity. We analyzed the parameters of the dose distribution (mean dose, Dmax, V5, and V20) in organs at risk, such as the heart, blood vessels, and lungs, using the following two irradiation techniques: whole breast irradiation and accelerated partial breast irradiation. Moreover, we presented the possible causes of side effects, taking into account biological and technical issues. Finally, we collected potential improvements in higher quality predictions of toxic cardiac effects, like biomarkers, and model-based approaches to give the full background of this complex issue.

A systematic review on the usage of averaged LET in radiation biology for particle therapy

Linear Energy Transfer (LET) is widely used to express the radiation quality of ion beams, when characterizing the biological effectiveness. However, averaged LET may be defined in multiple ways, and the chosen definition may impact the resulting reported value. We review averaged LET definitions found in the literature, and quantify which impact using these various definitions have for different reference setups. We recorded the averaged LET definitions used in 354 publications quantifying the relative biological effectiveness (RBE) of hadronic beams, and investigated how these various definitions impact the reported averaged LET using a Monte Carlo particle transport code. We find that the kind of averaged LET being applied is, generally, poorly defined. Some definitions of averaged LET may influence the reported averaged LET values up to an order of magnitude. For publications involving protons, most applied dose averaged LET when reporting RBE. The absence of what target medium is used and what secondary particles are included further contributes to an ill-defined averaged LET. We also found evidence of inconsistent usage of averaged LET definitions when deriving LET-based RBE models. To conclude, due to commonly ill-defined averaged LET and to the inherent problems of LET-based RBE models, averaged LET may only be used as a coarse indicator of radiation quality. We propose a more rigorous way of reporting LET values, and suggest that ideally the entire particle fluence spectra should be recorded and provided for future RBE studies, from which any type of averaged LET (or other quantities) may be inferred.

Proton therapy delivery method affects dose-averaged linear energy transfer in patients

The dosimetric advantages of proton therapy have led to its rapid proliferation in recent decades. This has been accompanied by a shift in technology from older units that deliver protons by passive scattering (PS) to newer units that increasingly use pencil-beam scanning (PBS). The biologic effectiveness of proton physical dose purportedly rises with increasing dose-weighted average linear energy transfer (LET_D). The objective of this study was to determine the extent to which proton delivery methods affect LET_D. We calculated LET_Dfrom simple, dosimetrically matched, and clinical treatment plans with TOPAS Monte-Carlo transport code. Simple treatment plans comprised single fields of PS and PBS protons in a water phantom. We performed simulations of matched and clinical treatment plans by using the treatment and anatomic data obtained from a cohort of children with craniopharyngioma who previously received PS or PBS proton therapy. We compared the distributions of LET_Dfrom PS and PBS delivery methods in clinically relevant ROIs. Wilcoxon signed-rank tests comparing single fields in water revealed that the LET_Dvalues from PBS were significantly greater than those from PS inside and outside the targeted volume (p < 0.01). Statistical tests comparing LET_D-volume histograms from matched and clinical treatment plans showed that LET_Dwas generally greater for PBS treatment plans than for PS treatment plans (p < 0.05). In conclusion, the proton delivery method affects LET_Dboth inside and outside of the target volume. These findings suggest that PBS is more biologically effective than PS. Given the rapid expansion of PBS proton therapy, future studies are needed to confirm the applicability of treatment evaluation methods developed for PS proton therapy to those for modern PBS treatments to ensure their safety and effectiveness for the growing population of patients receiving proton therapy. This study uses data from two clinical trials: NCT01419067 and NCT02792582.

The impact of variable relative biological effectiveness in proton therapy for left-sided breast cancer when estimating normal tissue complications in the heart and lung

The aim of this study was to evaluate the clinical impact of relative biological effectiveness (RBE) variations in proton beam scanning treatment (PBS) for left-sided breast cancer versus the assumption of a fixed RBE of 1.1, particularly in the context of comparisons with photon-based three-dimensional conformal radiotherapy (3DCRT) and volumetric modulated arc therapy (VMAT). Ten patients receiving radiation treatment to the whole breast/chest wall and regional lymph nodes were selected for each modality. For PBS, the dose distributions were re-calculated with both a fixed RBE and a variable RBE using an empirical RBE model. Dosimetric indices based on dose-volume histogram analysis were calculated for the entire heart wall, left anterior descending artery (LAD) and left lung. Furthermore, normal tissue toxicity probabilities for different endpoints were evaluated. The results show that applying a variable RBE significantly increases the RBE-weighted dose and consequently the calculated dosimetric indices increases for all organs compared to a fixed RBE. The mean dose to the heart and the maximum dose to the LAD and the left lung are significantly lower for PBS assuming a fixed RBE compared to 3DCRT. However, no statistically significant difference is seen when a variable RBE is applied. For a fixed RBE, lung toxicities are significantly lower compared to 3DCRT but when applying a variable RBE, no statistically significant differences are noted. A disadvantage is seen for VMAT over both PBS and 3DCRT. One-to-one plan comparison on 8 patients between PBS and 3DCRT shows similar results. We conclude that dosimetric analysis for all organs and toxicity estimation for the left lung might be underestimated when applying a fixed RBE for protons. Potential RBE variations should therefore be considered as uncertainty bands in outcome analysis.

Linear energy transfer weighted beam orientation optimization for intensity-modulated proton therapy

PURPOSE

In intensity-modulated proton therapy (IMPT), unaccounted-for variation in biological effectiveness contributes to the discrepancy between the constant relative biological effectiveness (RBE) model prediction and experimental observation. It is desirable to incorporate biological doses in treatment planning to improve modeling accuracy and consequently achieve a higher therapeutic ratio. This study addresses this demand by developing a method to incorporate linear energy transfer (LET) into beam orientation optimization (BOO).

METHODS

Instead of RBE-weighted dose, this LET weighted BOO (LETwBOO) framework uses the dose and LET product (LET  ×  D) as the biological surrogate. The problem is formulated with a physical dose fidelity term, a LET  ×  D constraint term, and a group sparsity term. The LET  ×  D of organs at risks is penalized for minimizing the biological effect while maintaining the physical dose objectives. Group sparsity is used to reduce the number of active beams from 600-800 non-coplanar candidate beams to between 2 and 4. This LETwBOO method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The LETwBOO plans were compared with IMPT plans using manually selected beams with only physical dose constraint (MAN) and the initial MAN plan reoptimized with additional LET  ×  D constraint (LETwMAN).

RESULTS

The LETwBOO plans show superior physical dose and LET  ×  D sparing. On average, the [mean, maximal] doses of organs at risks (OARs) in LETwBOO are reduced by [2.85, 4.6] GyRBE from the MAN plans in the SBT cases and reduced by [0.9, 2.5] GyRBE in the H&N cases, while LETwMAN is comparable to MAN. cLET × Ds of PTVs are comparable in LETwBOO and LETwMAN, where c is a scaling factor of 0.04 μm/keV. On average, in the SBT cases, LETwBOO reduces the OAR [mean, maximal] cLET × D by [1.1, 2.9] Gy from the MAN plans, compared to the reduction by LETwMAN from MAN of [0.7, 1.7] Gy. In the H&N cases, LETwBOO reduces the OAR [mean, maximal] cLET × D by [0.8, 2.6] Gy from the MAN plans, compared to the reduction by LETwMAN from MAN of [0.3, 1.2] Gy.

CONCLUSION

We developed a novel LET weighted BOO method for IMPT to generate plans with improved physical and biological OAR sparing compared with the plans unaccounted for biological effects from BOO.

Dose- rather than fluence-averaged LET should be used as a single-parameter descriptor of proton beam quality for radiochromic film dosimetry

PURPOSE

The dose response of Gafchromic EBT3 films exposed to proton beams depends on the dose, and additionally on the beam quality, which is often quantified with the linear energy transfer (LET) and, hence, also referred to as LET quenching. Fundamentally different methods to determine correction factors for this LET quenching effect have been reported in literature and a new method using the local proton fluence distribution differential in LET is presented. This method was exploited to investigate whether a more practical correction based on the dose- or fluence-averaged LET is feasible in a variety of clinically possible beam arrangements.

METHODS

The relative effectiveness (RE) was characterized within a high LET spread-out Bragg peak (SOBP) in water made up by the six lowest available energies (62.4-67.5 MeV, configuration " b 1 ") resulting in one of the highest clinically feasible dose-averaged LET distributions. Additionally, two beams were measured where a low LET proton beam (252.7 MeV) was superimposed on " b 1 ", which contributed either 50% of the initial particle fluence or 50% of the dose in the SOBP, referred to as configuration " b 2 " and " b 3 ," respectively. The proton LET spectrum was simulated with GATE/Geant4 at all measurement positions. The net optical density change differential in LET was integrated over the local proton spectrum to calculate the net optical density and therefrom the beam quality correction factor. The LET dependence of the film response was accounted for by an LET dependence of one of the three parameters in the calibration function and was determined from inverse optimization using measurement " b 1 ." This method was then validated on the measurements of " b 2 " and " b 3 " and subsequently used to calculate the RE at 900 positions in nine clinically relevant beams. The extrapolated RE set was used to derive a simple linear correction function based on dose-averaged LET ( L d ) and verify the validity in all points of the comprehensive RE set.

RESULTS

The uncorrected film dose deviated up to 26% from the reference dose, whereas the corrected film dose agreed within 3% in all three beams in water (" b 1 ", " b 2 " and " b 3 "). The LET dependence of the calibration function started to strongly increase around 5 keV/μm and flatten out around 30 keV/μm. All REs calculated from the proton fluence in the nine simulated beams could be approximated with a linear function of dose-averaged LET (RE = 1.0258-0.0211 μm/keV L d ). However, no functional relationship of RE- and fluence-averaged LET could be found encompassing all beam energies and modulations.

CONCLUSIONS

The film quenching was found to be nonlinear as a function of proton LET as well as of the dose-averaged LET. However, the linear relation of RE on dose-averaged LET was a good approximation in all cases. In contrast to dose-averaged LET, fluence-averaged LET could not describe the RE when multiple beams were applied.

Robust Optimization for Intensity Modulated Proton Therapy to Redistribute High Linear Energy Transfer from Nearby Critical Organs to Tumors in Head and Neck Cancer

PURPOSE

We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties.

METHODS AND MATERIALS

Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET-volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET-volume constraints. Dose-volume histogram indices, such as clinical target volume (CTV) D_98% and D_2%-D_98%, cord D_max, brain stem D_max, brain D_max, and oral cavity D_mean, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose-volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance.

RESULTS

In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET_98%: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LET_max: 7.20 vs 8.20, P = .0010; brain stem LET_max: 10.95 vs 12.05, P = .0007; oral cavity LET_mean: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LET_mean in the CTV, reduced LET_max in the brain, and was comparable to other LETVH indices (CTV LET_mean: 3.26 vs 3.35, P = .0012; brain LET_max: 24.80 vs 22.00, P = .0016).

CONCLUSIONS

LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.

Robust Proton Treatment Planning: Physical and Biological Optimization

Accurate prediction of tumor control and toxicities in radiation therapy faces many uncertainties. Besides interpatient variability in the response to radiation, there are also dosimetric uncertainties, that is, differences between the dose displayed in a treatment planning system and the dose actually delivered to the patient. These uncertainties originate from several sources including imperfect knowledge of the patient geometry, approximation in the physics of radiation interaction with tissues, and uncertainties in the biological effectiveness of radiation. Generally, uncertainties are considered in the treatment planning process by applying margins. In intensity-modulated radiotherapy (IMRT), this leads to the planning target volume (PTV) concept. Intensity-modulated proton therapy (IMPT) is widely considered as the future of proton therapy. The treatment planning methods for IMPT and IMRT are similar and based on mathematical optimization techniques for both modalities. However, the PTV concept has fundamental limitations in IMPT. Therefore, researchers have developed robust optimization methods that directly incorporate uncertainties into the IMPT optimization problem. In recent years, vendors of commercial planning systems have started to implement these methods so that robust IMPT planning becomes available in clinical practice. This article summarizes uncertainties in proton therapy and the limitations of the PTV concept to deal with them. Subsequently, robust optimization techniques to overcome these limitations are reviewed.

Comparing Photon and Charged Particle Therapy Using DNA Damage Biomarkers

Treatment modalities for cancer radiation therapy have become increasingly diversified given the growing number of facilities providing proton and carbon-ion therapy in addition to the more historically accepted photon therapy. An understanding of high-LET radiobiology is critical for optimization of charged particle radiation therapy and potential DNA damage response. In this review, we present a comprehensive summary and comparison of these types of therapy monitored primarily by using DNA damage biomarkers. We focus on their relative profiles of dose distribution and mechanisms of action from the level of nucleic acid to tumor cell death.

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a ‘proof-of-concept’ methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

Full Monte Carlo-Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit

PURPOSE

One of the major benefits of carbon ion therapy is enhanced biological effectiveness at the Bragg peak region. For intensity modulated carbon ion therapy (IMCT), it is desirable to use Monte Carlo (MC) methods to compute the properties of each pencil beam spot for treatment planning, because of their accuracy in modeling physics processes and estimating biological effects. We previously developed goCMC, a graphics processing unit (GPU)-oriented MC engine for carbon ion therapy. The purpose of the present study was to build a biological treatment plan optimization system using goCMC.

METHODS AND MATERIALS

The repair-misrepair-fixation model was implemented to compute the spatial distribution of linear-quadratic model parameters for each spot. A treatment plan optimization module was developed to minimize the difference between the prescribed and actual biological effect. We used a gradient-based algorithm to solve the optimization problem. The system was embedded in the Varian Eclipse treatment planning system under a client-server architecture to achieve a user-friendly planning environment. We tested the system with a 1-dimensional homogeneous water case and 3 3-dimensional patient cases.

RESULTS

Our system generated treatment plans with biological spread-out Bragg peaks covering the targeted regions and sparing critical structures. Using 4 NVidia GTX 1080 GPUs, the total computation time, including spot simulation, optimization, and final dose calculation, was 0.6 hour for the prostate case (8282 spots), 0.2 hour for the pancreas case (3795 spots), and 0.3 hour for the brain case (6724 spots). The computation time was dominated by MC spot simulation.

CONCLUSIONS

We built a biological treatment plan optimization system for IMCT that performs simulations using a fast MC engine, goCMC. To the best of our knowledge, this is the first time that full MC-based IMCT inverse planning has been achieved in a clinically viable time frame.

Linear energy transfer incorporated intensity modulated proton therapy optimization

The purpose of this study was to investigate the feasibility of incorporating linear energy transfer (LET) into the optimization of intensity modulated proton therapy (IMPT) plans. Because increased LET correlates with increased biological effectiveness of protons, high LETs in target volumes and low LETs in critical structures and normal tissues are preferred in an IMPT plan. However, if not explicitly incorporated into the optimization criteria, different IMPT plans may yield similar physical dose distributions but greatly different LET, specifically dose-averaged LET, distributions. Conventionally, the IMPT optimization criteria (or cost function) only includes dose-based objectives in which the relative biological effectiveness (RBE) is assumed to have a constant value of 1.1. In this study, we added LET-based objectives for maximizing LET in target volumes and minimizing LET in critical structures and normal tissues. Due to the fractional programming nature of the resulting model, we used a variable reformulation approach so that the optimization process is computationally equivalent to conventional IMPT optimization. In this study, five brain tumor patients who had been treated with proton therapy at our institution were selected. Two plans were created for each patient based on the proposed LET-incorporated optimization (LETOpt) and the conventional dose-based optimization (DoseOpt). The optimized plans were compared in terms of both dose (assuming a constant RBE of 1.1 as adopted in clinical practice) and LET. Both optimization approaches were able to generate comparable dose distributions. The LET-incorporated optimization achieved not only pronounced reduction of LET values in critical organs, such as brainstem and optic chiasm, but also increased LET in target volumes, compared to the conventional dose-based optimization. However, on occasion, there was a need to tradeoff the acceptability of dose and LET distributions. Our conclusion is that the inclusion of LET-dependent criteria in the IMPT optimization could lead to similar dose distributions as the conventional optimization but superior LET distributions in target volumes and normal tissues. This may have substantial advantages in improving tumor control and reducing normal tissue toxicities.

Special radiobiological features of second cancer risk after particle radiotherapy

In absolute terms: second cancer risks from radiotherapy of first cancers in adults are small compared to the benefits from radiotherapy but this is not so for radiotherapy of childhood cancers. Moreover, the radiation dose dependence of cancer induction differs between organs and tissues. The organ-specific dose dependence of second cancer risks may indicate the existence of different radiobiological mechanisms. As an inevitable consequence of the age dependence of organ sensitivity to second cancer induction, the organ/tissue weighting factors which have been proposed by ICRP for calculating effective dose (the dose unit Sv) and for risk estimation in the general population should not be used in medical radiation exposures. In adult cancer radiotherapy, the most common unwanted effect is local tumour recurrence whereas both, severe late normal tissue damage and radiation-induced second cancers are rare, around 1% of locally controlled cancer patients. In childhood cancers, local failures are rare (<10% in some cancers) yet second cancers are more common than uncontrolled primaries. The main reason for considering particle radiotherapy for childhood cancers is the possibility to exploit their physical characteristics to reduce the radiation exposure to organs and tissues close to and distant from the primary cancer which is to be targeted. However, the relative biological effectiveness of the radiation doses within the proton beam is not a constant and the relative biological effectiveness of the neutrons is not known as far as the mechanisms of late normal tissue damage and second cancer risk are concerned. In view of the highly charged discussions of the potential risks of treatment-induced seecond cancers from the neutron contamination of exposure doses in out-of-PTV critical organs a comprehensive European project called ANDANTE was performed which integrated the disciplines of radiation physics, molecular biology, systems biology modelling and epidemiology in order to investigate the RBE of induction of cancer from exposure to neutrons compared to photons. Since out-of-field "effective" neutron doses from proton therapy are smaller than the photon stray doses whichever reasonable RBE is chosen for comparison, and since the absolute risk of radiation-induced second cancer rates are in the order of 1% in the cohorts of adult patients who have been treated in the past with methods which caused relatively high out-of-field doses to large body volumes, it is highly unlikely that such patients treated in future with highly conformal particle therapy are at a higher radiation-induced second cancer risk than those patients treated with photons and described before. Still, the potential risks of second cancers from scattered proton radiotherapy for childhood cancers may cause concern. Yet, the overall risk of undesired consequences of radiation exposure of children which are more complex and manifold than in adult patients (including developmental, neurocognitive, hormonal and growth impairment effects) are likely to be very much reduced by the better focussing of the radiation dose in the target offered by particle radioherapy. This benefit may far outweigh the still hypothetical second cancer risk from particle radiotherapy in pediatric radiotherapy.

Radiobiological issues in proton therapy

BACKGROUND

The relative biological effectiveness (RBE) for particle therapy is a complex function of particle type, radiation dose, linear energy transfer (LET), cell type, endpoint, etc. In the clinical practice of proton therapy, the RBE is assumed to have a fixed value of 1.1. This assumption, along with the effects of physical uncertainties, may mean that the biologically effective dose distributions received by the patient may be significantly different from what is seen on treatment plans. This may contribute to unforeseen toxicities and/or failure to control the disease. Variability of Proton RBE: It has been shown experimentally that proton RBE varies significantly along the beam path, especially near the end of the particle range. While there is now an increasing acceptance that proton RBE is variable, there is an ongoing debate about whether to change the current clinical practice. Clinical Evidence: A rationale against the change is the uncertainty in the models of variable RBE. Secondly, so far there is no clear clinical evidence of the harm of assuming proton RBE to be 1.1. It is conceivable, however, that the evidence is masked partially by physical uncertainties. It is, therefore, plausible that reduction in uncertainties and their incorporation in the estimation of dose actually delivered may isolate and reveal the variability of RBE in clinical practice. Nevertheless, clinical evidence of RBE variability is slowly emerging as more patients are treated with protons and their response data are analyzed. Modelling and Incorporation of RBE in the Optimization of Proton Therapy: The improvement in the knowledge of RBE could lead to better understanding of outcomes of proton therapy and in the improvement of models to predict RBE. Prospectively, the incorporation of such models in the optimization of intensity-modulated proton therapy could lead to improvements in the therapeutic ratio of proton therapy.

Robust intensity-modulated proton therapy to reduce high linear energy transfer in organs at risk

PURPOSE

We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity-modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs.

METHODS

We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The "worst-case robust optimization" model was applied. One additional term as a "biological surrogate (BS)" of OARs due to the high LET-related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose-averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in-house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst-case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited-memory Broyden-Fletcher-Goldfarb-Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose-volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods.

RESULTS

For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model.

CONCLUSIONS

Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.

Quantification of the uncertainties of a biological model and their impact on variable RBE proton treatment plan optimization

PURPOSE

In proton radiation therapy, a relative biological effectiveness (RBE) equal to 1.1 is currently assumed, although biological experiments show that it is not constant. The purpose of this study was to quantify the uncertainties of a published biological model and explore their impact on variable RBE treatment plan (TP) optimization.

METHODS

Two patient cases with a high and a low (α/β)_x tumor were investigated. Firstly, intensity modulated proton therapy TPs assuming constant RBE were derived, and subsequently the variable RBE weighted dose (RWD), including the uncertainty originating in the fit to the experimental data and the uncertainty of the (α/β)_x, were calculated. Secondly, TPs optimized for uniform biological effect assuming a variable RBE were created using the worst case tissue specific (α/β)_x.

RESULTS

For the nasopharyngeal cancer patient, the uncertainty of (α/β)_x corresponded to a CTV D₉₈ confidence interval (CI) of (-2, +4)% while for the fit parameter CI was (-2,+1)%. For the standard fractionation prostate case the (α/β)_x CI was (-7,+5)% and the fit parameter CI was (-3,+3)%. For the hypofractionated case both CIs were (-1,+1)%. In both patient cases, the RBE in most organs at risk (OARs) was significantly underestimated by the constant RBE approximation, whereas the situation was not as definite in the target volumes. Overdosage of OARs was reduced by using the biological effect optimization.

CONCLUSION

For the two patient cases, the RWD uncertainty from the fit parameter in the biological model contributed non-negligibly to the total uncertainty, depending on the patient case and the organ. The presented optimization strategy is a basic method for robust biological effect optimization to reduce potential consequences caused by the (α/β)_x uncertainty.

A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy

Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6  ±  15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.

Reoptimization of Intensity Modulated Proton Therapy Plans Based on Linear Energy Transfer

PURPOSE

We describe a treatment plan optimization method for intensity modulated proton therapy (IMPT) that avoids high values of linear energy transfer (LET) in critical structures located within or near the target volume while limiting degradation of the best possible physical dose distribution.

METHODS AND MATERIALS

To allow fast optimization based on dose and LET, a GPU-based Monte Carlo code was extended to provide dose-averaged LET in addition to dose for all pencil beams. After optimizing an initial IMPT plan based on physical dose, a prioritized optimization scheme is used to modify the LET distribution while constraining the physical dose objectives to values close to the initial plan. The LET optimization step is performed based on objective functions evaluated for the product of LET and physical dose (LET×D). To first approximation, LET×D represents a measure of the additional biological dose that is caused by high LET.

RESULTS

The method is effective for treatments where serial critical structures with maximum dose constraints are located within or near the target. We report on 5 patients with intracranial tumors (high-grade meningiomas, base-of-skull chordomas, ependymomas) in whom the target volume overlaps with the brainstem and optic structures. In all cases, high LET×D in critical structures could be avoided while minimally compromising physical dose planning objectives.

CONCLUSION

LET-based reoptimization of IMPT plans represents a pragmatic approach to bridge the gap between purely physical dose-based and relative biological effectiveness (RBE)-based planning. The method makes IMPT treatments safer by mitigating a potentially increased risk of side effects resulting from elevated RBE of proton beams near the end of range.

Evaluation of the relative biological effectiveness of spot-scanning proton irradiation in vitro

Variations in relative biological effectiveness (RBE) from a fixed value of 1.1 are critical in proton beam therapy. To date, studies estimating RBE at multiple positions relative to the spread-out Bragg peak (SOBP) have been predominantly performed using passive scattering methods, and limited data are available for spot-scanning beams. Thus, to investigate the RBE of spot-scanning beams, Chinese hamster fibroblast V79 cells were irradiated using the beam line at the Hokkaido University Hospital Proton Therapy Center. Cells were placed at six different depths, including the entrance of the proton beam and the proximal and distal part of the SOBP. Surviving cell fractions were analyzed using colony formation assay, and cell survival curves were obtained by the curve fitted using a linear-quadratic model. RBE10 and RBE37 were 1.15 and 1.21 at the center of the SOBP, respectively. In contrast, the distal region showed higher RBE values (1.50 for RBE10 and 1.85 for RBE37). These results are in line with those of previous studies conducted using passive scattering proton beams. Taken together, these data strongly suggest that variations in RBE should be considered during treatment planning for spot-scanning beams as well as for passive scattering proton beams.

Disregarding RBE variation in treatment plan comparison may lead to bias in favor of proton plans

PURPOSE

Currently in proton radiation therapy, a constant relative biological effectiveness (RBE) equal to 1.1 is assumed. The purpose of this study is to evaluate the impact of disregarding variations in RBE on the comparison of proton and photon treatment plans.

METHODS

Intensity modulated treatment plans using photons and protons were created for three brain tumor cases with the target situated close to organs at risk. The proton plans were optimized assuming a standard RBE equal to 1.1, and the resulting linear energy transfer (LET) distribution for the plans was calculated. In the plan evaluation, the effect of a variable RBE was studied. The RBE model used considers the RBE variation with dose, LET, and the tissue specific parameter α/β of photons. The plan comparison was based on dose distributions, DVHs and normal tissue complication probabilities (NTCPs).

RESULTS

Under the assumption of RBE=1.1, higher doses to the tumor and lower doses to the normal tissues were obtained for the proton plans compared to the photon plans. In contrast, when accounting for RBE variations, the comparison showed lower doses to the tumor and hot spots in organs at risk in the proton plans. These hot spots resulted in higher estimated NTCPs in the proton plans compared to the photon plans.

CONCLUSIONS

Disregarding RBE variations might lead to suboptimal proton plans giving lower effect in the tumor and higher effect in normal tissues than expected. For cases where the target is situated close to structures sensitive to hot spot doses, this trend may lead to bias in favor of proton plans in treatment plan comparisons.

Risk-optimized proton therapy to minimize radiogenic second cancers

Proton therapy confers substantially lower predicted risk of second cancer compared with photon therapy. However, no previous studies have used an algorithmic approach to optimize beam angle or fluence-modulation for proton therapy to minimize those risks. The objectives of this study were to demonstrate the feasibility of risk-optimized proton therapy and to determine the combination of beam angles and fluence weights that minimizes the risk of second cancer in the bladder and rectum for a prostate cancer patient. We used 6 risk models to predict excess relative risk of second cancer. Treatment planning utilized a combination of a commercial treatment planning system and an in-house risk-optimization algorithm. When normal-tissue dose constraints were incorporated in treatment planning, the risk model that incorporated the effects of fractionation, initiation, inactivation, repopulation and promotion selected a combination of anterior and lateral beams, which lowered the relative risk by 21% for the bladder and 30% for the rectum compared to the lateral-opposed beam arrangement. Other results were found for other risk models.

Spatial mapping of the biologic effectiveness of scanned particle beams: towards biologically optimized particle therapy

The physical properties of particles used in radiation therapy, such as protons, have been well characterized, and their dose distributions are superior to photon-based treatments. However, proton therapy may also have inherent biologic advantages that have not been capitalized on. Unlike photon beams, the linear energy transfer (LET) and hence biologic effectiveness of particle beams varies along the beam path. Selective placement of areas of high effectiveness could enhance tumor cell kill and simultaneously spare normal tissues. However, previous methods for mapping spatial variations in biologic effectiveness are time-consuming and often yield inconsistent results with large uncertainties. Thus the data needed to accurately model relative biological effectiveness to guide novel treatment planning approaches are limited. We used Monte Carlo modeling and high-content automated clonogenic survival assays to spatially map the biologic effectiveness of scanned proton beams with high accuracy and throughput while minimizing biological uncertainties. We found that the relationship between cell kill, dose, and LET, is complex and non-unique. Measured biologic effects were substantially greater than in most previous reports, and non-linear surviving fraction response was observed even for the highest LET values. Extension of this approach could generate data needed to optimize proton therapy plans incorporating variable RBE.

Radiobiological intercomparison of the 160 MeV and 230 MeV proton therapy beams at the Harvard Cyclotron Laboratory and at Massachusetts General Hospital

The purpose of this study was to determine the relative biological effectiveness (RBE) along the axis of two range-modulated proton beams (160 and 230 MeV). Both the depth and the dose dependence of RBE were investigated. Chinese hamster V79-WNRE cells, suspended in medium containing gelatin and cooled to 2 °C, were used to obtain complete survival curves at multiple positions throughout the entrance and 10 cm spread-out Bragg peak (SOBP). Simultaneous measurements of the survival response to (60)Co gamma rays served as the reference data for the proton RBE determinations. For both beams the RBE increased significantly with depth in the 10 cm SOBP, particularly in the distal half of the SOBP, then rose even more sharply at the distal edge, the most distal position measured. At a 4 Gy dose of gamma radiation (S = 0.34) the average RBE values for the entrance, proximal half, distal half and distal edge were 1.07 ± 0.01, 1.10 ± 0.01, 1.17 ± 0.01 and 1.21 ± 0.01, respectively, and essentially the same for both beams. At a 2 Gy dose of gamma radiation (S = 0.71) the average RBE values rose to 1.13 ± 0.03, 1.15 ± 0.02, 1.26 ± 0.02 and 1.30 ± 0.02, respectively, for the same four regions of the SOBP. The difference between the 4 Gy and 2 Gy RBE values reflects the dose dependence of RBE as measured in these V79-WNRE cells, which have a low α/β value, as do other widely used cell lines that also show dose-dependent RBE values. Late-responding tissues are also characterized by low α/β values, so it is possible that these cell lines may be predictive for the response of such tissues (e.g., spinal cord, optic nerve, kidney, liver, lung). However, in the very small number of studies of late-responding tissues performed to date there appears to be no evidence of an increased RBE for protons at low doses. Similarly, RBE measurements using early responding in vivo systems (mostly mouse jejunum, an early-responding tissue which has a large α/β ∼ 10 Gy) have generally shown little or no detectable dose dependence. It is useful to compare the RBE values reported here to the commonly used generic clinical RBE of 1.1, which assumes no dependence on depth or on dose. Our proximal RBEs obviously avoid the depth-related increase in RBE and for doses of 4 Gy or more, the low-dose increase in RBE is also minimized, as shown in this article. Thus the proximal RBE at a 4 Gy dose of 1.10 ± 0.01, quoted above, represents an interesting point of congruence with the clinical RBE for conditions where it could reasonably be expected in the measurements reported here. The depth dependence of RBE reported here is consistent with the majority of measurements, both in vitro and in vivo, by other investigators. The dose dependence of RBE, on the other hand, is tissue specific but has not yet been demonstrated for protons by RBE values in late-responding normal tissue systems. This indicates a need for additional RBE determination as function of dose, especially in late-responding tissues.

Direct evaluation of radiobiological parameters from clinical data in the case of ion beam therapy: an alternative approach to the relative biological effectiveness

The relative biological effectiveness (RBE) concept is commonly used in treatment planning for ion beam therapy. Whether models based on in vitro/in vivo RBE data can be used to predict human response to treatments is an open issue. In this work an alternative method, based on an effective radiobiological parameterization directly derived from clinical data, is presented. The method has been applied to the analysis of prostate cancer trials with protons and carbon ions.Prostate cancer trials with proton and carbon ion beams reporting 5 year-local control (LC5) and grade 2 (G2) or higher genitourinary toxicity rates (TOX) were selected from literature to test the method. Treatment simulations were performed on a representative subset of patients to produce dose and linear energy transfer distribution, which were used as explicative physical variables for the radiobiological modelling. Two models were taken into consideration: the microdosimetric kinetic model (MKM) and a linear model (LM). The radiobiological parameters of the LM and MKM were obtained by coupling them with the tumor control probability and normal tissue complication probability models to fit the LC5 and TOX data through likelihood maximization. The model ranking was based on the Akaike information criterion.Results showed large confidence intervals due to the limited variety of available treatment schedules. RBE values, such as RBE = 1.1 for protons in the treated volume, were derived as a by-product of the method, showing a consistency with current approaches. Carbon ion RBE values were also derived, showing lower values than those assumed for the original treatment planning in the target region, whereas higher values were found in the bladder. Most importantly, this work shows the possibility to infer the radiobiological parametrization for proton and carbon ion treatment directly from clinical data.

Proton beam therapy: a fad or a new standard of care

PURPOSE OF REVIEW

Newer methods and advances in radiation therapy promise to reduce the risk of complications in children who require irradiation. They have secured the role of radiation therapy in the treatment of a variety of pediatric central nervous system and solid tumors and for young patients enrolled on clinical trials.

RECENT FINDINGS

Proton therapy is the latest advancement in radiation therapy. Its availability is increasing as new centers are built throughout the United States. Pediatric specialists should understand that proton therapy is in its pioneering stage of development and that advantages have not been quantitatively demonstrated. Proton therapy clearly reduces collateral radiation dose to normal tissue when compared with photon (X-ray)-based methods of irradiation and has the potential to selectively and safely escalate dose to high-risk tumors; however, research results are lacking in both of these areas, leading to some confusion among pediatric specialists with regard to indications and the need to refer patients for this limited resource and expensive form of radiation therapy.

SUMMARY

This review highlights a number of issues surrounding proton therapy in children and supports the use of proton therapy in clinical trials.

New strategies in radiation therapy: exploiting the full potential of protons

Protons provide significant dosimetric advantages compared with photons because of their unique depth-dose distribution characteristics. However, they are more sensitive to the effects of intra- and intertreatment fraction anatomic variations and uncertainties in treatment setup. Furthermore, in the current practice of proton therapy, the biologic effectiveness of protons relative to photons is assumed to have a generic fixed value of 1.1. However, this is a simplification, and it is likely higher in different portions of the proton beam. Current clinical practice and trials have not fully exploited the unique physical and biologic properties of protons. Intensity-modulated proton therapy, with its ability to manipulate energies (in addition to intensities), provides an entirely new dimension, which, with ongoing research, has considerable potential to increase the therapeutic ratio.

Planning and delivery of intensity-modulated radiation therapy

Intensity modulated radiation therapy (IMRT) is an advanced form of external beam radiation therapy. IMRT offers an additional dimension of freedom as compared with field shaping in three-dimensional conformal radiation therapy because the radiation intensities within a radiation field can be varied according to the preferences of locations within a given beam direction from which the radiation is directed to the tumor. This added freedom allows the treatment planning system to better shape the radiation doses to conform to the target volume while sparing surrounding normal structures. The resulting dosimetric advantage has shown to translate into clinical advantages of improving local and regional tumor control. It also offers a valuable mechanism for dose escalation to tumors while simultaneously reducing radiation toxicities to the surrounding normal tissue and sensitive structures. In less than a decade, IMRT has become common practice in radiation oncology. Looking forward, the authors wonder if IMRT has matured to such a point that the room for further improvement has diminished and so it is pertinent to ask what the future will hold for IMRT. This article attempts to look from the perspective of the current state of the technology to predict the immediate trends and the future directions. This article will (1) review the clinical experience of IMRT; (2) review what we learned in IMRT planning; (3) review different treatment delivery techniques; and finally, (4) predict the areas of advancements in the years to come.

Intensity-modulated radiotherapy of nasopharyngeal carcinoma: a comparative treatment planning study of photons and protons

BACKGROUND

The aim of this treatment planning study was to investigate the potential advantages of intensity-modulated (IM) proton therapy (IMPT) compared with IM photon therapy (IMRT) in nasopharyngeal carcinoma (NPC).

METHODS

Eight NPC patients were chosen. The dose prescriptions in cobalt Gray equivalent (GyE) for gross tumor volumes of the primary tumor (GTV-T), planning target volumes of GTV-T and metastatic (PTV-TN) and elective (PTV-N) lymph node stations were 72.6 GyE, 66 GyE, and 52.8 GyE, respectively. For each patient, nine coplanar fields IMRT with step-and-shoot technique and 3D spot-scanned three coplanar fields IMPT plans were prepared. Both modalities were planned in 33 fractions to be delivered with a simultaneous integrated boost technique. All plans were prepared and optimized by using the research version of the inverse treatment planning system KonRad (DKFZ, Heidelberg).

RESULTS

Both treatment techniques were equal in terms of averaged mean dose to target volumes. IMPT plans significantly improved the tumor coverage and conformation (P < 0.05) and they reduced the averaged mean dose to several organs at risk (OARs) by a factor of 2-3. The low-to-medium dose volumes (0.33-13.2 GyE) were more than doubled by IMRT plans.

CONCLUSION

In radiotherapy of NPC patients, three-field IMPT has greater potential than nine-field IMRT with respect to tumor coverage and reduction of the integral dose to OARs and non-specific normal tissues. The practicality of IMPT in NPC deserves further exploration when this technique becomes available on wider clinical scale.

Particle therapy in prostate cancer: a review

While dose escalation is proving important to achieve satisfactory long-term outcomes in prostate cancer, the optimal radiation modality to deliver the treatment is still a topic of debate. Charged particle beams can offer improved dose distributions to the target volume as compared to conventional 3D-conformal radiotherapy, with better sparing of surrounding healthy tissues. Exquisite dose distributions, with the fulfillment of dose-volume constraints to normal tissues, however, can also be achieved with photon-based intensity-modulated techniques. This review summarizes the literature on the use of particle therapy in prostate cancer and attempts to put in perspective its relative merits compared to current photon-based radiotherapy.

Energy optimization procedure for treatment planning with laser-accelerated protons

A simple analytical model is found that predicts the exact proton spectrum needed to obtain a spread-out-Bragg peak (SOBP) distribution for laser-accelerated proton beams. The theory is based on the solution to the Boltzmann kinetic equation for the proton distribution function. The resulting analytical expression allows one to calculate the SOBP proton energy spectra for the different beamlet sizes and modulation depths that can be readily implemented in the calculation of energy and intensity modulated proton dose distributions. Since the practical implementation of energy modulation for proton beams is realized through the discrete superposition of individual Bragg peaks, it is shown that there exists an optimal relationship between the energy sampling size and the width of the initial proton energy distribution.

Fast multifield optimization of the biological effect in ion therapy

In this paper, we present a new technique for simultaneous multifield optimization of the biological effect (i.e. relative biological effectiveness times dose) for intensity modulated radiotherapy with ion beams. It offers complete inverse treatment planning by taking into account planning constraints for the target volume as well as for organs at risk. The approach is based on the mixed irradiation formalism of the linear-quadratic model from radiobiology. We employ a novel objective function to directly optimize the biological effect rather than the physical dose. The required biological input data are reduced to a minimum and are completely independent from the optimization itself. They can be derived from any radiobiological model or even from directly measured data. The new optimization method was fully integrated into our inverse treatment planning tool KonRad. Comparisons with the TRiP98 treatment planning code were done for simple spread-out Bragg peaks as well as for three-dimensional treatment plans, where all fields were optimized separately. While the agreement between both planning systems was very good, the calculation time was substantially reduced in KonRad. By enabling the multifield optimization, the quality of the treatment plans and the sparing of healthy tissues can be clearly improved.