Pencil Beam Scanning Carbon Ion Radiotherapy for Hepatocellular Carcinoma

Purpose

Carbon ion radiotherapy (CIRT) has emerged as a promising treatment modality for hepatocellular carcinoma (HCC). However, evidence of using the pencil beam scanning (PBS) technique to treat moving liver tumors remains lacking. The present study investigated the efficacy and toxicity of PBS CIRT in patients with HCC.

Methods

Between January 2016 and October 2021, 90 consecutive HCC patients treated with definitive CIRT in our center were retrospectively analyzed. Fifty-eight patients received relative biological effectiveness-weighted doses of 50-70 Gy in 10 fractions, and 32 received 60-67.5 Gy in 15 fractions, which were determined by the tumor location and normal tissue constraints. Active motion-management techniques and necessary strategies were adopted to mitigate interplay effects efficiently. Oncologic outcomes and toxicities were evaluated.

Results

The median follow-up time was 28.6 months (range 5.7-74.6 months). The objective response rate was 75.0% for all 90 patients with 100 treated lesions. The overall survival rates at 1-, 2- and 3-years were 97.8%, 83.3% and 75.4%, respectively. The local control rates at 1-, 2- and 3-years were 96.4%, 96.4% and 93.1%, respectively. Radiation-induced liver disease was not documented, and 4 patients (4.4%) had their Child-Pugh score elevated by 1 point after CIRT. No grade 3 or higher acute non-hematological toxicities were observed. Six patients (6.7%) experienced grade 3 or higher late toxicities.

Conclusion

The active scanning technique was clinically feasible to treat HCC by applying necessary mitigation measures for interplay effects. The desirable oncologic outcomes as well as favorable toxicity profiles presented in this study will be a valuable reference for other carbon-ion centers using the PBS technique and local effect model-based system, and add to a growing body of evidence about the role of CIRT in the management of HCC.

Comprehensive comparison of local effect model IV predictions with the particle irradiation data ensemble

PURPOSE

The increased relative biological effectiveness (RBE) of ions is one of the key benefits of ion radiotherapy compared to conventional radiotherapy with photons. To account for the increased RBE of ions during the process of ion radiotherapy treatment planning, a robust model for RBE predictions is indispensable. Currently, at several ion therapy centers the local effect model I (LEM I) is applied to predict the RBE, which varies with biological and physical impacting factors. After the introduction of LEM I, several model improvements were implemented, leading to the current version, LEM IV, which is systematically tested in this study.

METHODS

As a comprehensive RBE model should give consistent results for a large variety of ion species and energies, the particle irradiation data ensemble (PIDE) is used to systematically validate the LEM IV. The database covers over 1100 photon and ion survival experiments in form of their linear-quadratic parameters for a wide range of ion types and energies. This makes the database an optimal tool to challenge the systematic dependencies of the RBE model. After appropriate filtering of the database, 571 experiments were identified and used as test data.

RESULTS

The study confirms that the LEM IV reflects the RBE systematics observed in measurements well. It is able to reproduce the dependence of RBE on the linear energy transfer (LET) as well as on the α_γ /β_γ ratio for several ion species in a wide energy range. Additionally, the systematic quantitative analysis revealed precision capabilities and limits of the model. At lower LET values, the LEM IV tends to underestimate the RBE with an increasing underestimation with increasing atomic number of the ion. At higher LET values, the LEM IV overestimates the RBE for protons or helium ions, whereas the predictions for heavier ions match experimental data well.

CONCLUSIONS

The LEM IV is able to predict general RBE characteristics for several ion species in a broad energy range. The accuracy of the predictions is reasonable considering the small number of input parameters needed by the model. The detailed quantification of possible systematic deviations, however, enables to identify not only strengths but also limitations of the model. The gained knowledge can be used to develop model adjustments to further improve the model accuracy, which is on the way.

Quantification of the radiosensitization effect of high-Znanoparticles on photon irradiated cells: combining Monte Carlo simulations and an analytical approach to the local effect model

In vitroexperiments show significant reduction in the survival fraction of cells under irradiation treatments assisted with high-Znanoparticles (NPs). In order to predict the radiosensitization effect of NPs, a modification of the local effect model (LEM), in which the energy deposition from NPs is assessed by Monte Carlo (MC) radiation transport codes, has been employed in the past. In this work, a combined framework that splits the consideration of the radiosensitization effect into two steps is proposed. The first step is the evaluation of the radial dose distribution (RDD) around a single NP ionized by a photon beam with given energy spectrum using MC simulation. Thereafter, an analytical approach based of the LEM and the calculated RDD is used for evaluation of the average dose and the average number of lethal lesions in a cell target due to a set of ionized NPs. The explicit expressions were derived for the case of a spherical cell target and the RDD describing by the power law function. RDDs around gold NPs (GNPs) of different radii were simulated using the MC technique and fitted by a power law function. The fitted RDD and the derived expressions were applied for calculation of the survival curves and relative biological effectiveness of a spherical MDA-MB-231 cell loaded with GNPs and irradiated with monoenergetic photons of 10-150 keV. The proposed framework provides a practical alternative to time-consuming MC simulations, enabling the assessment of the response of cell cultures to an irradiation treatment assisted with NPs for a wide variety of cell geometries, NP distributions and irradiation schemes.

Brainstem NTCP and Dose Constraints for Carbon Ion RT-Application and Translation From Japanese to European RBE-Weighted Dose

Background and Purpose

The Italian National Center of Oncological Hadrontherapy (CNAO) has applied dose constraints for carbon ion RT (CIRT) as defined by Japan’s National Institute of Radiological Sciences (NIRS). However, these institutions use different models to predict the relative biological effectiveness (RBE). CNAO applies the Local Effect Model I (LEM I), which in most clinical situations predicts higher RBE than NIRS’s Microdosimetric Kinetic Model (MKM). Equal constraints therefore become more restrictive at CNAO. Tolerance doses for the brainstem have not been validated for LEM I-weighted dose (D_{LEM I}). However, brainstem constraints and a Normal Tissue Complication Probability (NTCP) model were recently reported for MKM-weighted dose (D_MKM), showing that a constraint relaxation to D_{MKM|0.7 cm³} <30 Gy (RBE) and D_{MKM|0.1 cm³} <40 Gy (RBE) was feasible. The aim of this work was to evaluate the brainstem NTCP associated with CNAO’s current clinical practice and to propose new brainstem constraints for LEM I-optimized CIRT at CNAO.

Material and Methods

We reproduced the absorbed dose of 30 representative patient treatment plans from CNAO. Subsequently, we calculated both D_{LEM I} and D_MKM, and the relationship between D_MKM and D_{LEM I} for various brainstem dose metrics was analyzed. Furthermore, the NTCP model developed for D_MKM was applied to estimate the NTCPs of the delivered plans.

Results

The translation of CNAO treatment plans to D_MKM confirmed that the former CNAO constraints were conservative compared with D_MKM constraints. Estimated NTCPs were 0% for all but one case, in which the NTCP was 2%. The relationship D_MKM/D_{LEM I} could be described by a quadratic regression model which revealed that the validated D_MKM constraints corresponded to D_{LEM I|0.7 cm³} <41 Gy (RBE) (95% CI, 38–44 Gy (RBE)) and D_{LEM I|0.1 cm³} <49 Gy (RBE) (95% CI, 46–52 Gy (RBE)).

Conclusion

Our study demonstrates that RBE-weighted dose translation is of crucial importance in order to exchange experience and thus harmonize CIRT treatments globally. To mitigate uncertainties involved, we propose to use the lower bound of the 95% CI of the translation estimates, i.e., D_{LEM I|0.7 cm³} <38 Gy (RBE) and D_{LEM I|0.1 cm³} <46 Gy (RBE) as brainstem dose constraints for 16 fraction CIRT treatments optimized with LEM I.

Direct measurement of the 3-dimensional DNA lesion distribution induced by energetic charged particles in a mouse model tissue

Charged particles are increasingly used in cancer radiotherapy and contribute significantly to the natural radiation risk. The difference in the biological effects of high-energy charged particles compared with X-rays or γ-rays is determined largely by the spatial distribution of their energy deposition events. Part of the energy is deposited in a densely ionizing manner in the inner part of the track, with the remainder spread out more sparsely over the outer track region. Our knowledge about the dose distribution is derived solely from modeling approaches and physical measurements in inorganic material. Here we exploited the exceptional sensitivity of γH2AX foci technology and quantified the spatial distribution of DNA lesions induced by charged particles in a mouse model tissue. We observed that charged particles damage tissue nonhomogenously, with single cells receiving high doses and many other cells exposed to isolated damage resulting from high-energy secondary electrons. Using calibration experiments, we transformed the 3D lesion distribution into a dose distribution and compared it with predictions from modeling approaches. We obtained a radial dose distribution with sub-micrometer resolution that decreased with increasing distance to the particle path following a 1/r2 dependency. The analysis further revealed the existence of a background dose at larger distances from the particle path arising from overlapping dose deposition events from independent particles. Our study provides, to our knowledge, the first quantification of the spatial dose distribution of charged particles in biologically relevant material, and will serve as a benchmark for biophysical models that predict the biological effects of these particles.

Systematic analysis of RBE and related quantities using a database of cell survival experiments with ion beam irradiation

For tumor therapy with light ions and for experimental aspects in particle radiobiology the relative biological effectiveness (RBE) is an important quantity to describe the increased effectiveness of particle radiation. By establishing and analysing a database of ion and photon cell survival data, some remarkable properties of RBE-related quantities were observed. The database consists of 855 in vitro cell survival experiments after ion and photon irradiation. The experiments comprise curves obtained in different labs, using different ion species, different irradiation modalities, the whole range of accessible energies and linear energy transfers (LETs) and various cell types. Each survival curve has been parameterized using the linear-quadratic (LQ) model. The photon parameters, α and β, appear to be slightly anti-correlated, which might point toward an underlying biological mechanism. The RBE values derived from the survival curves support the known dependence of RBE on LET, on particle species and dose. A positive correlation of RBE with the ratio α/β of the photon LQ parameters is found at low doses, which unexpectedly changes to a negative correlation at high doses. Furthermore, we investigated the course of the β coefficient of the LQ model with increasing LET, finding typically a slight initial increase and a final falloff to zero. The observed fluctuations in RBE values of comparable experiments resemble overall RBE uncertainties, which is of relevance for treatment planning. The database can also be used for extensive testing of RBE models. We thus compare simulations with the local effect model to achieve this goal.