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Wenna Zhang,1– 3 Xin Cai,1– 3 Jiayao Sun,2– 4 Weiwei Wang,2– 4 Jingfang Zhao,2– 4 Qing Zhang,1– 3 Guoliang Jiang,1– 3,5 Zheng Wang1– 3,5 1Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, People’s Republic of China; 3Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, People’s Republic of China; 4Department of Medical Physics, Shanghai Proton and Heavy Ion Center, Shanghai, People’s Republic of China; 5Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, People’s Republic of ChinaCorrespondence: Guoliang Jiang; Zheng Wang, Department of Radiation Oncology, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, No. 4365 Kangxin Road, Pudong, Shanghai, 201315, People’s Republic of China, Email guoliang.jiang@sphic.org.cn; zheng.wang@sphic.org.cnPurpose: 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.Keywords: hepatocellular carcinoma, carbon ion radiotherapy, pencil beam scanning, local effect model

Abstract Background Gadolinium-based nanoparticles (GdNPs) are clinically used agents to increase the radiosensitivity of tumor cells. However, studies on the mechanisms and biological modeling of GdNP radio-enhancement are still preliminary. This study aims to investigate the mechanism of radio-enhancement of GdNPs for kilovoltage photons using Monte Carlo (MC) simulations, and to establish local effect model (LEM)-based biological model of GdNP radiosensitization. Methods The spectrum and yield of secondary electrons and dose enhancement around a single GdNP and clustered GdNPs were calculated in a water cube phantom by MC track-structure simulations using TOPAS code. We constructed a partial shell-like cell geometry model of pancreatic cancer cell based on transmission electron microscope (TEM) observations. LEM-based biological modeling of GdNP radiosensitization was established based on the MC-calculated nano-scale dose distributions in the cell model to predict the cell surviving fractions after irradiation. Results The yield of secondary electrons for GdNP was 0.16% of the yield for gold nanoparticle (GNP), whereas the average electron energy was 12% higher. The majority of the dose enhancement came from the contribution of Auger electrons. GdNP clusters had a larger range and extent of dose enhancement than single GdNPs, although GdNP clustering reduced radial dose per interacting photon significantly. For the dose range between 0 and 8 Gy, the surviving fraction predicted using LEM-based biological model laid within one standard deviation of the published experimental results, and the deviations between them were all within 25%. Conclusions The mechanism of radio-enhancement of GdNPs for kilovoltage photons was investigated using MC simulations. The prediction results of the established LEM-based biological model for GdNP radiosensitization showed good agreement with published experimental results, although the deviation of simulation parameters can lead to large disparity in the results. To our knowledge, this was the first LEM-based biological model for GdNP radiosensitization.

Background: Gadolinium-based nanoparticles (GdNPs) are clinically used agents to increase the radiosensitivity of tumor cells. However, studies on the mechanisms and biological modeling of GdNP radio-enhancement are still preliminary. This study aims to investigate the mechanism of radio-enhancement of GdNPs for kilovoltage photons using Monte Carlo (MC) simulations, and to establish local effect model (LEM)-based biological model of GdNP radiosensitization. Methods: The spectrum and yield of secondary electrons and dose enhancement around a single GdNP and clustered GdNPs were calculated in a water cube phantom by MC track-structure simulations using TOPAS code. We constructed a partial shell-like cell geometry model of pancreatic cancer cell based on transmission electron microscope (TEM) observations. LEM-based biological modeling of GdNP radiosensitization was established based on the MC-calculated nano-scale dose distributions in the cell model to predict the cell surviving fractions after irradiation. Results: The yield of secondary electrons for GdNP was 0.16% of the yield for gold nanoparticle (GNP), whereas the average electron energy was 12% higher. The majority of the dose enhancement came from the contribution of Auger electrons. GdNP clusters had a larger range and extent of dose enhancement than single GdNPs, although GdNP clustering reduced radial dose per interacting photon significantly. For the dose range between 0 and 8 Gy, the surviving fraction predicted using LEM-based biological model laid within one standard deviation of the published experimental results, and the deviations between them were all within 25%. Conclusions: The mechanism of radio-enhancement of GdNPs for kilovoltage photons was investigated using MC simulations. The prediction results of the established LEM-based biological model for GdNP radiosensitization showed good agreement with published experimental results, although the deviation of simulation parameters can lead to large disparity in the results. To our knowledge, this was the first LEM-based biological model for GdNP radiosensitization. [ABSTRACT FROM AUTHOR]

Active targeting gold nanoparticles (AuNPs) are a very promising avenue for cancer treatment with many publications on AuNP mediated radiosensitization at kilovoltage (kV) photon energies. However, uncertainty on the effectiveness of AuNPs under clinically relevant megavoltage (MV) radiation energies hinders the clinical translation of AuNP-assisted radiation therapy (RT) paradigm. The aim of this study was to investigate radiosensitization mediated by PSMA-targeted AuNPs irradiated by a 6 MV radiation beam at different depths to explore feasibility of AuNP-assisted prostate cancer RT under clinically relevant conditions. PSMA-targeted AuNPs (PSMA-AuNPs) were synthesized by conjugating PSMA antibodies onto PEGylated AuNPs through EDC/NHS chemistry. Confocal fluorescence microscopy was used to verify the active targeting of the developed PSMA-AuNPs. Transmission electron microscopy (TEM) was used to demonstrate the intracellular biodistribution of PSMA-AuNPs. LNCaP prostate cancer cells treated with PSMA-AuNPs were irradiated on a Varian 6 MV LINAC under varying depths (2.5 cm, 10 cm, 20 cm, 30 cm) of solid water. Clonogenic assays were carried out to determine the in vitro cell survival fractions. A Monte Carlo (MC) model developed on TOPAS platform was then employed to determine the nano-scale radial dose distribution around AuNPs, which was subsequently used to predict the radiation dose response of LNCaP cells treated with AuNPs. Two different cell models, with AuNPs located within the whole cell or only in the cytoplasm, were used to assess how the intracellular PSMA-AuNP biodistribution impacts the prostate cancer radiosensitization. Then, MC-based microdosimetry was combined with the local effect model (LEM) to calculate cell survival fraction, which was benchmarked against the in vitro clonogenic assays at different depths. In vitro clonogenic assay of LNCaP cells demonstrated the depth dependence of AuNP radiosensitization under clinical megavoltage beams, with sensitization enhancement ratio (SER) of 1.14 ± 0.03 and 1.55 ± 0.05 at 2.5 cm depth and 30 cm depth, respectively. The MC microdosimetry model showed the elevated percent of low-energy photons in the MV beams at greater depth, consequently resulting in increased dose enhancement ratio (DER) of AuNPs with depth. The AuNP-induced DER reached ~5.7 and ~8.1 at depths of 2.5 cm and 30 cm, respectively. Microdosimetry based LEM accurately predicted the cell survival under 6 MV beams at different depths, for the cell model with AuNPs placed only in the cell cytoplasm. TEM results demonstrated the distribution of PSMA-AuNPs in the cytoplasm, confirming the accuracy of MC microdosimetry based LEM with modelled AuNPs distributed within the cytoplasm. We conclude that AuNP radiosensitization can be achieved under megavoltage clinical radiotherapy energies with a dependence on tumor depth. Furthermore, the combination of Monte Carlo microdosimetry and LEM will be a valuable tool to assist with developing AuNP-aided radiotherapy paradigm and drive clinical translation. [ABSTRACT FROM AUTHOR]

Purpose: As local recurrence remains a challenge and the advantages of the simultaneous integrated boost (SIB) technique have been validated in photon radiotherapy, we applied the SIB technique to CIRT. The aim was to investigate the metabolomic changes of the CIRT with concurrent androgen deprivation therapy (ADT) in localized prostate cancer (PCa) and the unique metabolic effect of the SIB technique. Material and methods: This study enrolled 24 pathologically confirmed PCa patients. All patients went through CIRT with concurrent ADT. The gross target volume (GTV) boost was defined as positive lesions on both 68Ga-PSMA PET/CT and mpMRI images. Urine samples collected before and after CIRT were analyzed by the Q-TOF UPLC-MS/MS system. R platform and MetDNA were used for peak detection and identification. Statistical analysis and metabolic pathway analysis were performed on Metaboanalyst. Results: The metabolite profiles were significantly altered after CIRT. The most significantly altered metabolic pathway is PSMA participated alanine, aspartate and glutamate metabolism. Metabolites in this pathway showed a trend to be better suppressed in the SIB group. A total of 11 identified metabolites were significantly discriminative between two groups and all of them were better down-regulated in the SIB group. Meanwhile, among these metabolites, three metabolites in DNA damage and repair related purine metabolism were down-regulated to a greater extent in the SIB group. Conclusion: Metabolic dysfunction was one of the typical characteristics of PCa. CIRT with ADT showed a powerful inhibition of PCa metabolism, especially in PSMA participated metabolic pathway. The SIB CIRT showed even better performance on down-regulation of most metabolism than uniform-dose-distribution CIRT. Meanwhile, the SIB CIRT also showed its unique superiority to inhibit purine metabolism. PSMA PET/CT guided SIB CIRT showed its potentials to further benefit PCa patients. [ABSTRACT FROM AUTHOR]

BackgroundWe aimed at determining the safety and feasibility of spot-scanning carbon ion radiotherapy (CIRT) for patients with localized prostate cancer.MethodsWe enrolled 118 patients with localized prostate cancer who underwent treatment with spot-scanning CIRT at the Shanghai Proton and Heavy Ion Center (SPHIC) from January 2016 to December 2020. The dose was gradually increased from relative biological effectiveness (RBE)-weighted dose (DRBE) = 59.2–65.6 Gy in 16 fractions. The primary endpoint was the occurrence of acute and late toxicities, while the secondary endpoints were biochemical relapse-free survival (bRFS), distant metastasis-free survival (DMFS), prostate cancer-specific survival (PCSS), and overall survival (OS).ResultsThe median follow-up time was 30.2 months (4.8–62.7 months). Acute grade 1 and 2 genitourinary (GU) toxicities were 15.3% and 18.6%, while acute grade 1 and 2 gastrointestinal (GI) toxicities were 2.5% and 0%, respectively. Late grade 1 and 2 GU toxicities were 4.2% and 1.7%, respectively. No late GI toxicity was observed. Moreover, there were no cases of severe acute or late toxicity (≥ grade 3). No significant association were observed between the factors and the acute GU toxicities, except for clinical target volume (CTV) (p = 0.031) on multivariate analysis. The 2-year bRFS, DMFS, PCSS, and OS were 100%, 100%, 100%, and 98.8%, respectively.ConclusionThe 2-year outcomes were encouraging, providing additional and useful information on the feasibility and safety of spot-scanning CIRT for treating prostate cancer. Thus, we recommend long-term follow-up and prospective multicentered studies to reinforce the role of CIRT in the management of localized prostate cancer.

Background: We aimed at determining the safety and feasibility of spot-scanning carbon ion radiotherapy (CIRT) for patients with localized prostate cancer. Methods: We enrolled 118 patients with localized prostate cancer who underwent treatment with spot-scanning CIRT at the Shanghai Proton and Heavy Ion Center (SPHIC) from January 2016 to December 2020. The dose was gradually increased from relative biological effectiveness (RBE)-weighted dose (DRBE) = 59.2–65.6 Gy in 16 fractions. The primary endpoint was the occurrence of acute and late toxicities, while the secondary endpoints were biochemical relapse-free survival (bRFS), distant metastasis-free survival (DMFS), prostate cancer-specific survival (PCSS), and overall survival (OS). Results: The median follow-up time was 30.2 months (4.8–62.7 months). Acute grade 1 and 2 genitourinary (GU) toxicities were 15.3% and 18.6%, while acute grade 1 and 2 gastrointestinal (GI) toxicities were 2.5% and 0%, respectively. Late grade 1 and 2 GU toxicities were 4.2% and 1.7%, respectively. No late GI toxicity was observed. Moreover, there were no cases of severe acute or late toxicity (≥ grade 3). No significant association were observed between the factors and the acute GU toxicities, except for clinical target volume (CTV) (p = 0.031) on multivariate analysis. The 2-year bRFS, DMFS, PCSS, and OS were 100%, 100%, 100%, and 98.8%, respectively. Conclusion: The 2-year outcomes were encouraging, providing additional and useful information on the feasibility and safety of spot-scanning CIRT for treating prostate cancer. Thus, we recommend long-term follow-up and prospective multicentered studies to reinforce the role of CIRT in the management of localized prostate cancer. [ABSTRACT FROM AUTHOR]

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 αγ/βγ 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. [ABSTRACT FROM AUTHOR]

Background and PurposeThe 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 (DLEM I). However, brainstem constraints and a Normal Tissue Complication Probability (NTCP) model were recently reported for MKM-weighted dose (DMKM), showing that a constraint relaxation to DMKM|0.7 cm3

Gastric cancer ranks as one of the most common and poor prognosis human malignancies. We investigated the radioenhancer effects of gold, silver, platinum, bismuth, iron oxide, titanium oxide and germanium nanoparticles on simulated gastric adenocarcinoma cells, irradiated with 6 MV X-rays in presence of 0.05 µM, 0.25 µM and 0.5 µM homogeneous distributed metal nanoparticles throughout the cell, including the nucleus, cytoplasm and extracellular matrix using Geant4 Monte Carlo simulation along with LEM radiobiological model. The survival reduction coefficient was obtained 0.5 for light nanoparticles including 0.5 μM titanium oxide, iron oxide and germanium and also 0.3–0.1 for heavy nanoparticles including 0.5 μM gold, silver, bismuth and platinum. Nanoparticle-mediated Enhancement Ratio for 0.5 μM internuclear Gold, Silver, Bismuth, Germanium, Platinum, Iron oxide and Titanium oxide nanoparticles was calculated to 0.10, 0.27, 0.26, 0.47, 0.09, 0.50, and 0.58, respectively. Radiosensitization by NPs may enhance therapeutic efficacy of gastric adenocarcinoma. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: The dose‐averaged linear energy transfer (LETD) is frequently used as representative quantity for the biological effectiveness of a radiation field. Moreover, relative biological effectiveness (RBE) values measured or calculated in mixed radiation fields are typically plotted vs the LETD. In this study, we will investigate whether the LETD is an appropriate quantity to describe the RBE of any mixed radiation field of protons and heavier ions and discuss potential limitations. Methods: To study the reliability of LETD, we investigate model predictions of RBE in monoenergetic beams under track segment conditions and pristine Bragg peaks as well as spread out Bragg peaks (SOBP) in water. Both, the pristine Bragg peaks and the SOBPs are regarded as mixed radiation fields in this analysis, that is, they are characterized by a certain width of the energy spectrum of the projectile, although the underlying energy distribution is much broader in the case of an SOBP as compared to a pristine peak. For both cases, the corresponding RBE values are compared to those of strictly monoenergetic particles under track segment conditions, characterized by a single LET value. For the planning we use the treatment planning software TRiP98 together with the Local Effect Model to predict the RBE of protons, helium, and carbon ions. We further compare our model predictions for protons with a simplistic linear RBE‐LET relationship representative for the phenomenological models in literature. Results: Regarding pristine Bragg peaks in water, the deviations in RBE compared to monoenergetic particles under track segment conditions for the same LET value are low (mostly 0–5%), except for the distal fall‐off region. The situation changes in SOBPs for which we found deviations in the order of up to 25% for the lighter particles and even more pronounced deviations for heavier particles like carbon ions. Conclusions: The analysis showed that LETD is a sufficiently accurate predictor for RBE only in regions with comparably narrow, but not in regions with broad, LET distribution as in a single SOBP or in multiple overlapping fields. The deviations are caused by the nonlinearity of the RBE(LET) relationship in the case of track segment conditions. Thus, independent of the underlying RBE model and the particle type regarded, as long as the RBE(LET) relationship deviates from being purely linear, LETD is not a good predictor for RBE, and especially for heavier particles like carbon ions knowledge of the underlying LET distribution is mandatory to describe the RBE in mixed radiation fields. [ABSTRACT FROM AUTHOR]

Purpose To derive the dose–response curve for temporal lobe reactions (TLRs) after proton therapy and to compare the resulting relative biological effectiveness (RBE)-weighted tolerance doses based on an RBE of 1.1 with published values for carbon ions, which were calculated by the two versions of the local effect model (LEM I or IV). Methods and materials 62 patients treated with protons for skull base tumors were analyzed for TLRs using magnetic resonance imaging. Within the mean follow-up time of 38 months, TLRs were observed in six patients. Dose–response curves based on the RBE-weighted maximum dose, excluding the 1 cm 3 -volume with the highest dose, were derived and compared to previously published dose–response curves for carbon ions, which were obtained using LEM I or IV, respectively. Results The dose–response curves for protons and LEM I were found to be almost identical while the curve of LEM IV was shifted toward higher doses. The resulting tolerance doses at the 5% effect level were 68.2 + 2.7 - 5.6 , 68.6 + 3.0 - 3.9 and 78.3 + 3.8 - 5.0 Gy (RBE), respectively. Conclusions The RBE-weighted dose prescription for protons leads to the same RBE-weighted dose–response curve for TLR as the one for LEM I-based carbon ions, while LEM IV predicts clinically significant higher tolerance doses. [ABSTRACT FROM AUTHOR]

Purpose: To present details of the recent version of the 'Local Effect Model' (LEM), that has been developed and implemented in treatment planning for the ion beam therapy pilot project performed at GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, Germany. Materials and methods: The new version of the model is based on a detailed consideration of the spatial distribution of the initial damages, i.e., double-strand breaks (DSB). This spatial distribution of DSB is obtained from the radial dose profile of the ion track using Monte Carlo methods. These distributions are then analyzed with regard to the proximity of DSB. This version of the model also facilitates the calculation of full dose response curves up to arbitrary high doses, thus allowing to thoroughly check the approximations previously used to estimate the quadratic term (β-term) for the linear-quadratic description of dose response curves. Results: The accuracy of the model predictions is demonstrated by good agreement of the relative biological effectiveness (RBE) as a function of the linear energy transfer (LET) with experimental data obtained for V79 cells after carbon irradiation. The β-values predicted by the full simulation tend to be larger as compared to the approximation in the intermediate LET range. Conclusion: The new version of the model allows a more mechanistic description of the biological effects of ion radiation. The full simulation is a prerequisite for tests of the validity of the approach at high doses, which are of particular interest for application in hypofractionation studies. [ABSTRACT FROM AUTHOR]

Purpose: To establish the radiobiologic parameters of head-and-neck squamous cell carcinomas (HNSCC) in response to ion irradiation with various linear energy transfer (LET) values and to evaluate the relevance of the local effect model (LEM) in HNSCC. Methods and Materials: Cell survival curves were established in radiosensitive SCC61 and radioresistant SQ20B cell lines irradiated with [33.6 and 184 keV/n] carbon, [302 keV/n] argon, and X-rays. The results of ion experiments were confronted to LEM predictions. Results: The relative biologic efficiency ranged from 1.5 to 4.2 for SCC61 and 2.1 to 2.8 for SQ20B cells. Fixing an arbitrary D0 parameter, which characterized survival to X-ray at high doses (>10 Gy), gave unsatisfying LEM predictions for both cell lines. For D0 = 10 Gy, the error on survival fraction at 2 Gy amounted to a factor of 10 for [184 keV/n] carbon in SCC61 cells. We showed that the slope (s max) of the survival curve at high doses was much more reliable than D0. Fitting s max to 2.5 Gy−1 gave better predictions for both cell lines. Nevertheless, LEM could not predict the responses to fast and slow ions with the same accuracy. Conclusions: The LEM could predict the main trends of these experimental data with correct orders of magnitude while s max was optimized. Thus the efficiency of carbon ions cannot be simply extracted from the clinical response of a patient to X-rays. LEM should help to optimize planning for hadrontherapy if a set of experimental data is available for high-LET radiations in various types of tumors. [Copyright &y& Elsevier]

In vitro experiments show significant reduction in the survival fraction of cells under irradiation treatments assisted with high- Z nanoparticles (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., (© 2021 Institute of Physics and Engineering in Medicine.)

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.

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.