Superior antitumor immune response achieved with proton over photon immunoradiotherapy is amplified by the nanoradioenhancer NBTXR3.

Recent findings suggest that immunoradiotherapy (IRT), combining photon radiotherapy (XRT) or proton radiotherapy (PRT) with immune checkpoint blockade, can enhance systemic tumor control. However, the comparative efficacy of XRT and PRT in IRT remains understudied. To address this, we compared outcomes between XRT + αPD1 and PRT + αPD1 in murine αPD1-resistant lung cancer (344SQR). We also assessed the impact of the nanoparticle radioenhancer NBTXR3 on both XRT + αPD1 and PRT + αPD1 for tumor control and examined the tumor immune microenvironment using single-cell RNA sequencing (scRNAseq). Additionally, mice cured by NBTXR3 + PRT + αPD1 were rechallenged with three lung cancer cell lines to evaluate memory antitumor immunity. PRT + αPD1 showed superior local tumor control and abscopal effects compared to XRT + αPD1. NBTXR3 + PRT + αPD1 significantly outperformed NBTXR3 + XRT + αPD1 in tumor control, promoting greater infiltration of antitumor lymphocytes into irradiated tumors. Unirradiated tumors treated with NBTXR3 + PRT + αPD1 had more NKT cells, CD4 T cells, and B cells, with fewer Tregs, than those treated with NBTXR3 + XRT + αPD1. NBTXR3 + PRT + αPD1 also stimulated higher expression of IFN-γ, GzmB, and Nkg7 in lymphocytes, reduced the TGF-ß pathway, and increased tumor necrosis factor alpha expression compared to NBTXR3 + XRT + αPD1. Moreover, NBTXR3 + PRT + αPD1 resulted in greater M1 macrophage polarization in both irradiated and unirradiated tumors. Mice achieving remission through NBTXR3 + PRT + αPD1 exhibited a robust memory immune response, effectively inhibiting growth of subsequent tumors from three distinct lung cancer cell lines. Proton IRT combined with NBTXR3 offers enhanced tumor control and survival rates over photon-based treatments in managing αPD1-resistant lung cancer, indicating its potential as a potent systemic therapy.

Nanoparticle-enhanced proton beam immunoradiotherapy drives immune activation and durable tumor rejection.

The combination of radiation therapy (RT) and immunotherapy has emerged as a promising treatment option in oncology. Historically, x-ray radiation (XRT) has been the most commonly used form of RT. However, proton beam therapy (PBT) is gaining recognition as a viable alternative, as it has been shown to produce similar outcomes to XRT while minimizing off-target effects. The effects of PBT on the antitumor immune response have only just begun to be described, and to our knowledge no studies to date have examined the effect of PBT as part of a combinatorial immunoradiotherapeutic strategy. Here, using a 2-tumor model of lung cancer in mice, we show that PBT in tandem with an anti-PD1 antibody substantially reduced growth in both irradiated and unirradiated tumors. This was accompanied by robust activation of the immune response, as evidenced by whole-tumor and single-cell RNA sequencing showing upregulation of a multitude of immune-related transcripts. This response was further significantly enhanced by the injection of the tumor to be irradiated with NBTXR3 nanoparticles. Tumors of mice treated with the triple combination exhibited increased infiltration and activation of cytotoxic immune cells. This triple combination eradicated both tumors in 37.5% of the treated mice and showed robust long-term immunity to cancer.

Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.

BACKGROUND: MR scans used in radiotherapy can be partially truncated due to the limited field of view (FOV), affecting dose calculation accuracy in MR-based radiation treatment planning. PURPOSE: We proposed a novel Compensation-cycleGAN (Comp-cycleGAN) by modifying the cycle-consistent generative adversarial network (cycleGAN), to simultaneously create synthetic CT (sCT) images and compensate the missing anatomy from the truncated MR images. METHODS: Computed tomography (CT) and T1 MR images with complete anatomy of 79 head-and-neck patients were used for this study. The original MR images were manually cropped 10-25 mm off at the posterior head to simulate clinically truncated MR images. Fifteen patients were randomly chosen for testing and the rest of the patients were used for model training and validation. Both the truncated and original MR images were used in the Comp-cycleGAN training stage, which enables the model to compensate for the missing anatomy by learning the relationship between the truncation and known structures. After the model was trained, sCT images with complete anatomy can be generated by feeding only the truncated MR images into the model. In addition, the external body contours acquired from the CT images with full anatomy could be an optional input for the proposed method to leverage the additional information of the actual body shape for each test patient. The mean absolute error (MAE) of Hounsfield units (HU), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between sCT and real CT images to quantify the overall sCT performance. To further evaluate the shape accuracy, we generated the external body contours for sCT and original MR images with full anatomy. The Dice similarity coefficient (DSC) and mean surface distance (MSD) were calculated between the body contours of sCT and original MR images for the truncation region to assess the anatomy compensation accuracy. RESULTS: The average MAE, PSNR, and SSIM calculated over test patients were 93.1 HU/91.3 HU, 26.5 dB/27.4 dB, and 0.94/0.94 for the proposed Comp-cycleGAN models trained without/with body-contour information, respectively. These results were comparable with those obtained from the cycleGAN model which is trained and tested on full-anatomy MR images, indicating the high quality of the sCT generated from truncated MR images by the proposed method. Within the truncated region, the mean DSC and MSD were 0.85/0.89 and 1.3/0.7 mm for the proposed Comp-cycleGAN models trained without/with body contour information, demonstrating good performance in compensating the truncated anatomy. CONCLUSIONS: We developed a novel Comp-cycleGAN model that can effectively create sCT with complete anatomy compensation from truncated MR images, which could potentially benefit the MRI-based treatment planning.

Optimization of FLASH proton beams using a track-repeating algorithm.

BACKGROUND: Radiation with high dose rate (FLASH) has shown to reduce toxicities to normal tissues around the target and maintain tumor control with the same amount of dose compared to conventional radiation. This phenomenon has been widely studied in electron therapy, which is often used for shallow tumor treatment. Proton therapy is considered a more suitable treatment modality for deep-seated tumors. The feasibility of FLASH proton therapy has recently been demonstrated by a series of pre- and clinical trials. One of the challenges is to efficiently generate wide enough dose distributions in both lateral and longitudinal directions to cover the entire tumor volume. The goal of this paper is to introduce a set of automatic FLASH proton beam optimization algorithms developed recently. PURPOSE: To develop a fast and efficient optimizer for the design of a passive scattering proton FLASH radiotherapy delivery at The University of Texas MD Anderson Proton Therapy Center, based on the fast dose calculator (FDC). METHODS: A track-repeating algorithm, FDC, was validated versus Geant4 simulations and applied to calculate dose distributions in various beamline setups. The design of the components was optimized to deliver homogeneous fields with well-defined diameters between 11.0 and 20.5 mm, as well as a spread-out Bragg peak (SOBP) with modulations between 8.5 and 39.0 mm. A ridge filter, a high-Z material scatterer, and a collimator with range compensator were inserted in the beam path, and their shapes and sizes were optimized to spread out the Bragg peak, widen the beam, and reduce the penumbra. The optimizer was developed and tested using two proton energies (87.0 and 159.5 MeV) in a variety of beamline arrangements. Dose rates of the optimized beams were estimated by scaling their doses to those of unmodified beams. RESULTS: The optimized 87.0-MeV beams, with a distance from the beam pipe window to the phantom surface (window-to-surface distance [WSD]) of 550 mm, produced an 8.5-mm-wide SOBP (proximal 90% to distal 90% of the maximum dose); 14.5, 12.0, and 11.0-mm lateral widths at the 50%, 80%, and 90% dose location, respectively; and a 2.5-mm penumbra from 80% to 20% in the lateral profile. The 159.5-MeV beam had an SOBP of 39.0 mm and lateral widths of 20.5, 15.0, and 12.5 mm at 50%, 80%, and 90% dose location, respectively, when the WSD was 550 mm. Wider lateral widths were obtained with increased WSD. The SOBP modulations changed when the ridge filters with different characteristics were inserted. Dose rates on the beam central axis for all optimized beams (other than the 87.0-MeV beam with 2000-mm WSD) were above that needed for the FLASH effect threshold (40 Gy/s) except at the very end of the depth dose profile scaling with a dose rate of 1400 Gy/s at the Bragg peak in the unmodified beams. The optimizer was able to instantly design the individual beamline components for each of the beamline setups, without the need of time intensive iterative simulations. CONCLUSION: An efficient system, consisting of an optimizer and an FDC have been developed and validated in a variety of beamline setups, comprising two proton energies, several WSDs, and SOBPs. The set of automatic optimization algorithms produces beam shaping element designs efficiently and with excellent quality.

An empirical model of proton RBE based on the linear correlation between x-ray and proton radiosensitivity.

BACKGROUND: Proton relative biological effectiveness (RBE) is known to depend on physical factors of the proton beam, such as its linear energy transfer (LET), as well as on cell-line specific biological factors, such as their ability to repair DNA damage. However, in a clinical setting, proton RBE is still considered to have a fixed value of 1.1 despite the existence of several empirical models that can predict proton RBE based on how a cell's survival curve (linear-quadratic model [LQM]) parameters α and ß vary with the LET of the proton beam. Part of the hesitation to incorporate variable RBE models in the clinic is due to the great noise in the biological datasets on which these models are trained, often making it unclear which model, if any, provides sufficiently accurate RBE predictions to warrant a departure from RBE = 1.1. PURPOSE: Here, we introduce a novel model of proton RBE based on how a cell's intrinsic radiosensitivity varies with LET, rather than its LQM parameters. METHODS AND MATERIALS: We performed clonogenic cell survival assays for eight cell lines exposed to 6 MV x-rays and 1.2, 2.6, or 9.9 keV/µm protons, and combined our measurements with published survival data (n = 397 total cell line/LET combinations). We characterized how radiosensitivity metrics of the form DSF% , (the dose required to achieve survival fraction [SF], e.g., D10% ) varied with proton LET, and calculated the Bayesian information criteria associated with different LET-dependent functions to determine which functions best described the underlying trends. This allowed us to construct a six-parameter model that predicts cells' proton survival curves based on the LET dependence of their radiosensitivity, rather than the LET dependence of the LQM parameters themselves. We compared the accuracy of our model to previously established empirical proton RBE models, and implemented our model within a clinical treatment plan evaluation workflow to demonstrate its feasibility in a clinical setting. RESULTS: Our analyses of the trends in the data show that DSF% is linearly correlated between x-rays and protons, regardless of the choice of the survival level (e.g., D10% , D37% , or D50% are similarly correlated), and that the slope and intercept of these correlations vary with proton LET. The model we constructed based on these trends predicts proton RBE within 15%-30% at the 68.3% confidence level and offers a more accurate general description of the experimental data than previously published empirical models. In the context of a clinical treatment plan, our model generally predicted higher RBE-weighted doses than the other empirical models, with RBE-weighted doses in the distal portion of the field being up to 50.7% higher than the planned RBE-weighted doses (RBE = 1.1) to the tumor. CONCLUSIONS: We established a new empirical proton RBE model that is more accurate than previous empirical models, and that predicts much higher RBE values in the distal edge of clinical proton beams.

Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning.

PURPOSE: Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles. METHODS: A single-institution retrospective data of 746 patients with esophageal cancer treated with photons (n = 500) and protons (n = 246) was reviewed. The primary end point of our study was G4RIL. Clustering techniques were applied to identify patient subpopulations with similar pretreatment clinical and radiation dosimetric characteristics. XGBoost was built on a training set (n = 499) to predict G4RIL risks. Predictive performance was assessed on the remaining n = 247 patients. SHapley Additive exPlanations were used to rank the importance of individual predictors. Counterfactual analyses compared patients' risk profiles assuming that they had switched modalities. RESULTS: Baseline absolute lymphocyte count and volumes of lung and spleen receiving ≥ 15 and ≥ 5 Gy, respectively, were the most important G4RIL risk determinants. The model achieved sensitivitytesting-set 0.798 and specificitytesting-set 0.667 with an area under the receiver operating characteristics curve (AUCtesting-set) of 0.783. The G4RIL risk for an average patient receiving protons increased by 19% had the patient switched to photons. Reductions in G4RIL risk were maximized with proton therapy for patients with older age, lower baseline absolute lymphocyte count, and higher lung and heart dose. CONCLUSION: G4RIL risk varies for individual patients with esophageal cancer and is modulated by radiotherapy dosimetric parameters. The framework for machine learning presented can be applied broadly to study risk determinants of other adverse events, providing the basis for adapting treatment strategies for mitigation.

Technical Note: Field size analysis of patient-specific quality assurance in scanned carbon ion radiotherapy.

PURPOSE: To evaluate the dose difference between measurement and double Gaussian beam model prediction according to the field size and correct the measurements in patient-specific quality assurance (QA). METHODS: The field size dependence of the dose was evaluated with volumes of 20 × 20 × 80 mm3 , 40 × 40 × 80 mm3 , 60 × 60 × 80 mm3 , and 80 × 80 × 80 mm3 of 1 Gy uniform dose at three depths. Additional two 80 × 80 × 80 mm3 volumes of nonuniform fields were created: one high-dose field was given 1 Gy at the central 40 × 40 mm2 and 0.5 Gy in its surrounding, and the other low-dose field was given 0.5 Gy in the middle and 1 Gy at the periphery. The dose in the center of the spread-out Bragg peak (SOBP) was measured in a water phantom and compared with the treatment planning system (TPS) predication. A field factor based on the two-dimensional (2D) dose distribution was proposed to estimate the field size. The field factor was first evaluated against the dose difference in the square fields, and then used to analyze and correct the patient-specific QA results. RESULTS: TPS overestimated dose for fields smaller than 80 × 80 mm2 . A practically positive correlation was observed between the measured dose and the field factor. In the patient-specific QA, measured doses were lower than the TPS predication as they were calculated a relatively small field factor. The corrected dose differences were no longer field factor dependent. CONCLUSIONS: Using the proposed field factor, we have shown that all the measurements with a large dose deviation were due to the small-sized field. It is clinically relevant to take into consideration the field size in the QA analysis as long as the double Gaussian beam model being used for the dose calculation. Correction to the measurement can be made based on the field factor.

The effect of denture restoration and dental implant restoration in the treatment of dentition defect: a systematic review and meta-analysis.

BACKGROUND: Dentition defect is a common symptom in clinical dental patients. This study compared the clinical effects of denture restoration and dental implant restoration in the treatment of dentition defects through meta-analysis. METHODS: Data retrieval was conducted through the PubMed, Web of Science, Embase, CNKI, and Wanfang databases. A total of 479 related literatures published in English or Chinese from 2013 to 2020 were included. Literature screening, data extraction and comprehensive evaluation, and analysis by meta-analysis was performed by 3 authors. RESULTS: A total of 17 studies and 1,459 patients were included. Among the 17 studies, the effective rate of treatment between the two groups was compared and the experimental group rate was significantly higher than that of the control group [odds ratio (OR) =6.149, 95% confidence interval (CI): 4.103-9.215, P<0.001]; the mastication function score was compared, and was higher in the experimental group than in the control group [standardized mean difference (SMD) =1.632, 95% CI: 1.039-2.224, P<0.001]; the retention function score was compared, and was higher in the experimental group than in the control group (SMD =1.775, 95% CI: 1.095-2.455), P<0.001); the aesthetics score was also compared, and was higher in the experimental group than in the control group (SMD =1.300, 95% CI: 0.499-2.100, P=0.001). Among 17 studies, 15 compared the comfort score, which was higher in the experimental group than in the control group (SMD =1.357, 95% CI: 0.455-2.258, P=0.003). CONCLUSIONS: Compared with denture restoration, dental implant restoration is more effective in the treatment of dentition defect with a higher comprehensive score of functional restoration.

Automatic phase space generation for Monte Carlo calculations of intensity modulated particle therapy.

Monte Carlo (MC) is generally considered as the most accurate dose calculation tool for particle therapy. However, a proper description of the beam particle kinematics is a necessary input for a realistic simulation. Such a description can be stored in phase space (PS) files for different beam energies. A PS file contains kinetic information such as energies, positions and travelling directions for particles traversing a plane perpendicular to the beam direction. The accuracy of PS files plays a critical role in the performance of the MC method for dose calculations. A PS file can be generated with a set of parameters describing analytically the beam kinematics. However, determining such parameters can be tedious and time consuming. Thus, we have developed an algorithm to obtain those parameters automatically and efficiently. In this paper, we presented such an algorithm and compared dose calculations using PS automatically generated for the Shanghai Proton and Heavy Ion Center (SPHIC) with measurements. The gamma-index for comparing calculated depth dose distributions (DDD) with measurements are above 96.0% with criterion 0.6%/0.6 mm. For each single energy, the mean difference percentage between calculated lateral spot sizes at 5 different locations along beam direction and measurements are below 3.5%.

A track repeating algorithm for intensity modulated carbon ion therapy.

The fast dose calculator (FDC), a track repeating Monte Carlo (MC) algorithm was initially developed for proton therapy. The validation for proton therapy has been demonstrated in a previous work. In this work we presented the extension of FDC to the calculation of dose distributions for ions, particularly for carbon. Moreover the code algorithm is validated by comparing 3D dose distributions and dose volume histograms (DVH) calculated by FDC with Geant4. A total of 19 patients were employed, including three patients of prostate, five of brain, three of head and neck, four of lung and four of spine. We used a gamma-index technique to analyze dose distributions and we performed a dosimetric analysis for DVHs, a more direct and informative quantity for planning system assessment. The gamma-index passing rates of all patients discussed in this paper are above 90% with the criterion 1%/1 mm, above 98% with the criterion 2%/2 mm and over 99.9% with the criterion 3%/3 mm. The root mean square (RMS) of percent difference of dosimetric indices D 02, D 05, D 50, D 95 and D 98 are 0.75%, 0.70%, 0.79%, 0.83% and 0.76%. All the differences are within clinically accepted norms.

Fixed- versus Variable-RBE Computations for Intensity Modulated Proton Therapy.

PURPOSE: To evaluate how using models of proton therapy that incorporate variable relative biological effectiveness (RBE) versus the current practice of using a fixed RBE of 1.1 affects dosimetric indices on treatment plans for large cohorts of patients treated with intensity modulated proton therapy (IMPT). METHODS AND MATERIALS: Treatment plans for 4 groups of patients who received IMPT for brain, head-and-neck, thoracic, or prostate cancer were selected. Dose distributions were recalculated in 4 ways: 1 with a fast-dose Monte Carlo calculator with fixed RBE and 3 with RBE calculated to 3 different models-McNamara, Wedenberg, and repair-misrepair-fixation. Differences among dosimetric indices (D02, D50, D98, and mean dose) for target volumes and organs at risk (OARs) on each plan were compared between the fixed-RBE and variable-RBE calculations. RESULTS: In analyses of all target volumes, for which the main concern is underprediction or RBE less than 1.1, none of the models predicted an RBE less than 1.05 for any of the cohorts. For OARs, the 2 models based on linear energy transfer, McNamara and Wedenberg, systematically predicted RBE >1.1 for most structures. For the mean dose of 25% of the plans for 2 OARs, they predict RBE equal to or larger than 1.4, 1.3, 1.3, and 1.2 for brain, head-and-neck, thorax, and prostate, respectively. Systematically lower increases in RBE are predicted by repair-misrepair-fixation, with a few cases (eg, femur) in which the RBE is less than 1.1 for all plans. CONCLUSIONS: The variable-RBE models predict increased doses to various OARs, suggesting that strategies to reduce high-dose linear energy transfer in critical structures should be developed to minimize possible toxicity associated with IMPT.

Comparison of Monte Carlo and analytical dose computations for intensity modulated proton therapy.

To evaluate the effect of approximations in clinical analytical calculations performed by a treatment planning system (TPS) on dosimetric indices in intensity modulated proton therapy. TPS calculated dose distributions were compared with dose distributions as estimated by Monte Carlo (MC) simulations, calculated with the fast dose calculator (FDC) a system previously benchmarked to full MC. This study analyzed a total of 525 patients for four treatment sites (brain, head-and-neck, thorax and prostate). Dosimetric indices (D02, D05, D20, D50, D95, D98, EUD and Mean Dose) and a gamma-index analysis were utilized to evaluate the differences. The gamma-index passing rates for a 3%/3 mm criterion for voxels with a dose larger than 10% of the maximum dose had a median larger than 98% for all sites. The median difference for all dosimetric indices for target volumes was less than 2% for all cases. However, differences for target volumes as large as 10% were found for 2% of the thoracic patients. For organs at risk (OARs), the median absolute dose difference was smaller than 2 Gy for all indices and cohorts. However, absolute dose differences as large as 10 Gy were found for some small volume organs in brain and head-and-neck patients. This analysis concludes that for a fraction of the patients studied, TPS may overestimate the dose in the target by as much as 10%, while for some OARs the dose could be underestimated by as much as 10 Gy. Monte Carlo dose calculations may be needed to ensure more accurate dose computations to improve target coverage and sparing of OARs in proton therapy.