Dose painting by dynamic irradiation delivery on an image-guided small animal radiotherapy platform

Understanding the impact of radiation-induced lymphopenia: Preclinical and clinical research perspectives

Immunotherapy has revolutionized the field of cancer treatment, changing the standard of care to the use of immune checkpoint inhibitors. Radiotherapy can boost anti-tumour immune responses by changing the tumour microenvironment, but it also can cause radiotherapy-induced lymphopenia (RIL), a decrease in circulating lymphocyte counts. RIL has been associated with lower survival in patients undergoing radiotherapy, and new studies have suggested that it can also affect immunotherapy outcome. To study RIL's effects and to explore mitigation treatment strategies, preclinical models closely mimicking the clinical situation are needed. State-of-the-art image-guided small animal irradiators now offer the possibility to target specific organs in small animals to induce RIL, aiding research on its molecular mechanisms and prevention. This review covers the relationship between radiotherapy and RIL, its impact on patient survival, and future directions to generate models to investigate and prevent RIL.

Absorbed dose calculation for a realistic CT-derived mouse phantom irradiated with a standard Cs-137 cell irradiator using a Monte Carlo method

Computed tomography (CT) derived Monte Carlo (MC) phantoms allow dose determination within small animal models that is not feasible with in-vivo dosimetry. The aim of this study was to develop a CT-derived MC phantom generated from a mouse with a xenograft tumour that could then be used to calculate both the dose heterogeneity in the tumour volume and out of field scattered dose for pre-clinical small animal irradiation experiments. A BEAMnrc Monte-Carlo model has been built of our irradiation system that comprises a lead collimator with a 1 cm diameter aperture fitted to a Cs-137 gamma irradiator. The MC model of the irradiation system was validated by comparing the calculated dose results with dosimetric film measurement in a polymethyl methacrylate (PMMA) phantom using a 1D gamma-index analysis. Dose distributions in the MC mouse phantom were calculated and visualized on the CT-image data. Dose volume histograms (DVHs) were generated for the tumour and organs at risk (OARs). The effect of the xenographic tumour volume on the scattered out of field dose was also investigated. The defined gamma index analysis criteria were met, indicating that our MC simulation is a valid model for MC mouse phantom dose calculations. MC dose calculations showed a maximum out of field dose to the mouse of 7% of Dmax. Absorbed dose to the tumour varies in the range 60%-100% of Dmax. DVH analysis demonstrated that tumour received an inhomogeneous dose of 12 Gy-20 Gy (for 20 Gy prescribed dose) while out of field doses to all OARs were minimized (1.29 Gy-1.38 Gy). Variation of the xenographic tumour volume exhibited no significant effect on the out of field scattered dose to OARs. The CT derived MC mouse model presented here is a useful tool for tumour dose verifications as well as investigating the doses to normal tissue (in out of field) for preclinical radiobiological research.

An efficient rectangular optimization method for sparse orthogonal collimator based small animal irradiation

Objective.Intensity-modulated radiotherapy (IMRT) is widely used in clinical radiotherapy, treating varying malignancies with conformal doses. As the test field for clinical translation, preclinical small animal experiments need to mimic the human radiotherapy condition, including IMRT. However, small animal IMRT is a systematic challenge due to the lack of corresponding hardware and software for miniaturized targets.Approach.The sparse orthogonal collimators (SOC) based on the direct rectangular aperture optimization (RAO) substantially simplified the hardware for miniaturization. This study investigates and evaluates a significantly improved RAO algorithm for complex mouse irradiation using SOC. Because the Kronecker product representation of the rectangular aperture is the main limitation of the computational performance, we reformulated matrix multiplication in the data fidelity term using multiplication with small matrices instead of the Kronecker product of the dose loading matrices. Solving the optimization problem was further accelerated using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA).Main results.Four mouse cases, including a liver, a brain tumor, a concave U-target, and a complex total marrow irradiation (TMI) case, were included in this study with manually delineated targets and OARs. Seven coplanar-field SOC IMRT (sIMRT) plans were compared with idealistic fluence map based IMRT (iIMRT) plans. For the first three cases with simpler and smaller targets, the differences between sIMRT plans and iIMRT plans in the planning target volumes (PTV) statistics are within 1%. For the TMI case, the sIMRT plans are superior in reducing hot spots (also termedDmax) of PTV, kidneys, lungs, heart, and bowel by 20.5%, 31.5%, 24.67%, 20.13%, and 17.78%, respectively. On average, in four cases in this study, the sIMRT plan conformity is comparable to that of the iIMRT's with lightly increased R50 and Integral Dose by 2.23% and 2.78%.Significance.The significantly improved sIMRT optimization method allows fast plan creation in under 1 min for smaller targets and makes complex TMI planning feasible while achieving comparable dosimetry to idealistic IMRT with fluence map optimization.

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications

Objective. In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline. Approach. A deep learning model is proposed that denoises low precision (∼106 simulated particles) dose distributions to produce high precision (109 simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well. Main results. The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average. Significance. This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Small Animal IMRT Using 3D-Printed Compensators

PURPOSE

Preclinical radiation replicating clinical intensity modulated radiation therapy (IMRT) techniques can provide data translatable to clinical practice. For this work, treatment plans were created for oxygen-guided dose-painting in small animals using inverse-planned IMRT. Spatially varying beam intensities were achieved using 3-dimensional (3D)-printed compensators.

METHODS AND MATERIALS

Optimized beam fluence from arbitrary gantry angles was determined using a verified model of the XRAD225Cx treatment beam. Compensators were 3D-printed with varied thickness to provide desired attenuation using copper/polylactic-acid. Spatial resolution capabilities were investigated using printed test-patterns. Following American Association of Physicists in Medicine TG119, a 5-beam IMRT plan was created for a miniaturized (∼1/8th scale) C-shape target. Electron paramagnetic resonance imaging of murine tumor oxygenation guided simultaneous integrated boost (SIB) plans conformally treating tumor to a base dose (Rx₁) with boost (Rx₂) based on tumor oxygenation. The 3D-printed compensator intensity modulation accuracy and precision was evaluated by individually delivering each field to a phantom containing radiochromic film and subsequent per-field gamma analysis. The methodology was validated end-to-end with composite delivery (incorporating 3D-printed tungsten/polylactic-acid beam trimmers to reduce out-of-field leakage) of the oxygen-guided SIB plan to a phantom containing film and subsequent gamma analysis.

RESULTS

Resolution test-patterns demonstrate practical printer resolution of ∼0.7 mm, corresponding to 1.0 mm bixels at the isocenter. The miniaturized C-shape plan provides planning target volume coverage (V_95% = 95%) with organ sparing (organs at risk D_max < 50%). The SIB plan to hypoxic tumor demonstrates the utility of this approach (hypoxic tumor V_95%,Rx2 = 91.6%, normoxic tumor V_95%,Rx1 = 95.7%, normal tissue V_100%,Rx1 = 7.1%). The more challenging SIB plan to boost the normoxic tumor rim achieved normoxic tumor V_95%,Rx2 = 90.9%, hypoxic tumor V_95%,Rx1 = 62.7%, and normal tissue V_100%,Rx2 = 5.3%. Average per-field gamma passing rates using 3%/1.0 mm, 3%/0.7 mm, and 3%/0.5 mm criteria were 98.8% ± 2.8%, 96.6% ± 4.1%, and 90.6% ± 5.9%, respectively. Composite delivery of the hypoxia boost plan and gamma analysis (3%/1 mm) gave passing results of 95.3% and 98.1% for the 2 measured orthogonal dose planes.

CONCLUSIONS

This simple and cost-effective approach using 3D-printed compensators for small-animal IMRT provides a methodology enabling preclinical studies that can be readily translated into the clinic. The presented oxygen-guided dose-painting demonstrates that this methodology will facilitate studies driving much needed biologic personalization of radiation therapy for improvements in patient outcomes.

Radiotherapy dose painting by circadian rhythm based radiomics

Radiotherapy dose painting is a new dose delivery technique to achieve higher treatment outcome. In this approach, does is escalated to high progressive regions which are heterogeneous and determined by advanced medical imaging. Radiomics is issued as a feasible image quantification method to reveal tumor heterogeneity by extraction of high throughput mineable texture features. On the other hand, circadian rhythm is a given biological process that studied as a critical factor to obtain more effective treatment outcome. In this study, we hypothesized that radiotherapy dose painting could be enhanced by using circadian rhythm that is determined on the radiomics maps obtained from medical images. This hypothesis is based on the idea which circadian rhythm could change the tumor heterogeneity and therefore image features.

Evolution of the Supermodel: Progress in Modelling Radiotherapy Response in Mice

Mouse models are essential tools in cancer research that have been used to understand the genetic basis of tumorigenesis, cancer progression and to test the efficacies of anticancer treatments including radiotherapy. They have played a critical role in our understanding of radiotherapy response in tumours and normal tissues and continue to evolve to better recapitulate the underlying biology of humans. In addition, recent developments in small animal irradiators have significantly improved in vivo irradiation techniques, allowing previously unimaginable experimental approaches to be explored in the laboratory. The combination of contemporary mouse models with small animal irradiators represents a major step forward for the radiobiology field in being able to much more accurately replicate clinical exposure scenarios. As radiobiology studies become ever more sophisticated in reflecting developments in the clinic, it is increasingly important to understand the basis and potential limitations of extrapolating data from mice to humans. This review provides an overview of mouse models and small animal radiotherapy platforms currently being used as advanced radiobiological research tools towards improving the translational power of preclinical studies.