Determining RBE for development of lung fibrosis induced by fractionated irradiation with carbon ions utilizing fibrosis index and high-LET BED model

Mechanistic model of radiotherapy-induced lung fibrosis using coupled 3D agent-based and Monte Carlo simulations

BACKGROUND

Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.

METHODS

In this work, we develop and characterize a coupled 3D agent-based - Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.

RESULTS

Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.

CONCLUSIONS

This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.

Gene Signatures for Latent Radiation-Induced Lung Injury Post X-ray Exposure in Mouse

Objective

To investigate the X-ray-specific sensitive genes and potential signaling pathways involved in the latent period of radiation-induced lung injury (RILI) in mouse models.

Method

Mice were randomized into groups for whole thoracic irradiation with a single fraction of 20 Gy X-ray or 12.5 Gy carbon heavy ion. Lungs were harvested 3 weeks after the irradiation, whole RNA was extracted and detected with the genome-wide transcriptional microarrays. Differentially expressed genes (DEGs) were calculated for each group and the X-ray-specific sensitive genes were determined, followed by the gene enrichment analysis of those DEGs exploring the potentially relevant signaling pathways and biological processes in latent RILI.

Results

Three weeks after irradiation, gene expression levels varied between groups. 76 up-regulated DEGs were determined with mice in the X-ray group and gene ontology enrichment analysis for biological process (GO-BP) obtained several processes which were associated with radiation reaction, mitotic, immune cell chemotaxis or metastasis, immune factors, p53 apoptosis, and tissue remodeling. KEGG signaling pathway enrichment analysis showed that those 76 up-regulated DEGs were enriched in p53, IL-17, FoXO, melanoma, and non-small-cell lung cancer signaling pathways. By comparing the DEGs in X-ray and heavy ion groups, X-ray-specific sensitive genes were determined, the top 10 genes were Adamts9, Aacs, Col6a2, Fdps, Mdk, Mcam, Stbd1, Lbh, Ak3, and Emid1. The expression level of the top 10 genes was found to be significantly higher in the X-ray group than in the control and heavy ion groups.

Conclusion

Our research determined the X-ray-specific sensitive gene set in mice lungs after exposure to radiation. The gene set could be used as a genetic marker to suggest the latency of RILI. The enrichment analysis results suggested that the relevant signaling pathways were potentially involved in the development of RILI. Further validation of those genes and signaling pathways is needed to confirm these findings.

Dose- and LET-dependent changes in mouse skin contracture up to a year after either single dose or fractionated doses of carbon ion or gamma rays

Time dependence of relative biological effectiveness (RBE) of carbon ions for skin damage was investigated to answer the question of whether the flat distribution of biological doses within a Spread-Out Bragg peak (SOBP) which is designed based on in vitro cell kill could also be flat for in vivo late responding tissue. Two spots of Indian ink intracutaneously injected into the legs of C3H mice were measured by calipers. An equieffective dose to produce 30% skin contraction was calculated from a dose-response curve and used to calculate the RBE of carbon ion beams. We discovered skin contraction progressed after irradiation and then reached a stable/slow progression phase. Equieffective doses decreased with time and the decrease was most prominent for gamma rays and least prominent for 100 keV/μm carbon ions. Survival parameter of alpha but not beta in the linear-quadratic model is closely related to the RBE of carbon ions. Biological doses within the SOBP increased with time but their distribution was still flat up to 1 year after irradiation. The outcomes of skin contraction studies suggest that (i) despite the higher RBE for skin contracture after carbon ions compared to gamma rays, gamma rays can result in a more severe late effect of skin contracture. This is due to the carbon effect saturating at a lower dose than gamma rays, and (ii) the biological dose distribution throughout the SOBP remains approximately the same even one year after exposure.

Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice

PURPOSE

To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions.

MATERIALS AND METHODS

In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n = 1200), validated (n = 300), and tested (n = 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n = 20; group C [n = 16 for training and n = 4 for testing]).

RESULTS

The trained model yielded a high median DSC in both test datasets: 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. The median HD in both test datasets was 0.47 mm (IQR, 0-0.51 mm [group A]) and 0.31 mm (IQR, 0.30-0.32 mm [group B]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation. Finally, for the higher-resolution mouse CT images, the median DSC was 0.905 (IQR, 0.902-0.929) and the median 95th percentile of the HD was 0.33 mm (IQR, 2.61-2.78 mm).

CONCLUSION

The developed deep learning-based method for mouse lung segmentation performed well independently of disease state (healthy, fibrotic, emphysematous lungs) and CT resolution.Keywords: Deep Learning, Lung Fibrosis, Radiation Therapy, Segmentation, Animal Studies, CT, Thorax, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.

Physics of Particle Beam and Hypofractionated Beam Delivery in NSCLC

The dosimetric advantages of particle therapy lead to significantly reduced integral dose to normal tissues, making it an attractive treatment option for body sites such as the thorax. With reduced normal tissue dose comes the potential for dose escalation, toxicity reduction, or hypofractionation. While proton and heavy ion therapy have been used extensively for NSCLC, there are challenges in planning and delivery compared with X-ray-based radiation therapy. Particularly, range uncertainties compounded by breathing motion have to be considered. This article summarizes the current state of particle therapy for NSCLC with a specific focus on the impact of dosimetric uncertainties in planning and delivery.

Charged Particle and Conventional Radiotherapy: Current Implications as Partner for Immunotherapy

Radiotherapy (RT) has been shown to interfere with inflammatory signals and to enhance tumor immunogenicity via, e.g., immunogenic cell death, thereby potentially augmenting the therapeutic efficacy of immunotherapy. Conventional RT consists predominantly of high energy photon beams. Hypofractionated RT regimens administered, e.g., by stereotactic body radiation therapy (SBRT), are increasingly investigated in combination with cancer immunotherapy within clinical trials. Despite intensive preclinical studies, the optimal dose per fraction and dose schemes for elaboration of RT induced immunogenic potential remain inconclusive. Compared to the scenario of combined immune checkpoint inhibition (ICI) and RT, multimodal therapies utilizing other immunotherapy principles such as adoptive transfer of immune cells, vaccination strategies, targeted immune-cytokines and agonists are underrepresented in both preclinical and clinical settings. Despite the clinical success of ICI and RT combination, e.g., prolonging overall survival in locally advanced lung cancer, curative outcomes are still not achieved for most cancer entities studied. Charged particle RT (PRT) has gained interest as it may enhance tumor immunogenicity compared to conventional RT due to its unique biological and physical properties. However, whether PRT in combination with immune therapy will elicit superior antitumor effects both locally and systemically needs to be further investigated. In this review, the immunological effects of RT in the tumor microenvironment are summarized to understand their implications for immunotherapy combinations. Attention will be given to the various immunotherapeutic interventions that have been co-administered with RT so far. Furthermore, the theoretical basis and first evidences supporting a favorable immunogenicity profile of PRT will be examined.

Carbon Ion Radiobiology

Simple Summary

Radiotherapy with carbon ions has been used for over 20 years in Asia and Europe and is now planned in the USA. The physics advantages of carbon ions compared to X-rays are similar to those of protons, but their radiobiological features are quite distinct and may lead to a breakthrough in the treatment of some cancers characterized by high mortality.

Abstract

Radiotherapy using accelerated charged particles is rapidly growing worldwide. About 85% of the cancer patients receiving particle therapy are irradiated with protons, which have physical advantages compared to X-rays but a similar biological response. In addition to the ballistic advantages, heavy ions present specific radiobiological features that can make them attractive for treating radioresistant, hypoxic tumors. An ideal heavy ion should have lower toxicity in the entrance channel (normal tissue) and be exquisitely effective in the target region (tumor). Carbon ions have been chosen because they represent the best combination in this direction. Normal tissue toxicities and second cancer risk are similar to those observed in conventional radiotherapy. In the target region, they have increased relative biological effectiveness and a reduced oxygen enhancement ratio compared to X-rays. Some radiobiological properties of densely ionizing carbon ions are so distinct from X-rays and protons that they can be considered as a different “drug” in oncology, and may elicit favorable responses such as an increased immune response and reduced angiogenesis and metastatic potential. The radiobiological properties of carbon ions should guide patient selection and treatment protocols to achieve optimal clinical results.

Implementation of the Chick Chorioallantoic Membrane (CAM) Model in Radiation Biology and Experimental Radiation Oncology Research

Radiotherapy (RT) is part of standard cancer treatment. Innovations in treatment planning and increased precision in dose delivery have significantly improved the therapeutic gain of radiotherapy but are reaching their limits due to biologic constraints. Thus, a better understanding of the complex local and systemic responses to RT and of the biological mechanisms causing treatment success or failure is required if we aim to define novel targets for biological therapy optimization. Moreover, optimal treatment schedules and prognostic biomarkers have to be defined for assigning patients to the best treatment option. The complexity of the tumor environment and of the radiation response requires extensive in vivo experiments for the validation of such treatments. So far in vivo investigations have mostly been performed in time- and cost-intensive murine models. Here we propose the implementation of the chick chorioallantoic membrane (CAM) model as a fast, cost-efficient model for semi high-throughput preclinical in vivo screening of the modulation of the radiation effects by molecularly targeted drugs. This review provides a comprehensive overview on the application spectrum, advantages and limitations of the CAM assay and summarizes current knowledge of its applicability for cancer research with special focus on research in radiation biology and experimental radiation oncology.