Konstantinos P. Chatzipapas

Background This study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve. Methods A Python-based tool named “complexDNA” was developed to facilitate the design of both simple and complex DNA geometries. This tool was employed to construct a human cell geometry with individual pairs of chromosomes. Subsequently, the performance of this chromosomal model was compared to the standard human cell model provided in the “molecularDNA” Geant4-DNA example. Results Simulations using the new chromosomal model revealed minimal discrepancies in DNA damage yield and fragment size distribution compared to the default human cell model. Notably, the chromosomal model demonstrated significant computational efficiency, requiring approximately three times less simulation time to achieve equivalent results. Conclusions This work highlights the importance of incorporating chromosomal structure into human cell models for radiation biology research. The “complexDNA” tool offers a valuable resource for creating intricate DNA structures for future studies. Further refinements, such as implementing smaller voxels for euchromatin regions, are proposed to enhance the model’s accuracy.

BACKGROUND: This study investigated alternative, non-invasive methods for human papillomavirus (HPV) detection in head and neck cancers (HNCs). We compared two approaches: analyzing computed tomography (CT) scans with a Deep Learning (DL) model and using radiomic features extracted from CT images with machine learning (ML) models. METHODS: Fifty patients with histologically confirmed HNC were included. We first trained a modified ResNet-18 DL model on CT data to predict HPV status. Next, radiomic features were extracted from manually segmented regions of interest near the oropharynx and used to train four ML models (K-Nearest Neighbors, logistic regression, decision tree, random forest) for the same purpose. RESULTS: The CT-based model achieved the highest accuracy (90%) in classifying HPV status. Among the ML models, K-Nearest Neighbors performed best (80% accuracy). Weighted Ensemble methods combining the CT-based model with each ML model resulted in moderate accuracy improvements (70-90%). CONCLUSIONS: Our findings suggest that CT scans analyzed by DL models hold promise for non-invasive HPV detection in HNC. Radiomic features, while less accurate in this study, offer a complementary approach. Future research should explore larger datasets and investigate the potential of combining DL and radiomic techniques.

The evolution of 3D printing has ushered in accessibility and cost-effectiveness, spanning various industries including biomedical engineering, education, and microfluidics. In biomedical engineering, it encompasses bioprinting tissues, producing prosthetics, porous metal orthopedic implants, and facilitating educational models. Hybrid Additive Manufacturing approaches and, more specifically, the integration of Fused Deposition Modeling (FDM) with bio-inkjet printing offers the advantages of improved accuracy, structural support, and controlled geometry, yet challenges persist in cell survival, interaction, and nutrient delivery within printed structures. The goal of this study was to develop and present a low-cost way to produce physical phantoms of human organs that could be used for research and training, bridging the gap between the use of highly detailed computational phantoms and real-life clinical applications. To this purpose, this study utilized anonymized clinical Computed Tomography (CT) data to create a liver physical model using the Creality Ender-3 printer. Polylactic Acid (PLA), Polyvinyl Alcohol (PVA), and light-bodied silicone (Polysiloxane) materials were employed for printing the liver including its veins and arteries. In brief, PLA was used to create a mold of a liver to be filled with biocompatible light-bodied silicone. Molds of the veins and arteries were printed using PVA and then inserted in the liver model to create empty channel. In addition, the PVA was then washed out by the final product using warm water. Despite minor imperfections due to the printer’s limitations, the final product imitates the computational model accurately enough. Precision adjustments in the design phase compensated for this variation. The proposed novel low-cost 3D printing methodology successfully produced an anatomically accurate liver physical model, presenting promising applications in medical education, research, and surgical planning. Notably, its implications extend to medical training, personalized medicine, and organ transplantation. The technology’s potential includes injection training for medical professionals, personalized anthropomorphic phantoms for radiation therapy, and the future prospect of creating functional living organs for organ transplantation, albeit requiring significant interdisciplinary collaboration and financial investment. This technique, while showcasing immense potential in biomedical applications, requires further advancements and interdisciplinary cooperation for its optimal utilization in revolutionizing medical science and benefiting patient healthcare.

Purpose: This study aimed to develop a computational environment for the accurate simulation of human cancer cell irradiation using Geant4-DNA. New cell geometrical models were developed and irradiated by alpha particle beams to induce DNA damage. The proposed approach may help further investigation of the benefits of external alpha irradiation therapy. Methods: The Geant4-DNA Monte Carlo (MC) toolkit allows the simulation of cancer cell geometries that can be combined with accurate modelling of physical, physicochemical and chemical stages of liquid water irradiation, including radiolytic processes. Geant4-DNA is used to calculate direct and non-direct DNA damage yields, such as single and double strand breaks, produced by the deposition of energy or by the interaction of DNA with free radicals. Results: In this study, the “molecularDNA” example application of Geant4-DNA was used to quantify early DNA damage in human cancer cells upon irradiation with alpha particle beams, as a function of linear energy transfer (LET). The MC simulation results are compared to experimental data, as well as previously published simulation data. The simulation results agree well with the experimental data on DSB yields in the lower LET range, while the experimental data on DSB yields are lower than the results obtained with the “molecularDNA” example in the higher LET range. Conclusion: This study explored and demonstrated the possibilities of the Geant4-DNA toolkit together with the “molecularDNA” example to simulate the helium beam irradiation of cancer cell lines, to quantify the early DNA damage, or even the following DNA damage response.

Abstract Purpose The scientific community shows great interest in the study of DNA damage induction, DNA damage repair, and the biological effects on cells and cellular systems after exposure to ionizing radiation. Several in silico methods have been proposed so far to study these mechanisms using Monte Carlo simulations. This study outlines a Geant4-DNA example application, named “molecularDNA”, publicly released in the 11.1 version of Geant4 (December 2022). Methods It was developed for novice Geant4 users and requires only a basic understanding of scripting languages to get started. The example includes two different DNA-scale geometries of biological targets, namely “cylinders” and “human cell”. This public version is based on a previous prototype and includes new features, such as: the adoption of a new approach for the modeling of the chemical stage, the use of the standard DNA damage format to describe radiation-induced DNA damage, and upgraded computational tools to estimate DNA damage response. Results Simulation data in terms of single-strand break and double-strand break yields were produced using each of the available geometries. The results were compared with the literature, to validate the example, producing less than 5% difference in all cases. Conclusion: “molecularDNA” is a prototype tool that can be applied in a wide variety of radiobiology studies, providing the scientific community with an open-access base for DNA damage quantification calculations. New DNA and cell geometries for the “molecularDNA” example will be included in future versions of Geant4-DNA.