Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Its functionality and modeling capabilities continue to be extended, while its performance is enhanced. An overview of recent developments in diverse areas of the toolkit is presented. These include performance optimization for complex setups; improvements for the propagation in fields; new options for event biasing; and additions and improvements in geometry, physics processes and interactive capabilities

In 2021 the World Health Organization published the fifth and latest version of the Central Nervous System tumors classification, which incorporates and summarizes a long list of updates from the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy work. Among the adult-type diffuse gliomas, glioblastoma represents most primary brain tumors in the neuro-oncology practice of adults. Despite massive efforts in the field of neuro-oncology diagnostics to ensure a proper taxonomy, the identification of glioblastoma-tumor subtypes is not accompanied by personalized therapies, and no improvements in terms of overall survival have been achieved so far, confirming the existence of open and unresolved issues. The aim of this review is to illustrate and elucidate the state of art regarding the foremost biological and molecular mechanisms that guide the beginning and the progression of this cancer, showing the salient features of tumor hallmarks in glioblastoma. Pathophysiology processes are discussed on molecular and cellular levels, highlighting the critical overlaps that are involved into the creation of a complex tumor microenvironment. The description of glioblastoma hallmarks shows how tumoral processes can be linked together, finding their involvement within distinct areas that are engaged for cancer-malignancy establishment and maintenance. The evidence presented provides the promising view that glioblastoma represents interconnected hallmarks that may led to a better understanding of tumor pathophysiology, therefore driving the development of new therapeutic strategies and approaches.

Using the Monte Carlo simulation tool GEANT4, we simulated the hadron-therapy beam line of the Centro di AdroTerapia ed Applicazioni Nucleari Avanzate (CATANA) center. This is the unique Italian hadron-therapy facility in which 62-MeV proton beams are used for the radiotherapeutic treatment of choroidal and iris melanomas. All the elements, such as diffusers, range shifters, collimators, and detectors, typical of a proton-therapy line were modeled. The beam line provides an ideal environment for the experimental testing and validation of the software developed. The software architecture was developed, and the validation of the software is in its final stage. Simulated ranges, energy distribution, depth and lateral dose distributions for full energy proton beams will be compared to the experimental results obtained at Laboratori Nazionali del Sud (LNS) with different detectors.

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.

Advances in functional imaging are supporting neurosurgery and radiotherapy for glioblastoma, which still remains the most aggressive brain tumor with poor prognosis. The typical infiltration pattern of glioblastoma, which impedes a complete surgical resection, is coupled with a high rate of invasiveness and radioresistance, thus further limiting efficient therapy, leading to inevitable and fatal recurrences. Hypoxia is of crucial importance in gliomagenesis and, besides reducing radiotherapy efficacy, also induces cellular and molecular mediators that foster proliferation and invasion. In this review, we aimed at analyzing the biological mechanism of glioblastoma invasiveness and radioresistance in hypoxic niches of glioblastoma. We also discussed the link between hypoxia and radiation-induced radioresistance with activation of SRC proto-oncogene non-receptor tyrosine kinase, prospecting potential strategies to overcome the current limitation in glioblastoma treatment.

Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.