In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.

Quantitative analysis of biomedical images, referred to as radiomics, is emerging as a promising approach to facilitate clinical decisions and improve patient stratification. The typical radiomic workflow includes image acquisition, segmentation, feature extraction, and analysis of high-dimensional datasets. While procedures for primary radiomic analyses have been established in recent years, processing the resulting radiomic datasets remains a challenge due to the lack of specific tools for doing so. Here we present RadAR (Radiomics Analysis with R), a new software to perform comprehensive analysis of radiomic features. RadAR allows users to process radiomic datasets in their entirety, from data import to feature processing and visualization, and implements multiple statistical methods for analysis of these data. We used RadAR to analyze the radiomic profiles of more than 850 patients with cancer from publicly available datasets and showed that it was able to recapitulate expected results. These results demonstrate RadAR as a reliable and valuable tool for the radiomics community. Significance: A new computational tool performs comprehensive analysis of high-dimensional radiomic datasets, recapitulating expected results in the analysis of radiomic profiles of >850 patients with cancer from independent datasets.

Background: High‐dose rate brachytherapy using a non‐sealed ¹⁸⁸Rhenium resin (¹⁸⁸Re) is a recently approved treatment option for non‐melanoma skin cancer (NMSC). The treatment goal is to deliver a personalized absorbed dose to the deepest point of neoplastic infiltration corresponding to the minimal target dose. The treatment consists of the application of a ¹⁸⁸Re‐based resin over a plastic foil placed on the target skin surface. However, there is no treatment planning tool to assess the ¹⁸⁸Re activity needed for a personalized treatment.Purpose: The paper aims to present a novel Monte Carlo (MC)‐based tool for ¹⁸⁸Re‐based resin activity and dose calculation, experimentally validated using Gafchromic EBT3 films.Methods: MC simulations were carried out using FLUKA modeling density and composition of ¹⁸⁸Re resin. The MC‐based look up table (LUT) was incorporated in an ad hoc developed tool. The proposed tool allows the personalized calculation of treatment parameters (i.e., activity to be dispensed, the treatment duration, and dose volume histograms), according to the target dimension. The proposed tool was compared using Bland–Altman analysis to the previous calculation approaches conducted using VARSKIN in a retrospective cohort of 76 patients. The tool was validated in ad hoc experimental set ups using a stack of calibrated Gafchromic EBT3 films covered by a plastic film and exposed using a homogenous activity distribution of ¹⁸⁸Re eluate and a heterogeneous activity distribution of ¹⁸⁸Re resin mimic the patient treatment.Results: The agreement between the proposed tool and VARSKIN was evaluated on the investigated cohort with median range of target area, target depth, and treatment time equal to 4.8 [1.0–60.1] cm², 1.1 [0.2–3.0] mm, and 70 [21–285] min, with a median range of target dose (Gy) of 23.5 [10–54.9]. The calculated minimal target doses, ranged from 1% to 10% for intermediate target depths (1.2 ± 0.7 mm), while showing significant differences in the estimation of superficial (maximal) target doses. The agreement between MC calculation and measurements at different plans in a stack of Gafchromic EBT3 films was within 10% for both the homogenous and heterogeneous activity distribution of ¹⁸⁸Re. Worst agreements were observed for absorbed doses lower than 0.3 Gy.Conclusions: Our results support the implementation of our MC‐based tool in the practical routine for calculating the ¹⁸⁸Re resin activity and treatment parameters necessary for obtaining the prescribed minimal target dose.

This paper summarizes the three-year achievements of the AMS INFN Roma Sapienza research group, with a focus on their active engagement in astroparticle experiments and the exploration of the synergistic connection between astroparticle experiments and space radiobiology. Collaborating with institutions like the Medical Physics division of the IRCCS Azienda Ospedaliera-Universitaria di Bologna, they have made substantial contributions to space radiobiology projects, fostering interdisciplinary collaboration. The group's research has produced significant findings on various fronts, including the assessment of carcinogenic risks from galactic cosmic rays, with a particular emphasis on bystander effects. Additionally, the paper underscores the complementary role of astroparticle experiments in enhancing the assessment of biological risks associated with ionizing radiation exposure. Finally, the paper showcases examples of research conducted through collaboration with scientists and researchers worldwide. These illustrations offer valuable insights and highlight the interdisciplinary nature of this dynamic field.

Background: Transarterial radioembolization (TARE) relies on directly injected ⁹⁰Y- or ¹⁶⁶Ho-loaded microspheres in the hepatic arteries. The activity to be injected is generally based on pre-TARE ^99mTc-macro-aggregated-albumin (MAA) imaging, while the actual dose distribution is based on post-treatment images. The volume of interest (VOIs) propagation methods (i.e., rigid and deformable) from pre- to post-TARE imaging might affect the estimation of the mean absorbed dose in the tumor and non-tumoral liver (NTL), i.e., D_T and D_NTL, respectively. Methods: In 101 consecutive patients, liver and tumor were delineated on pre-TARE images and semi-automatically transferred on ⁹⁰Y-PET/CT images with a rigid or deformable registration approach. Pre- and post-TARE volumes and D_T/D_NTL/D_L were compared using correlation coefficient (CC) indexes, such as intra-class (ICC), Pearson’s (PCC), concordance (CCCo) and Bland–Altman analyses. The Kaplan–Meier curves of overall survival (OS) were calculated according to D_T. Results: All computed CCs indicated very good (>0.92) agreement for volume comparison, while they suggested good (ICC ≥ 0.869, PCC ≥ 0.876 and CCCo ≥ 0.790) and moderate agreement in the intra- and inter-modality D_T/D_NTL/D_L comparisons, respectively. Bland–Altman analyses showed percentage differences between the manual and deformable approaches of up to about −31%, 9% and 62% for tumoral volumes, D_T and D_NTL, respectively. The overall survival analysis showed statistically significant differences using D_T cutoffs of 110, 90 and 85 Gy for the manual, rigid and deformable approaches, respectively. Conclusions: The semi-automatic transfer of VOIs from pre- and post-TARE imaging is feasible, but the selected method might affect prognostic D_T/D_NTL constraints.

Background: Radiotherapy (RT) is a cornerstone of cancer treatment, with patient-specific quality assurance (PSQA) being essential for patient safety. However, PSQA can be time-consuming, particularly in high-throughput centers. Machine learning and deep learning (DL) offer tools to efficiently analyze large datasets and support data-driven workflows. Methods: DL was applied to predict PSQA outcomes for clinical RT plans delivered with 6 MV and 6 FFF MV. A retrospective dataset of 839 VMAT plans was used to extract 4470 dosiomic features from five automatic isodose contours. The dataset was split into training and test subsets. Two hybrid models were developed to identify plans with γ-passing rates below the median QA value (97%). A prospective dataset of 201 PSQA measurements was used for validation. Results: Both models demonstrated strong predictive performance. In the test set, Hybrid-1 achieved 0.74 specificity and 0.64 sensitivity, while Hybrid-2 reached 0.72 specificity and 0.52 sensitivity. Similar results were observed in the prospective validation cohort. Conclusion: Applying deep learning to dose distributions with carefully engineered features allows accurate identification of RT plans that require PSQA. This approach facilitates more efficient allocation of both personnel and linear accelerator time, thereby supporting safer and more streamlined PSQA workflows.