Christian Macis

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.

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.