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.

Ionizing radiation is a common tool in medical procedures. Monte Carlo (MC) techniques are widely used when dosimetry is the matter of investigation. The scientific community has invested, over the last 20 years, a lot of effort into improving the knowledge of radiation biology. The present article aims to summarize the understanding of the field of DNA damage response (DDR) to ionizing radiation by providing an overview on MC simulation studies that try to explain several aspects of radiation biology. The need for accurate techniques for the quantification of DNA damage is crucial, as it becomes a clinical need to evaluate the outcome of various applications including both low-and high-energy radiation medical procedures. Understanding DNA repair processes would improve radiation therapy procedures. Monte Carlo simulations are a promising tool in radiobiology studies, as there are clear prospects for more advanced tools that could be used in multidisciplinary studies, in the fields of physics, medicine, biology and chemistry. Still, lot of effort is needed to evolve MC simulation tools and apply them in multiscale studies starting from small DNA segments and reaching a population of cells.

Pediatric patient-specific dosimetry of ionizing radiation is of great scientific and social interest. Children provide a higher relative cancer-risk from exposure to ionizing radiation compared to adults. The proposed study reviews the recent techniques applied in pediatric imaging and therapy applications for dosimetry purposes. Modern medicine makes use of advance computational tools for the personalization of internal and external dosimetry, especially in the sensitive group of children. Several groups of pediatric computational models have been developed which are combined with Monte Carlo (MC) simulations, machine learning (ML) techniques, and image processing algorithms for accurate dosimetry assessment. More specifically, this paper reviews the dosimetry applications in pediatric diagnostic procedures, including computed tomography and nuclear medicine applications. Right afterward, the most recent applications in therapeutic brachytherapy protocols are presented, which is a rather sensitive procedure in pediatrics. Finally, modern tools for dosimetry optimization are discussed, reviewing the most indicative applications with: 1) MC simulations for pediatric dosimetry assessment; 2) pediatric computational models, widely used in medical applications; and 3) ML techniques that provide an alternative method for estimating individualized absorbed doses.