This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can research practices in medical image segmentation be improved?”, “What is missing from the current corpus?”, and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today’s competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information.

Although radiation therapy (RT) provides several therapeutic advantages in terms of cancer control and quality of life, it continues to be a poorly understood field by most students and health workers. Theoretical lessons are not sufficient, while practical exercitations are time-consuming, both in terms of man- and machine-hours. Furthermore, RT candidates often have several prejudices that may affect their treatment choices, favoring the more well-known surgical or chemotherapy approaches or resulting in a high level of anxiety during treatment. Moreover, the misperception of low treatment control and its related side effects could worsen the patients’ psychological distress, already brought by a cancer diagnosis. Augmented reality (AR) and virtual reality (VR) could be a valid instrument for promoting the awareness of radiation oncology as a discipline with its own identity and respect in the scientific community. The aim of the present work is to provide a glance at the recent developments in AR/VR to support students’ education, personnel training and patients’ empowerment in this clinical setting. The main findings of our work show that such technologies have already become a reality in many institutions worldwide and it has been shown to be an effective strategy for raising educational standards, improving health workers’ skills and promoting patients’ well-being and compliance. These results seem to promote the further implementation of AR/VR technologies and their development as a driving force of a much-hoped-for revolution in the way patients are treated and radiation oncology is taught.

Background: The therapeutic potential of proton therapy (PT) was first recognized in 1946 by Robert Wilson, and nowadays, over 100 proton centers are in operation worldwide, and more than 60 are under construction or planned. Bibliometric data can be used to perform a structured analysis of large amounts of scientific data to provide new insights, e.g., to assess the growth and development of the field and to identify research trends and hot topics. The aim of this study is to provide a comprehensive bibliometric analysis of the current status and trends in scientific literature in the PT field. Methods: The literature on PT until the 31st December 2022 in the Scopus database was searched, including the following keywords: proton AND radiotherapy AND cancer/tumor in title, abstract, and/or keywords. The open-source R Studio’s Bibliometrix package and Biblioshiny software (version 2.0) were used to perform the analysis. Results: A total of 7335 documents, mainly articles (n = 4794, 65%) and reviews (n = 1527, 21%), were collected from 1946 to 2022 from 1054 sources and 21,696 authors. Of these, roughly 84% (n = 6167) were produced in the last 15 years (2008–2022), in which the mean annual growth rate was 13%. Considering the corresponding author’s country, 79 countries contributed to the literature; the USA was the top contributor, with 2765 (38%) documents, of whom 84% were single-country publications (SCP), followed by Germany and Japan, with 535 and 531 documents of whom 66% and 93% were SCP. Considering the themes subanalysis (2002–2022), a total of 7192 documents were analyzed; among all keywords used by authors, the top three were radiotherapy (n = 1394, 21% of documents), intensity-modulated radiotherapy (n = 301, 5%), and prostate cancer (n = 301, 5%). Among disease types, prostate cancer is followed by chordoma, head and neck, and breast cancer. The change in trend themes demonstrated the fast evolution of hotspots in PT; among the most recent trends, the appearance of flash, radiomics, relative biological effectiveness (RBE), and linear energy transfer (LET) deserve to be highlighted. Conclusions: The results of the present bibliometric analysis showed that PT is an active and rapidly increasing field of research. Themes of the published works encompass the main aspects of its application in clinical practice, such as the comparison with the actual photon-based standard of care technique and the continuing technological advances. This analysis gives an overview of past scientific production and, most importantly, provides a useful point of view on the future directions of the research activities.