Radiomics investigates the predictive role of quantitative parameters calculated from radiological images. In oncology, tumour segmentation constitutes a crucial step of the radiomic workflow. Manual segmentation is time-consuming and prone to inter-observer variability. In this study, a state-of-the-art deep-learning network for automatic segmentation (nnU-Net) was applied to computed tomography images of lung tumour patients, and its impact on the performance of survival radiomic models was assessed. In total, 899 patients were included, from two proprietary and one public datasets. Different network architectures (2D, 3D) were trained and tested on different combinations of the datasets. Automatic segmentations were compared to reference manual segmentations performed by physicians using the DICE similarity coefficient. Subsequently, the accuracy of radiomic models for survival classification based on either manual or automatic segmentations were compared, considering both hand-crafted and deep-learning features. The best agreement between automatic and manual contours (DICE = 0.78 ± 0.12) was achieved averaging 2D and 3D predictions and applying customised post-processing. The accuracy of the survival classifier (ranging between 0.65 and 0.78) was not statistically different when using manual versus automatic contours, both with hand-crafted and deep features. These results support the promising role nnU-Net can play in automatic segmentation, accelerating the radiomic workflow without impairing the models’ accuracy. Further investigations on different clinical endpoints and populations are encouraged to confirm and generalise these findings.

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.

Background: Radiomics represents an emerging field of precision‐medicine. Its application in head and neck is still at the beginning.Methods: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010–2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic‐software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical‐radiomic models) and compared using C‐index.Results: In almost all clinical‐radiomic models radiomic‐score maintained statistical significance. In all cases C‐index was higher in clinical‐radiomic models than in clinical ones. ADC provided the best fit to the models (C‐index 0.98, 0.86, 0.84 in loco‐regional recurrence, cause‐specific mortality, overall survival, respectively).Conclusion: MRI‐based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery.