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Artificial intelligence (AI) has made a tremendous impact in the space of healthcare, and proton therapy is not an exception. Proton therapy has witnessed growing popularity in oncology over recent decades, and researchers are increasingly looking to develop AI and machine learning tools to aid in various steps of the treatment planning and delivery processes. This review delves into the emergent role of AI in proton therapy, evaluating its development, advantages, intended clinical contexts, and areas of application. Through the analysis of 76 studies, we aim to underscore the importance of AI applications in advancing proton therapy and to highlight their prospective influence on clinical practices., Competing Interests: Declaration of Competing Interest BAJF received speaker fees from Roche, Bayer, Janssen, Ipsen, Accuray, Astellas, Elekta, IBA and Astra Zeneca (all outside the current project). EGCT is member of the scientific advisory board of IBA and received speaker fees from Elekta (all outside the current manuscript). M.G.V received a research fellowship from the Associazione Italiana per la Ricerca sul Cancro (AIRC) entitled “Radioablation ± hormonotherapy for prostate cancer oligorecurrences (RADIOSA trial): potential of imaging and biology” registered at Clinical Trials.gov NCT03940235, approved by the Ethics Committee of IEO and Centro Cardiologico Monzino (IEO-997). GC reports speakers fee and advisory board from Roche, Novartis, Lilly, Pfizer, Astra Zeneca, Daichii Sankyo, Ellipsis, Veracyte, Exact Science, Celcuity, Merck, BMS, Gilead, Sanofi, Menarini. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The remaining authors declare no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose or Objective-The aim of the study is to evaluate the efficacy and safety of SBRT on detectable prostate bed recurrence in RT-naïve prostate cancer patients., Materials and Methods: Eighty-six patients who underwent SBRT for macroscopic bed recurrence after prostatectomy were retrospectively included. Patients were treated based on mpMRI or choline/PSMA PET., Results: The median time to biochemical relapse (BCR) after RP was 46 months, with a median PSA at restaging of 1.04 ng/mL. Forty-six patients were staged with mpMRI and choline/PSMA PET, while ten and thirty were treated based on PET and MRI only, respectively. Only one late G ≥ 2 GI toxicity was observed. With a median BCR follow-up of 14 months, twenty-nine patients experienced a BCR with a median PSA at recurrence of 1.66 ng/mL and a median survival free from the event of 40.1 months. The median time to BCR was 17.9 months. Twenty-seven patients had clinical relapse (CR), with a median CR follow-up of 16.27 months and a median time to CR of 23.0 months. Biochemical recurrence-free survival at one and two years was 88% and 66%, respectively, while clinical recurrence-free survival at one and two years was 92% and 82%, respectively. Regarding local relapses, seven were in the field of treatment, while eight of them were outside the field of treatment., Conclusions: Data showed that SBRT targeting only the macroscopic bed recurrence instead of the whole prostate bed is safe and effective. Additional data and longer follow-ups will provide a clearer indication of the appropriate treatment and staging methodology for these patients.

Osteoradionecrosis (ORN) is a serious long-term complication of head and neck radiotherapy (RT), which is often triggered by dental extractions. It results from avascular aseptic necrosis due to irradiated bone damage. ORN is challenging to treat and can lead to severe complications. Furthermore, ORN causes pain and distress, significantly reducing the patient's quality of life. There is currently no established preventive strategy. This narrative review aims to provide an update for the clinicians on the risk of ORN associated with oral surgery in head and neck RT patients, with a focus on the timing suitable for the oral surgery and possible ORN preventive treatments. An electronic search of articles was performed by consulting the PubMed database. Intervention and observational studies were included. A multidisciplinary approach to the patient is highly recommended to mitigate the risk of RT complications. A dental visit before commencing RT is highly advised to minimize the need for future dental extractions after irradiation, and thus the risk of ORN. Post-RT preventive strategies, in case of dento-alveolar surgery, have been proposed and include antibiotics, hyperbaric oxygen (HBO), and the combined use of pentoxifylline and tocopherol ("PENTO protocol"), but currently there is a lack of established standards of care. Some limitations in the use of HBO involve the low availability of HBO facilities, its high costs, and specific clinical contraindications; the PENTO protocol, on the other hand, although promising, lacks clinical trials to support its efficacy. Due to the enduring risk of ORN, removable prostheses are preferable to dental implants in these patients, as there is no consensus on the appropriate timing for their safe placement. Overall, established standards of care and high-quality evidence are lacking concerning both preventive strategies for ORN as well as the timing of the dental surgery. There is an urgent need to improve research for more efficacious clinical decision making.

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.

Aims: Aim of the present analysis was to report results of a systematic review of the literature in the setting of patients treated with hypoF PT for benign lesions of the central nervous system (CNS)., Methods: The methodology complied with the PRISMA recommendations. PubMed, EMBASE and Scopus databases were interrogated in September 2022., Results: Twelve papers have been selected including patients treated for base of the skull meningiomas (6 papers), vestibular schwannoma (3 papers) and pituitary adenomas (3 papers). Clinical outcomes were evaluated with both radiologic images and clinical parameters. Long-term toxicity was reported in all but one series with an incidence ranging from 2 % to 7 % in patients treated for base of skull meningioma and 1-9 % for schwannoma., Conclusions: HypoF PT is a safe and effective treatment in selected benign tumors of the CNS. Further dosimetric and clinical comparisons are required to better refine the patients' selection criteria., Competing Interests: Declaration of Competing Interest BAJF received speakers fees from Roche, Bayer, Janssen, Carl Zeiss, Ipsen, Accuray, Astellas, Elekta, IBA and Astra Zeneca. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Aims: The number of Proton Therapy (PT) facilities is still limited worldwide, and the access to treatment could be characterized by patients' logistic and economic challenges. Aim of the present survey is to assess the support provided to patients undergoing PT across Europe., Methods: Through a personnel contact, an online questionnaire (62 multiple-choice and open-ended questions) via Microsoft Forms was administered to 10 European PT centers. The questionnaire consisted of 62 questions divided into 6 sections: i) personal data; ii) general information on clinical activity; iii) fractionation, concurrent systemic treatments and technical aspects of PT facility; iv) indication to PT and reimbursement policies; v) economic and/ or logistic support to patients vi) participants agreement on statements related to the possible limitation of access to PT. A qualitative analysis was performed and reported., Results: From March to May 2022 all ten involved centers filled the survey. Nine centers treat from 100 to 500 patients per year. Paediatric patients accounted for 10-30%, 30-50% and 50-70% of the entire cohort for 7, 2 and 1 center, respectively. The most frequent tumours treated in adult population were brain tumours, sarcomas and head and neck carcinomas; in all centers, the mean duration of PT is longer than 3 weeks. In 80% of cases, the treatment reimbursement for PT is supplied by the respective country's Health National System (HNS). HNS also provides economic support to patients in 70% of centers, while logistic and meal support is provided in 20% and 40% of centers, respectively. PT facilities offer economic and/or logistic support in 90% of the cases. Logistic support for parents of pediatric patients is provided by HNS only in one-third of centers. Overall, 70% of respondents agree that geographic challenges could limit a patient's access to proton facilities and 60% believe that additional support should be given to patients referred for PT care., Conclusions: Relevant differences exist among European countries in supporting patients referred to PT in their logistic and economic challenges. Further efforts should be made by HNSs and PT facilities to reduce the risk of inequities in access to cancer care with protons., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Background: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs., Methods: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables., Results: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables., Conclusions: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice)., (© 2023. The Author(s).)

Objective: Deploying an automatic segmentation model in practice should require rigorous quality assurance (QA) and continuous monitoring of the model's use and performance, particularly in high-stakes scenarios such as healthcare. Currently, however, tools to assist with QA for such models are not available to AI researchers. In this work, we build a deep learning model that estimates the quality of automatically generated contours., Methods: The model was trained to predict the segmentation quality by outputting an estimate of the Dice similarity coefficient given an image contour pair as input. Our dataset contained 60 axial T2-weighted MRI images of prostates with ground truth segmentations along with 80 automatically generated segmentation masks. The model we used was a 3D version of the EfficientDet architecture with a custom regression head. For validation, we used a fivefold cross-validation. To counteract the limitation of the small dataset, we used an extensive data augmentation scheme capable of producing virtually infinite training samples from a single ground truth label mask. In addition, we compared the results against a baseline model that only uses clinical variables for its predictions., Results: Our model achieved a mean absolute error of 0.020 ± 0.026 (2.2% mean percentage error) in estimating the Dice score, with a rank correlation of 0.42. Furthermore, the model managed to correctly identify incorrect segmentations (defined in terms of acceptable/unacceptable) 99.6% of the time., Conclusion: We believe that the trained model can be used alongside automatic segmentation tools to ensure quality and thus allow intervention to prevent undesired segmentation behavior., (© 2022. The Author(s).)