Despite the promising efficacy of immunotherapy in some patients, many other patients are resistant. The synergistic effect of radiotherapy (RT) in combination with immunotherapy reported in case reports and clinical trials has piqued the interest of radiologists in investigating the underlying mechanisms and efficacy of the combination in preclinical and clinical trials. To date, the reported data are limited to small-sized samples, trials lacking a comparison arm, and trials using diverse immunotherapies, various radiation doses, and fractionations. There are just a few studies comparing the efficacy of immunotherapy and radiotherapy to that of conventional therapies or different combinations. Radiologists should design and conduct clinical trials wisely to confirm the efficacy of the combination, particularly the abscopal effect, identify the best combination of various immunotherapeutic drugs and different radiation models for patients, identify the best sequence of the combination, determine the optimal timing of the combination, select the target site and volume, lower adverse effects, and explore predictive models to identify patients who may benefit from the combination therapy. We expect that these clinical trials performed by radiologists will offer definitive evidence for the wide use of the combination of RT and immunotherapy in clinical practice. IMPLICATIONS FOR PRACTICE: This review will provide an update on the use of a combination of radiotherapy and immunotherapy, a cautious interpretation of preliminary results, and future directions for radiologists to perform well-designed clinical trials.

Software applications executed on a smart-phone or mobile device ("Apps") are increasingly used by oncologists in their daily work. A comprehensive critical review was conducted on Apps specifically designed for Radiation Oncology, which aims to provide scientific support for these tools and to guide users in choosing the most suited to their needs.

In an era of increasing virtual communication, we aimed to investigate current formats used by radiation oncology residents for reviewing radiation treatment plans with attendings, preferences for formats, and reasons contributing to preferences. Residents enrolled in Canadian radiation oncology programs received questionnaires examining training level, typical review formats, preferred format, and reasons for preference. Analysis excluded PGY-1s due to insufficient exposure. Fifty-two residents participated. National response rate was 55%. Overall, hybrid review was the most used format (77%). Virtual review was the most preferred format (44%). Preference for virtual review was most common among junior residents (57%), while in-person review was most preferred by senior residents (45.4%). Few residents typically use their preferred format (35%). Reasons for preference varied between groups in convenience (p < 0.01), interactivity (p < 0.01), and teaching quality (p = 0.04). The persistence of e-learning suggests that virtual treatment planning education will continue to some degree. Junior residents prefer virtual review, while a clearly preferred review format was less apparent among senior residents. Preferences are multifactorial, and the trends seen in reasons for preference between formats may reflect advantages inherent to each. Progress is still needed in optimizing treatment planning education, as suggested by few residents using their preferred format. Residents and staff should collectively decide which educational format for treatment planning best meets educational needs.

Introduction We aimed to characterize the most influential radiation oncologists on Twitter, the correlation between their Twitter activity and their academic profiles as measured by the Scopus H-index as well as their activity around the American Society for Radiation Oncologists (ASTRO) 2018 meeting. Methods We defined radiation oncology influencers as any radiation oncologist with 500 or more followers on Twitter through the first two weeks of August 2019. We collected their available characteristics, their Scopus H-index, and Twitter metrics. We examined their general Twitter activity as well as their specific activity before, during, and after the 2018 ASTRO annual meeting. We identified the most frequent tweet content categories for each influencer. Results We identified 48 radiation oncologist influencers; 79% were male, 75% were based in the United States, and 94% were affiliated with an academic center. Among them, 44% had high H-indices of ≥21, an average value in academic faculty for full professors or department heads. There were no correlations between H-index and Twitter metrics such as the number of individuals the influencer was following (p = 0.58), the number of followers (p = 0.66), the number of tweets (p = 0.88), and the number of likes (p = 0.54). During the period around ASTRO 2018, the mean number of tweets per influencer was 4437 (range 87-93,000). Conclusion Current radiation oncology influencers are predominantly North American males from academic institutions. A correlation between academic productivity as measured by the H-index and Twitter metrics was not demonstrated. The fact that some influencers had a low H-index supports that a high academic profile as measured by traditional metrics is not necessary to have a voice in the Twitter radiation oncology community.

The accurate attribution of death in oncologic patients is a difficult task. The patient's death is often attributed to his or her underlying cancer and therefore judged as cancer-related. We hypothesized that even though our patient's cancers were either advanced or metastatic, not all patients had died simply because of their cancer.

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

Mentored medical student (MS) research opportunities in radiation oncology (RO) provide in-depth exposure to the specialty and may promote greater interest in a career in RO. Many radiation oncologists conduct research; however, the extent to which they directly engage MSs in their research is unknown. The purpose of this study was to characterize MS authorship in American Society for Radiation Oncology (ASTRO) journals.

Cancer care is moving from a disease-focused management toward a patient-centered tailored approach. Multidisciplinary management that aims to define individual, optimal treatment strategies through shared decision making between healthcare professionals and patient is a fundamental aspect of high-quality cancer care and often includes radiation oncology. Advances in technology and radiobiological research allow to deliver ever more tailored radiation treatments in an ever easier and faster way, thus improving the efficacy, safety, and accessibility of radiation therapy. While these changes are improving quality of cancer care, they are also enormously increasing complexity of decision making, thus challenging the ability to deliver quality affordable cancer care. In this review, we provide an updated outline of the role of radiation oncology in the modern multidisciplinary treatment of cancer. Particularly, we focus on the way some developments in key areas of cancer management are challenging multidisciplinary cancer care in the different clinical settings of early, locally advanced, and metastatic disease, thus highlighting some priority areas of research.

The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports.

To review existing scientific literature on mobile applications (apps) in the field of radiation oncology and to evaluate characteristics of commercially available apps across different platforms.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has changed the lives of most humans worldwide. The aim of this study was to evaluate the impact of the SARS-CoV‑2 pandemic on health care professionals (HCPs) in radiation oncology facilities.

Demand for radiation therapy is expected to increase over time. In Aotearoa/New Zealand, the radiation oncology workforce experiences high numbers of clinical hours but an intervention rate that is lower than in comparable countries, suggesting unmet treatment need. Accurate models on the supply and demand for radiation oncologists (ROs) are needed to ensure adequate staffing levels.

The effect of climate-driven events, such as wildfires, on health care delivery and cancer care is a growing concern. Patients with cancer undergoing radiation therapy are particularly vulnerable to treatment interruptions, which have a direct effect on survival. We report the results of a study characterizing the effect of wildfires on radiation oncology clinics and their patients.

Beam Output Auditing (BOA) is one key process of the EORTC radiation therapy quality assurance program. Here the results obtained between 2005 and 2014 are presented and compared to previous results.For all BOA reports the following parameters were scored: centre, country, date of audit, beam energies and treatment machines audited, auditing organisation, percentage of agreement between stated and measured dose.Four-hundred and sixty-one BOA reports were analyzed containing the results of 1790 photon and 1366 electron beams, delivered by 755 different treatment machines. The majority of beams (91.1%) were within the optimal limit of ≤ 3%. Only 13 beams (0.4%; n = 9 electrons; n = 4 photons), were out of the range of acceptance of ≤ 5%. Previous reviews reported a much higher percentage of 2.5% or more of the BOAs with >5% deviation.The majority of EORTC centres present beam output variations within the 3% tolerance cutoff value and only 0.4% of audited beams presented with variations of more than 5%. This is an important improvement compared to previous BOA results.

Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address.

Radiotherapy has been optimized over the last decades not only through technological advances, but also through the translation of biological knowledge into clinical treatment schedules. Optimization of fractionation schedules and/or the introduction of simultaneous combined systemic treatment have significantly improved tumour cure rates in several cancer types. With modern techniques, we are currently able to measure factors of radiation resistance or radiation sensitivity in patient tumours; the definition of new biomarkers is expected to further enable personalized treatments. In this Review article, we overview important translation paths and summarize the quality requirements for preclinical and translational studies that will help to avoid bias in trial results.

Both medical education and radiation oncology have progressed significantly in the past decade, but a generalized overview of educational research for radiation oncology residents has not been produced. This study examines recent research trends in medical education for residents in radiation oncology through a scoping review.

Technological progress of machine learning and natural language processing has led to the development of large language models (LLMs), capable of producing well-formed text responses and providing natural language access to knowledge. Modern conversational LLMs such as ChatGPT have shown remarkable capabilities across a variety of fields, including medicine. These models may assess even highly specialized medical knowledge within specific disciplines, such as radiation therapy. We conducted an exploratory study to examine the capabilities of ChatGPT to answer questions in radiation therapy.

To provide an overview of Radiation Oncology (RO) teaching to medical students around Europe.

The details of breast reconstruction and radiation therapy (RT) vary between institutions; therefore, we sought to investigate the practice patterns of radiation oncologists who specialize in breast cancer.