Report of China's innovation increase and research growth in radiation oncology

Aims： To investigate the research status of radiation oncology in China through survey of literature in international radiation oncology journals and retrospectively compare the outputs of radiation oncology articles of the three major regions of China--Mainland （ML）, Taiwan （TW） and Hong Kong （HK）. Methods： Radiation oncology journals were selected from ＂oncology＂ and ＂radiology, nuclear ＆ medical image＂ category from Science Citation Index Expand （SCIE）. Articles from the ML, TW and HK were retrieved from MEDLINE. The number of total articles, clinical trials, case reports, impact factors （IF）, institutions and articles published in each journals were conducted for quantity and quality comparisons. Results： A total 818 articles from 13 radiation oncology journals were searched, of which 427 are from ML, 259 from TW, and 132 from HK. Ninety-seven clinical trials and 5 case reports are reported in China. Accumulated IF of articles from ML （1,417.11） was much higher than that of TW （1,003.093） and HK （544.711）, while the average IF of articles from ML is the lowest. Conclusions： The total number of articles from China especially ML increased significantly in the last decade. The number of articles published from the ML has exceeded those from TW and HK. However, the quality of articles from TW and HK is better than that from ML.

COVID-19 and information and communication technology in radiation oncology:A new paradigm

Due to coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus 2,there has been a major reallocation of resources that has impacted the treatment of many diseases,including cancer.The growing use of information and communication technologies(ICT),together with a new approach to work aimed at ensuring the safety of health care professionals and patients alike,has allowed us to maintain the quality of care while ensuring biosecurity.The application of ICT to health care(eHealth)aims to significantly improve the quality,access to,and effectiveness of medical care.In fact,the expanded use of ICT has been recognized as a key,cost-effective priority for health care by the World Health Organisation.The medical speciality of radiation oncology is closely linked to technology and as a consequence of coronavirus disease 2019,ICT has been widely employed by radiation oncologists worldwide,providing new opportunities for interaction among professionals,including telemedicine and e-learning,while also minimizing treatment interruptions.Future research should concentrate on this emerging paradigm,which offers new opportunities,including faster and more diverse exchange of scientific knowledge,organizational improvements,and more efficient workflows.Moreover,these efficiencies will allow professionals to dedicate more time to patient care,with a better work-life balance.In the present editorial,we discuss the opportunities provided by these digital tools,as well as barriers to theirimplementation,and a vision of the future.

Radiation Oncology and Medical Devices( Part 1)

Modern radiation treatments have become fairly complex and involve in utilizing a variety of medical devices to achieve the goal of providing conformal radiation dose coverage to the tumor target(s)while maximizing the sparing of normal organ structures.Recently,different forms of linear accelerators/radioactive source based machines have been invented and developed with the aim of providing improved treatments and more treatment options.Besides linear accelerators(Linac)that have been undergoing constant improvement and advancement and can deliver fairly complicated dose distribution patterns,imaging systems,computer information and calculation systems have been more and more integrated into radiotherapy processes.To bring radiotherapy to a potentially higher level,many institutions have either acquired or started to consider particle therapy,especially proton therapy.The complexity of modern radiotherapy demands in-depth understanding of radiation physics and machine engineering as well as computer information systems.This paper is intended to provide an introductory description of radiation oncology and related procedures,and to provide an overview of the current status of medical devices in radiotherapy in the United States of America.This paper covers the radiation delivery systems,imaging systems,treatment planning systems,record and verify systems,and QA systems.

Radiation Oncology and Medical Devices(Part 2)

Radiation oncology is one of the three major treatment modalities to manage cancer patient cares,and is a discipline mainly driven by technology and medical devices.Modern radiation treatments have become fairly complex and involve in utilizing a variety of medical devices to achieve the goal of providing conformal radiation dose coverage to the tumor target(s)while maximizing the sparing of normal organ structures.Recently,different forms of linear accelerators/radioactive source based machines have been invented and developed with the aim of providing improved treatments and more treatment options.Besides linear accelerators(Linac)that have been undergoing constant improvement and advancement and can deliver fairly complicated dose distribution patterns,imaging systems,computer information and calculation systems have been more and more integrated into radiotherapy processes.To bring radiotherapy to a potentially higher level,many institutions have either acquired or started to consider particle therapy,especially proton therapy.The complexity of modern radiotherapy demands in-depth understanding of radiation physics and machine engineering as well as computer information systems.This paper is intended to provide an introductory description of radiation oncology and related procedures,and to provide an overview of the current status of medical devices in radiotherapy in the United States of America.This paper covers the radiation delivery systems,imaging systems,treatment planning systems,record and verify systems,and QA systems.

Artificial intelligence in radiation oncology

Artificial intelligence(AI)is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input.Machine learning(ML)is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience.Deep learning is an ML technique that uses deep neural networks to create a model.The growth and sharing of data,increasing computing power,and developments in AI have initiated a transformation in healthcare.Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging,dosimetry,and imaging performed before each fraction.Of the many algorithms used in radiation oncology,has advantages and limitations with different computational power requirements.The aim of this review is to summarize the radiotherapy(RT)process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML.The RT stage is divided into seven stages:patient evaluation,simulation,contouring,planning,quality control,treatment application,and patient follow-up.A systematic evaluation of the applicability,limitations,and advantages of AI algorithms has been done for each stage.

Advances of radiation oncology in China

Radiation therapy is one of the main treatment .modalities used in cancer management. It is the primary curative modality for about 30% of patients with cancer, and more than half of all cancer patients received radiation therapy sometime during the course of their disease. It is estimated that 70% of the patients with cancer required radiation therapy in China.1 Radiation oncology is based on the development of radiological physics, radiological biology and clinical oncology.

Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy

BACKGROUND Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer(LAPC).However,the diagnostic performance of computed tomography(CT)imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation.In this context,radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional,unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue,potentially serving in the therapeutic decision-making process.AIM To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy.METHODS Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed.One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume.Both extracted features and clinical data contribute to create and validate the predictive model of resectability status.Patients were repeatedly divided into training and validation sets.The discriminating performance of each model,obtained applying a LASSO regression analysis,was assessed with the area under the receiver operating characteristic curve(AUC).The validated model was applied to the entire dataset to obtain the most significant features.RESULTS Seventy-one patients were included in the analysis.Median age was 65 years and 57.8%of patients were male.All patients underwent induction chemotherapy followed by ablative radiotherapy,and 19(26.8%)ultimately received surgical resection.After the first step of variable selections,a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862(95%CI:0.792-0.921)and 0.853(95%CI:0.706-0.960),respectively.The validated model was applied to the entire dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944(95%CI:0.892-0.996).CONCLUSION The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy,potentially integrating clinical and morphological parameters in predicting surgical resection.

Concise review of radiosurgery for contemporary management of pilocytic astrocytomas in children and adults

Pilocytic astrocytoma(PA)may be seen in both adults and children as a distinct histologic and biologic subset of low-grade glioma.Surgery is the principal treatment for the management of PAs;however,selected patients may benefit from irradiation particularly in the setting of inoperability,incomplete resection,or recurrent disease.While conventionally fractionated radiation therapy has been traditionally utilized for radiotherapeutic management,stereotactic irradiation strategies have been introduced more recently to improve the toxicity profile of radiation delivery without compromising tumor control.PAs may be suitable for radiosurgical management due to their typical appearance as well circumscribed lesions.Focused and precise targeting of these well-defined lesions under stereotactic immobilization and image guidance may offer great potential for achieving an improved therapeutic ratio by virtue of radiosurgical techniques.Given the high conformality along with steep dose gradients around the target volume allowing for reduced normal tissue exposure,radiosurgery may be considered a viable modality of radiotherapeutic management.Another advantage of radiosurgery may be the completion of therapy in a usually shorter overall treatment time,which may be particularly well suited for children with requirement of anesthesia during irradiation.Several studies have addressed the utility of radiosurgery particularly as an adjuvant or salvage treatment modality for PA.Nevertheless,despite the growing body of evidence supporting the use of radiosurgery,there is need for a high level of evidence to dictate treatment decisions and establish its optimal role in the management of PA.Herein,we provide a concise review of radiosurgery for PA in light of the literature.

Retreatment in locally recurrent nasopharyngeal carcinoma:Current status and perspectives

1 Background Nasopharyngeal carcinoma(NPC)arises from the epithelial cells that cover and line the nasopharynx.While it is considered a rare cancer globally,it is commonly observed in South China and a few other ethnically distinct racial groups.Due to its propensity to spread early through the submucosal tissue and the highly infiltrative nature of this disease,NPC spreads easily through areas of lesser resistance within the pharyngobasilar fascia with a tendency for neural infiltration[1-3].