Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use,Perceptions of and Barriers to Implementation

AimsArtificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors.Materials and MethodsThe Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters.ResultsIn total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country.ConclusionCareful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.     

Radiotherapy Standardisation and Artificial Intelligence within the National Cancer Institute's Clinical Trials Network

Artificial intelligence in healthcare refers to the use of complex algorithms designed to conduct certain tasks in an automated manner. Artificial intelligence has a transformative power in radiation oncology to improve the quality and efficiency of patient care, given the increase in volume and complexity of digital data, as well as the multi-faceted and highly technical nature of this field of medicine. However, artificial intelligence alone will not be able to fix healthcare's problem, because new technologies bring unexpected and potentially underappreciated obstacles. The inclusion of multicentre datasets, the incorporation of time-varying data, the assessment of missing data as well as informative censoring and the addition of clinical utility could significantly improve artificial intelligence models. Standardisation plays a crucial, supportive and leading role in artificial intelligence. Clinical trials are the most reliable method of demonstrating the efficacy and safety of a treatment or clinical approach, as well as providing high-level evidence to justify artificial intelligence. The National Surgical Adjuvant Breast and Bowel Project, the Radiation Therapy Oncology Group and the Gynecologic Oncology Group collaborated to form NRG Oncology (acronym NRG derived from the names of the parental groups). NRG Oncology is one of the adult cancer clinical trial groups containing radiotherapy specialty of the National Cancer Institute's Clinical Trials Network (NCTN). Standardisation from NRG/NCTN has the potential to reduce variation in clinical treatment and patient outcome by eliminating potential errors, enabling broader application of artificial intelligence tools. NCTN, NRG and Imaging and Radiation Oncology Core are in a unique position to help with standards development, advocacy and enforcement, all of which can benefit from artificial intelligence, as artificial intelligence has the ability to improve trial success rates by transforming crucial phases in clinical trial design, from study planning through to execution. Here we will examine: (i) how to conduct technical and clinical evaluations before adopting artificial intelligence technologies, (ii) how to obtain high-quality data for artificial intelligence, (iii) the NCTN infrastructure and standards, (iv) radiotherapy standardisation for clinical trials and (v) artificial intelligence applications in standardisation.     

人工智能在肿瘤放射治疗中的研究进展

大数据和人工智能在各行各业方兴未艾,在精准放疗领域也在不断深入。近些年来,放射治疗技术发展迅速,积累了大量的影像和治疗数据,接下来迫切需要进行大数据分析,来协助医生做决策,提高医生和物理师的工作效率,加强放疗过程的质控,因此人工智能在放疗领域应运而生。本文阐述了人工智能在放射治疗各环节中的发展前景和应用情况。     

Big Data: the challenge for small research groups in the era of cancer genomics

In the past decade, cancer research has seen an increasing trend towards high-throughput techniques and translational approaches. The increasing availability of assays that utilise smaller quantities of source material and produce higher volumes of data output have resulted in the necessity for data storage solutions beyond those previously used. Multifactorial data, both large in sample size and heterogeneous in context, needs to be integrated in a standardised, cost-effective and secure manner. This requires technical solutions and administrative support not normally financially accounted for in small- to moderate-sized research groups. In this review, we highlight the Big Data challenges faced by translational research groups in the precision medicine era; an era in which the genomes of over 75 000 patients will be sequenced by the National Health Service over the next 3 years to advance healthcare. In particular, we have looked at three main themes of data management in relation to cancer research, namely (1) cancer ontology management, (2) IT infrastructures that have been developed to support data management and (3) the unique ethical challenges introduced by utilising Big Data in research.     

Biobanking in the digital pathology era

Digital Pathology is becoming more and more important to achieve the goal of precision medicine. Advances in whole-slide imaging, software integration, and the accessibility of storage solutions have changed the pathologists’ clinical practice, not only in terms of laboratory workflow but also for diagnosis and biomarkers analysis. In parallel with the pathology setting advancement, translational medicine is approaching the unprecedented opportunities unrevealed by artificial intelligence (AI). Indeed, the increased usage of biobanks’ datasets in research provided new challenges for AI applications, such as advanced algorithms, and computer-aided techniques. In this scenario, machine learning-based approaches are being propose in order to improve biobanks from biospecimens collection repositories to computational datasets. To date, evidence on how to implement digital biobanks in translational medicine is still lacking. This viewpoint article summarizes the currently available literature that supports the biobanks’ role in the digital pathology era, and to provide possible practical applications of digital biobanks.     

Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology.     

Multi-Agent Systems: Effective Approach for Cancer Care Information Management

Physicians, in order to study the causes of cancer, detect cancer earlier, prevent or determine the effectivenessof treatment, and specify the reasons for the treatment ineffectiveness, need to access accurate, comprehensive, andtimely cancer data. The cancer care environment has become more complex because of the need for coordinationand communication among health care professionals with different skills in a variety of roles and the existenceof large amounts of data with various formats. The goals of health care systems in such a complex environmentare correct health data management, providing appropriate information needs of users to enhance the integrityand quality of health care, timely access to accurate information and reducing medical errors. These roles innew systems with use of agents efficiently perform well. Because of the potential capability of agent systems tosolve complex and dynamic health problems, health care system, in order to gain full advantage of E- health,steps must be taken to make use of this technology. Multi-agent systems have effective roles in health servicequality improvement especially in telemedicine, emergency situations and management of chronic diseasessuch as cancer. In the design and implementation of agent based systems, planning items such as informationconfidentiality and privacy, architecture, communication standards, ethical and legal aspects, identificationopportunities and barriers should be considered. It should be noted that usage of agent systems only with atechnical view is associated with many problems such as lack of user acceptance. The aim of this commentary is tosurvey applications, opportunities and barriers of this new artificial intelligence tool for cancer care informationas an approach to improve cancer care management.     

放射治疗计划自动化分级建议北大核心CSCD

在大数据和人工智能的助力下,放射治疗计划自动化的研究及临床应用发展迅速。放疗计划自动化系统的使用和监管等工作,需要仔细考虑自动化的程度及其适用环境。对于自动驾驶车辆,国内外定义了其自动化水平,但对于放疗计划自动化没有类似的定义。为了促进和规范放疗计划自动化发展,并激发行业讨论,我们参考汽车驾驶自动化分级制定了此分级建议,将放疗计划自动化分成6个等级(1级至6级)。     

Cancer in Lockdown: Impact of the COVID‐19 Pandemic on Patients with Cancer

The lockdown measures of the ongoing COVID‐19 pandemic have disengaged patients with cancer from formal health care settings, leading to an increased use of social media platforms to address unmet needs and expectations. Although remote health technologies have addressed some of the medical needs, the emotional and mental well‐being of these patients remain underexplored and underreported. We used a validated artificial intelligence framework to conduct a comprehensive real‐time analysis of two data sets of 2,469,822 tweets and 21,800 discussions by patients with cancer during this pandemic. Lung and breast cancer are most prominently discussed, and the most concerns were expressed regarding delayed diagnosis, cancellations, missed treatments, and weakened immunity. All patients expressed significant negative sentiment, with fear being the predominant emotion. Even as some lockdown measures ease, it is crucial that patients with cancer are engaged using social media platforms for real‐time identification of issues and the provision of informational and emotional support.     

Breast cancer control among the underserved — An overview

Summary This paper explores barriers to the use of standard screening and breast cancer treatment that result in systematic differences in health outcomes. We review available data on individual, socioeconomic, and health system determinants of access to standard breast cancer care, including screening, diagnostic, and treatment services. Based on this review, we discuss the combination of factors which result in underservice. We argue that a broad framework which considers health system and social class as well as individual factors is useful for analyzing how structures of health care delivery tend to provide less than standard care to women who are older, have less income, or are less educated, black, or Hispanic. Data collection efforts which do not include structural and socioeconomic variables may result in an incomplete or misleading understanding of the determinants of underservice. These factors also need to be considered in the design and evaluation of public health policies and interventions meant to ameliorate the effects of underservice.     

Deep Learning for Radiotherapy Outcome Prediction Using Dose Data – A Review

Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. We review and summarise studies that applied deep learning to radiotherapy dose data, in particular studies that utilised full three-dimensional dose distributions. Ten papers have reported on deep learning models for outcome prediction utilising spatial dose information, whereas four studies used reduced dimensionality (dose volume histogram) information for prediction. Many of these studies suffer from the same issues that plagued early normal tissue complication probability modelling, including small, single-institutional patient cohorts, lack of external validation, poor data and model reporting, use of late toxicity data without taking time-to-event into account, and nearly exclusive focus on clinician-reported complications. They demonstrate, however, how radiation dose, imaging and clinical data may be technically integrated in convolutional neural networks-based models; and some studies explore how deep learning may help better understand spatial variation in radiosensitivity. In general, there are a number of issues specific to the intersection of radiotherapy outcome modelling and deep learning, for example translation of model developments into treatment plan optimisation, which will require further combined effort from the radiation oncology and artificial intelligence communities.     

Racial Bias Can Confuse AI for Genomic Studies

Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work, we performed comprehensive analyses taking The Cancer Genome Atlas (TCGA) project as a case study. We construct a survival model as well as multiple artificial intelligence prediction models to analyze potential confusion caused by racial bias. From the genomic discovery, we demonstrated cancer associated genes identified from the major race hardly overlap with the discoveries from minor races from the same causal gene discovery model. We demonstrated that the biased racial distribution will greatly affect the cancer-associated genes, even taking the racial identity as a confounding factor in the model. The prediction models will be potentially risky and less accurate due to the existence of racial bias in projects. Cancer genes from the overall patient model with strong racial bias will be less informative to the minor races. Meanwhile, when the racial bias is less severe, the major conclusion from the overall analysis can be less useful even for the major group.     

Improving the Quality of Care in Radiation Oncology using Artificial Intelligence

Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance.     

New prognostic factors and calculators for outcome prediction in patients with recurrent glioblastoma: a pooled analysis of EORTC Brain Tumour Group phase I and II clinical trials

BackgroundPrognostic models have been developed to predict survival of patients with newly diagnosed glioblastoma (GBM). To improve predictions, models should be updated with information at the recurrence. We performed a pooled analysis of European Organization for Research and Treatment of Cancer (EORTC) trials on recurrent glioblastoma to validate existing clinical prognostic factors, identify new markers, and derive new predictions for overall survival (OS) and progression free survival (PFS).MethodsData from 300 patients with recurrent GBM recruited in eight phase I or II trials conducted by the EORTC Brain Tumour Group were used to evaluate patient’s age, sex, World Health Organisation (WHO) performance status (PS), presence of neurological deficits, disease history, use of steroids or anti-epileptics and disease characteristics to predict PFS and OS. Prognostic calculators were developed in patients initially treated by chemoradiation with temozolomide.ResultsPoor PS and more than one target lesion had a significant negative prognostic impact for both PFS and OS. Patients with large tumours measured by the maximum diameter of the largest lesion (⩾42 mm) and treated with steroids at baseline had shorter OS. Tumours with predominant frontal location had better survival. Age and sex did not show independent prognostic values for PFS or OS.ConclusionsThis analysis confirms performance status but not age as a major prognostic factor for PFS and OS in recurrent GBM. Patients with multiple and large lesions have an increased risk of death. With these data prognostic calculators with confidence intervals for both medians and fixed time probabilities of survival were derived.     

Space-time clustering patterns in childhood leukaemia support a role for infection

Previous studies of space-time clustering in childhood leukaemia have produced equivocal and inconsistent results. To address this issue we have used Manchester Children's Tumour Registry leukaemia data in space-time clustering analyses. Knox tests for space-time interactions between cases were applied with fixed thresholds of close in space, <5 km and close in time <1 year apart. Addresses at birth as well as diagnosis were utilized. Tests were repeated replacing geographical distance with distance to the Nth nearest neighbour. N was chosen such that the mean distance was 5 km. Data were also examined by a second order procedure based on K-functions. All methods showed highly significant evidence of space-time clustering based on place of birth and time of diagnosis, particularly for all leukaemias aged 0-14 and 0-4 years, and acute lymphoblastic leukaemia (ALL) 0-4 years. Some results based on location at diagnosis were significant but mainly gave larger P-values. The results are consistent with an infectious hypothesis. Furthermore, we found an excess of male cases over females involved in space-time pairs. We suggest this may be related to genetic differences in susceptibility to infection between males and females. These findings provide the basis for future studies to identify possible infectious agents.     

Derivation of molecular signatures for breast cancer recurrence prediction using a two-way validation approach

Previous studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical breast cancer recurrence, however, many of these predictive models have been derived using simple computational algorithms and validated internally or using one-way validation on a single dataset. We have recently developed a new feature selection algorithm that overcomes some limitations inherent to high-dimensional data analysis. In this study, we applied this algorithm to two publicly available gene expression datasets obtained from over 400 patients with breast cancer to investigate whether we could derive more accurate prognostic signatures and reveal common predictive factors across independent datasets. We compared the performance of three advanced computational algorithms using a robust two-way validation method, where one dataset was used for training and to establish a prediction model that was then blindly tested on the other dataset. The experiment was then repeated in the reverse direction. Analyses identified prognostic signatures that while comprised of only 10–13 genes, significantly outperformed previously reported signatures for breast cancer evaluation. The cross-validation approach revealed CEGP1 and PRAME as major candidates for breast cancer biomarker development.     

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning

In this review, we aim to assess the current state of science in relation to the integration of patient‐generated health data (PGHD) and patient‐reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine‐learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.     

Contribution of expert systems in clinical practice: apropos of the Penelope experience, an expert system in assisted diagnosis and therapy of ovarian adenocarcinoma]

An expert system (ES) for Diagnosis and Therapy of ovarian adenocarcinoma has been developed at the Institut Gustave-Roussy. From surgical and histological results, clinical examination and additional investigative reports, the system presents a synthesis and then determines the stage of the disease. The system than proposes therapeutic indications adapted to the characteristics of the illness and of the patient, and edits a report at the end of the ES consultation. This experience allowed us to specify the field of ES applications in oncology. As tools for diagnosis and therapy, they cannot act as a substitute for the know-how of the physician, as too many medical decisions remain difficult to formalize in the ES. On the other hand the use of artificial intelligence techniques appears to be useful for establishing coherent data bases, which are necessary pre-requisites for clinical research in oncology. The integration of the system in the Hospital Information System is the guarantee of its use in current clinical practice.