Search

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context. [ABSTRACT FROM AUTHOR]

The Canadian Association of Radiologists Incidental Findings Working Group consists of both academic subspecialty and general radiologists and is tasked with adapting and expanding upon the American College of Radiology incidental findings white papers to more closely apply to Canadian practice patterns, particularly more comprehensively dealing with the role of ultrasound and pursuing more cost-effective approaches to the workup of incidental findings without compromising patient care. Presented here are the 2020 Canadian guidelines for the management of hepatobiliary incidental findings. Topics covered include initial assessment of hepatic steatosis and cirrhosis, the workup of incidental liver masses identified on ultrasound and computed tomography (with algorithms presented), incidental gallbladder findings (wall thickening, calcification, and polyps), and management of incidental biliary dilatation. [ABSTRACT FROM AUTHOR]

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 2 of this article will inform CAR members on the practical aspects of medical imaging de-identification, strengths and limitations of de-identification approaches, list of de-identification software and tools available, and perspectives on future directions. [ABSTRACT FROM AUTHOR]

The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboration between different sites or data transfer to a third party, precautions are required to safeguard patient privacy. Safety measures are required to prevent inadvertent access to and transfer of identifiable information. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI Ethical and Legal standing committee with the mandate to guide the medical imaging community in terms of best practices in data management, access to health care data, de-identification, and accountability practices. Part 1 of this article will inform CAR members on principles of de-identification, pseudonymization, encryption, direct and indirect identifiers, k-anonymization, risks of reidentification, implementations, data set release models, and validation of AI algorithms, with a view to developing appropriate standards to safeguard patient information effectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system. [ABSTRACT FROM AUTHOR]

Abstract Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada. [ABSTRACT FROM AUTHOR]

The article discusses the conclusions and recommendations from the position paper on interventional radiology (IR) in Canada. The Millennium Research Group has been commissioned to produce a position paper on IR in the country, as part of a lobbying and public relations campaign initiated by the Canadian Interventional Radiology Association. The position paper has aimed to highlight the current state of IR in the country, to identify disease and treatment trends.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

The Canadian Association of Radiologists Incidental Findings Working Group consists of both academic subspecialty and general radiologists and is tasked with adapting and expanding upon the American College of Radiology incidental findings white papers to more closely apply to Canadian practice patterns, particularly more comprehensively dealing with the role of ultrasound and pursuing more cost-effective approaches to the workup of incidental findings without compromising patient care. Presented here are the 2021 Canadian guidelines for the management of pancreatic incidental findings. Topics covered include anatomic variants, fatty atrophy, pancreatic calcifications, ductal ectasia, and management of incidental pancreatic cysts. [ABSTRACT FROM AUTHOR]

Purpose: To identify trends in female authorship in the Canadian Association of Radiologists Journal (CARJ) from 2010 to 2019. Methods: We retrieved papers published in the CARJ over a 10-year period, and retrospectively reviewed 602 articles. All articles except editorials and advertisements were included. We categorized the names of the first and last position authors as female or male and excluded articles that had at least one author of which gender was not known. We compared the trends in the first and last position authors of the articles from 2010 to 2019. For statistical analysis, logistic regression was performed with reported odds ratios (ORs), and a P value of <.05 was defined as statistically significant. Results: Five hundred thirteen articles met inclusion criteria. Among them, 23 articles with a single author were classified as having only a first author. 39.8% (204/513) of first authors were female and 26.9% (132/490) of last authors were female. There has been an overall temporal increase in the odds of both the first and last author being female in CARJ publications (OR: 1.11, P =.034). Similarly, the odds a CARJ publication's first author being female increased over time (OR: 1.07, P =.033). Female last author did not predict female first author (OR: 1.48, P =.056). There was no association identified between female last author and year of publication (OR: 1.04, P =.225). Conclusion: There has been an overall increase in engagement of female authorship in CARJ. [ABSTRACT FROM AUTHOR]

Purpose: The novel coronavirus disease (COVID-19) pandemic has swept the globe, with a domino effect on medical education and training. In this study, we surveyed Canadian radiology residents to understand the impact of the pandemic on their residency training, strategies utilized by the residency programs in mitigating those impacts, and factors important to residents in the selection of educational resources on COVID-19. Methods: A 10-item questionnaire was distributed to 460 resident members of the Canadian Association of Radiologists. The survey was open for 2 weeks, with a reminder sent at half-way mark. Results: We received 96 responses (response rate: 20.9%). The 4 highest affected domains of training were daytime case volumes (92.4%), daytime schedules (87.4%), internal and external assessments (86.5%), and vacation/travel (83.3%). Virtual teaching rounds (91.7%), change in schedules to allow staying home (78.1%), and virtual/phone readouts (72.9%) were the most utilized strategies by the Canadian radiology residency programs. Overall stress of exposure to the disease was moderate to low (86.5%). A minority of the residents were redeployed (6.2%), although most (68.8%) were on standby for redeployment. Residents preferred published society guidelines (92.3%), review papers (79.3%), video lectures (79.3%), and web tools (76.9%) for learning about COVID-19 imaging manifestations. Conclusion: The COVID-19 pandemic has had a significant impact on various domains of the Canadian radiology residency programs, which has been mitigated by several strategies employed by the training programs. [ABSTRACT FROM AUTHOR]

Several regulatory bodies have agreed that low-dose radiation used in medical imaging is a weak carcinogen that follows a linear, non-threshold model of cancer risk. While avoiding radiation is the best course of action to mitigate risk, computed tomography (CT) scans are often critical for diagnosis. In addition to the as low as reasonably achievable principle, a more concrete method of dose reduction for common CT imaging exams is the use of a diagnostic reference level (DRL). This paper examines Canada's national DRL values from the recent CT survey and compares it to published provincial DRLs as well as the DRLs in the United Kingdom and the United States of America for the 3 most common CT exams: head, chest, and abdomen/pelvis. Canada compares well on the international scale, but it should consider using more electronic dose monitoring solutions to create a culture of dose optimization. [ABSTRACT FROM AUTHOR]

Purpose: To determine the percentage of abstracts presented at the Canadian Association of Radiologists (CAR) annual scientific meetings that go on to publication. Methods: Records of previous CAR meetings from the years 2005-2011 were obtained. An Internet search was performed to determine which abstracts went on to publication. Abstracts were assessed according to exhibit category (Resident Award Papers), educational institution, publishing journal, and time to publication. Results: Of the 402 abstracts presented, 112 (28%) were published. Overall, an average of 37% of Radiologists-In-Training Presentations, 34% of Scientific Exhibits, and 20% of Educational Exhibits went on to publication. The University of British Columbia and University of Ottawa published the largest number of abstracts (66 and 62, respectively) from the years 2005-2011. The University of Montreal had the largest percentage of abstracts published (42%). The range of publishing journals was wide, but the top publisher was the Canadian Association of Radiologists Journal (27%). Eighty-three percent of abstracts were published within 3 years of being presented. Conclusion: In total, 28% of all the abstracts presented at the CAR conferences between 2005 and 2011 were published. Further exploration into the reasons and barriers for abstracts not being published may be a next step in future research. [ABSTRACT FROM AUTHOR]