Functional and efficient medical equipment is at the core of modern healthcare delivery, particularly in medical imaging. Growing healthcare costs and constrained budgets can delay equipment renewal. Aging equipment risks malfunction, potentially causing injury to patients and staff, and downtimes delaying patient care and impacting departmental revenue. Extensive equipment failure can lead to significant operational disruption which can compromise the delivery of timely and quality healthcare. Although extensive equipment failure is uncommon, 2 interventional radiology divisions at tertiary academic hospitals in Canada and the UK recently faced such a crisis. Their experiences of crisis and recovery inform this review of angiography equipment failure, and the principles learned. The concept of organizational resilience is introduced as a framework through which we review the crises. This concept can be split into successive and cooperative stages of anticipation, coping, and adaptation. Resilient organizations can identify potential threats, cope with unexpected crises, and recover swiftly to ensure future success. The author's experience of critical angiography unit failure, their response, and lessons learned are reviewed. We find these principles are broadly applicable to other medical imaging divisions and relevant to any system reliant on technology for healthcare delivery.

Pediatric low-grade gliomas (pLGG) are the most common brain tumour in children, and the molecular diagnosis of pLGG enables targeted treatment. We use MRI-based Convolutional Neural Networks (CNNs) for molecular subtype identification of pLGG and augment the models using tumour location probability maps. MRI FLAIR sequences of 214 patients (110 male, mean age of 8.54 years, 143 BRAF fused and 71 BRAF V600E mutated pLGG tumours) from January 2000 to December 2018 were included in this retrospective REB-approved study. Tumour segmentations (volumes of interest-VOIs) were provided by a pediatric neuroradiology fellow and verified by a pediatric neuroradiologist. Patients were randomly split into development and test sets with an 80/20 ratio. The 3D binary VOI masks for each class in the development set were combined to derive the probability density functions of tumour location. Three pipelines for molecular diagnosis of pLGG were developed: location-based, CNN-based, and hybrid. The experiment was repeated 100 times each with different model initializations and data splits, and the Areas Under the Receiver Operating Characteristic Curve (AUROC) was calculated, and Student's -test was conducted. The location-based classifier achieved an AUROC of 77.9, 95% confidence interval (CI) (76.8, 79.0). CNN-based classifiers achieved an AUROC of 86.1, 95% CI (85.0, 87.3), while the tumour-location-guided CNNs outperformed the other classifiers with an average AUROC of 88.64, 95% CI (87.6, 89.7), which was statistically significant (-value .0018). Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.

In tandem with the ever-increasing global population, the demand for diagnostic radiology service provision is on the rise and at a disproportionate rate compared to the number of radiologists available to practice. The current "revolution in robotics" promises to alleviate personnel shortages in many sectors of industry, including medicine. Despite negative depictions of robots in popular culture, their multiple potential benefits cannot be overlooked, in particular when it comes to health service provision. The type of robots used for interventional procedures are largely robotic-assistance devices, such as the Da Vinci surgical robot. Advances have also been made with regards to robots for image-guided percutaneous needle placement, which have demonstrated superior accuracy compared to manual methods. It is likely that artificial intelligence will come to play a key role in the field of robotics and will result in an increase in the levels of robotic autonomy attainable. However, this concept is not without ethical and legal considerations, most notably who is responsible should an error occur; the physician, the robot manufacturer, software engineers, or the robot itself? Efforts have been made to legislate in order to protect against the potentially harmful effects of unexplainable "black-box" decision outputs of artificial intelligence systems. In order to be accepted by patients, studies have shown that the perceived level of trustworthiness and predictability of robots is crucial. Ultimately, effective, widespread implementation of medical robotic systems will be contingent on developers remaining cognizant of factors that increase human acceptance, as well as ensuring compliance with regulations.

Use a tailored version of the Quality Assessment of Diagnostic Accuracy Studies tool to evaluate risk of bias and applicability across LIRADS related publications. A tailored QUADAS-2 tool was created through consensus approach to assess risk of bias and applicability across 37 LI-RADS related publications. Studies were selected from 2017 to 2022 using the assistance of experienced hospital librarians to search for studies evaluating the diagnostic accuracy of CT, MRI, or contrast-enhanced ultrasound for HCC using LI-RADS through multiple different databases. QUADAS-2 assessments were performed in duplicate and independently by 2 authors with experience using the QUADAS-2 tool. Disagreements were resolved with a third expert reviewer. Consensus QUADAS-2 assessments were tabulated for each domain. Using the tailored QUADAS-2 tool, 31 of the 37 included LI-RADS studies were assessed as high risk of bias, and 9 out of 37 studies demonstrated concerns for applicability. Patient selection (21 out of 37 studies) and flow/timing (24 out of 37 studies) domains demonstrated the highest risk of bias. 6 out of 37 studies in the index domain demonstrated high risk of bias. 2 out of 37 studies showed high risk of bias in the reference standard domain. A significant proportion of LI-RADS research is at risk of bias with concerns for applicability. Identifying risk of bias in such research is essential to recognize limitations of a study that may affect the validity of the results. Areas for improvement in LI-RADS research include reducing selection bias, avoiding inappropriate exclusions, and decreasing verification bias.

To evaluate whether single-exposure, dual-energy chest X-ray (DEX) improves visualization of coronary artery calcium (CAC) and valve/vascular calcifications compared to conventional X-ray. Sixty-one bone-marrow transplant patients (22- 79 years; median 61; IQR 15; w/m, 24/37), underwent single-exposure dual-energy X-ray (Reveal 35C, KA imaging) in pa and lateral projection, followed by a standard-of-care chest CT. Two DEX pairs (pa/lateral) were calculated: a composite image (COMP) and a bone image with soft-tissue subtraction (BI). The COMP pair was reviewed by 2 chest radiologists, assessing the presence/absence of CAC and valve/vascular calcifications on a confidence scale from -2 (confidently not present) to 2 (confidently present). Subsequently, the BI pair was revealed, and readers reevaluated both pairs (COMP and BI) jointly using the identical scale. CTCAC scores were categorized according to the CAC-DRS (0-3) and served as standard of reference, valve/vascular calcifications were categorized on CT as present or absent. For detecting CAC on DEX in any CAC-DRS category (1-3), in category 2-3, in category 3, and for valve/vascular calcifications, the ROC-AUC (combined for both readers) for COMP images was 0.74 (CI: 0.64-0.84), 0.81 (CI: 0.68-0.94), 0.84 (CI: 0.69-0.98), and 0.90 (CI: 0.83-0.99), and for the BI images 0.91 (CI: 0.83-0.98), 0.94 (CI: 0.86- 1.00), 0.89 (CI: 0.77-1.00), and 0.98 (CI: 0.96-1.00), with = .0003, = .044, = .42, and = .55, respectively. The Intraclass-Correlation-Coefficient (ICC) for CAC on COMP/BI was 0.973/0.954, and for valve/vascular calcifications 0.971/0.965. Single-exposure, dual-energy acquisition improves diagnostic confidence for coronary artery calcium and valve/vascular calcification identification on chest X-rays.

The Canadian Association of Radiologists (CAR) Spine Expert Panel is made up of physicians from the disciplines of radiology, emergency medicine, neurology, neurosurgery, physiatry, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 10 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 23 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 22 recommendation statements across the 8 scenarios (one scenario points to the CAR Trauma Referral Guideline and one scenario points to the CAR Musculoskeletal Guideline). This guideline presents the methods of development and the referral recommendations for myelopathy, suspected spinal infection, possible atlanto-axial instability (non-traumatic), axial pain (non-traumatic), radicular pain (non-traumatic), cauda equina syndrome, suspected spinal tumour, and suspected compression fracture. Spondyloarthropathies and spine trauma point to other CAR Diagnostic Imaging Referral Guidelines, Musculoskeletal and Trauma, respectively.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

Radiology departments are increasingly tasked with managing growing demands on services including long waitlists for scanning and interventional procedures, human health resource shortages, equipment needs, and challenges incorporating advanced imaging solutions. The burden of system inefficiencies and the overuse of "low-value" imaging causes downstream impact on patients at the individual level, the economy and healthcare system at the societal level, and planetary health at an overarching level. Low value imaging includes those performed for an inappropriate clinical indication, with little to no value to the management of the patient, and resulting in healthcare resource waste; it is estimated that up to a quarter of advanced imaging studies in Canada meet this criterion. Strategies to reduce low-value imaging include the development and use of referral guidelines, use of appropriateness criteria, optimization of existing protocols, and integration of clinical decision support tools into the ordering provider's workflow. Additional means of optimizing system efficiency such as centralized intake models, improved access to electronic medical records and outside imaging, enhanced communication with patients and referrers, and the utilization of artificial intelligence will further increase the value of radiology provided to patients and care providers.

Peripartum cardiomyopathy (PPCM) affects women in late pregnancy and postpartum. Cardiovascular magnetic resonance (CMR) can contribute to PPCM diagnosis and management. We explored CMR findings in PPCM, including myocardial strain and late gadolinium enhancement (LGE) patterns. This retrospective single-centre study included patients with PPCM who underwent CMR from 2010 to 2018. Exclusions were other cardiomyopathy causes. CMR parameters, including ventricular function, LGE, and myocardial strain, were compared between the PPCM group and healthy controls. Transthoracic echocardiographic data were reviewed to assess functional improvement in PPCM patients. Thirty-two women with PPCM (mean age 42 ± 6 years) and 26 controls (mean age 43 ± 14 years) were included. PPCM patients had significantly lower left ventricular (LV) ejection fractions (median 37.5% vs 60.5%, < .001), higher LV end-diastolic volumes (median 108 ml/m² vs 76 ml/m², < .001), and reduced global LV strain compared to controls. Eighteen PPCM patients (58%) had non-ischaemic pattern LGE, with no LGE in controls besides hingepoint LGE (23%). LGE was most prevalent in the basal and mid anteroseptum. LGE patterns included linear mid-wall, subepicardial, and right ventricular side of the septum. Twenty-four patients (92%) showed improvement in LVEF at follow-up echocardiogram (mean LVEF 28% ± 1.9% at diagnosis and 45% ± 3% at follow-up, < .001). We identified a non-ischaemic pattern LGE that is nonspecific in isolation but could suggest PPCM in the correct clinical context along with abnormal CMR strain values. Future studies should evaluate the clinical application of these findings to facilitate earlier diagnosis and enhance management.

Vascular Ehlers-Danlos syndrome (vEDS) is a rare and aggressive heritable aortic disease caused by pathogenic variants in COL3A1 gene, characterized by spontaneous arterial dissection and organ rupture. The purpose of this study is to evaluate ventricular size and function and to explore their associations with complications in vEDS. Adults with genetically confirmed vEDS who underwent clinical cardiac MRI were retrospectively compared with controls matched for age and sex. Cardiac MRI analysis included assessment of ventricular volumetry and arterial vasculature. vEDS-related complications were evaluated including dissection, aneurysm, and pneumothorax. Multivariable logistic regression was performed. We studied 26 individuals with vEDS (38.6 ± 15.6 years, 50.0% female) and 26 healthy controls. Median clinical follow-up was 2.4 (1.1-3.6) years. Left and right ventricular ejection fractions were lower in vEDS compared with controls (LVEF 58 ± 6% vs 61 ± 4%, = .03; RVEF 54 ± 5% vs 58 ± 4%, = .03). After controlling for age, sex, and antihypertensive medication, LV end-diastolic volume indexed to body surface area (LVEDVi) predicted dissections (OR 1.1, 95% CI 1.01-1.2, = .04) and aneurysms (OR 1.1, 95% CI 1.01-1.3, = .03). Indexed LV end systolic volume (LVESVi) also predicted aneurysms (OR 1.2, 95% CI 1.03-1.5, = .02). LVEF predicted the presence of any complication (OR 0.71, 95% CI 0.52-0.99, = .04). Pneumothorax occurred exclusively in vEDS group among those with LVEF <58% (below the mean), 50.0% versus 0.0%, = .02. Those with LVEF <58% had more frequent dissection and/or aneurysm (75.0% vs 12.5%, = .04). Lower LVEF and larger cardiac size are associated with complications in vEDS.

Assess the effectiveness of standardizing multidisciplinary case conferences (MDCs). Anonymous electronic surveys gauged opinions of abdominal radiologists engaged in recurring MDCs. A standardized Excel template, following Cancer Care Ontario guidelines and relevant literature, was distributed to MDC managers. Physicians were instructed to send cases 36 hours prior to MDC. Template adherence was assessed at 1.5 and 8 months. A follow-up survey at 4 months evaluated the intervention's effectiveness. 27/34 abdominal radiologists provided 47 baseline responses, and 12 delegated radiologists provided 23 follow-up responses. "Often/always" being provided the image's location increased from 36% (17/47) at baseline to 70% (16/23) at follow-up. Non-adherence to the 36-hour cut-off decreased from 36% (16/45) to 17% (4/23). 72% disagreed that uploading remote imaging to hospital servers is easy (33/46), similar to follow-up (18/23, 78%). In assessing the intervention, 41% noted improved standardization (9/22), another 41% considered MDCs already standardized (9/22), and 18% reported no change (4/22). Those reporting no change experienced a higher frequency of non-adherence to the 36-hour cut-off (3/4, 75%) than others (1/18, 6%), and less frequent "often/always" ratings for image location being provided (3/4, 75%) than others (2/18, 11%). 89% (25/28) of MDCs adhered to the template. Issues regarding last-minute add-on cases may be mitigated through EPIC force functions. Artificial intelligence advancements may assist in retrieving external images and patient information. Adherence to MDC standardization was high, allowing for more efficient preparation, potentially reducing radiologist administrative burdens. Future force functions and artificial intelligence integration into electronic patient records may further augment this.

The health of Canadians is already impacted by climate change due to wildfire smoke, heat domes, floods, droughts, and the changing distribution of vector borne disease. The healthcare sector contributes to climate change, accounting for approximately 4.6% of annual greenhouse gas emissions in Canada. Healthcare teams have a responsibility and opportunity to reduce harm by limiting emissions and waste, and engaging the public in understanding the planetary health links between clean air and water, a stable climate, a healthy planet and human health. Transformation of Canadian healthcare to a low carbon, climate resilient system will be enhanced by physician engagement and leadership. Cornerstones to physician participation include knowledge of the anthropogenic etiology of the climate crisis, the human health impacts, and the contribution providing healthcare makes to the climate crisis. Integration of climate change knowledge into the Canadian Radiology educational curricula is essential to position radiologists to lead transformative change in mitigation and adaptation of the healthcare system to the climate crisis. This statement is intended to provide guidelines to optimize education and research for current and future Canadian radiologists, and builds on existing planetary healthcare education publications and the Canadian Association of Radiologists Statement on Environmental Sustainability in Medical Imaging.

To evaluate the clinical performance of a Protocol Recommendation System (PRS) automatic protocolling of chest CT imaging requests. 322 387 consecutive historical imaging requests for chest CT between 2017 and 2022 were extracted from a radiology information system (RIS) database containing 16 associated patient information values. Records with missing fields and protocols with <100 occurrences were removed, leaving 18 protocols for training. After freetext pre-processing and applying CLEVER terminology word replacements, the features of a bag-of-words model were used to train a multinomial logistic regression classifier. Four readers protocolled 300 clinically executed protocols (CEP) based on all clinically available information. After their selection was made, the PRS and CEP were unblinded, and the readers were asked to score their agreement (1 = severe error, 2 = moderate error, 3 = disagreement but acceptable, 4 = agreement). The ground truth was established by the readers' majority selection, a judge helped break ties. For the PRS and CEP, the accuracy and clinical acceptability (scores 3 and 4) were calculated. The readers' protocolling reliability was measured using Fleiss' Kappa. Four readers agreed on 203/300 protocols, 3 on 82/300 cases, and in 15 cases, a judge was needed. PRS errors were found by the 4 readers in 1%, 2.7%, 1%, and 0.7% of the cases, respectively. The accuracy/clinical acceptability of the PRS and CEP were 84.3%/98.6% and 83.0%/99.3%, respectively. The Fleiss' Kappa for all readers and all protocols was 0.805. The PRS achieved similar accuracy to human performance and may help radiologists master the ever-increasing workload.

Image-guided tumour ablation is a minimally invasive treatment for early stage hepatocellular carcinoma (HCC). Our study reviews the complications and long term outcomes in patients treated at a tertiary referral centre. Retrospective study. All patients with HCC who underwent microwave ablation (MWA) or radiofrequency ablation (RFA) from 1st January 2014 to 31st December 2022 were identified. Treatment response of target lesion, complications, and survival were recorded. One hundred seventy ablations were performed in 118 patients; 70% MWA, 30% RFA. Median radiological follow-up 21 months (range 3-107). Follow-up imaging was reported using LI-RADS and mRECIST. At first follow-up imaging, 94 patients had complete response (primary efficacy rate 80.3%) while 19.7% (n = 23) had residual disease. Fifteen of these had repeat ablation; 10 had complete response (secondary efficacy rate 85.6%). By end of study duration, 70.5% (n = 79) achieved sustained local complete response from single ablation without documented recurrence. 14.3% (n = 16) required more than one ablation of target lesion. Overall, 84.8% (n = 95) demonstrated long term local complete response to ablation. Complication occurred in 5.9% (n = 10); 40.0% Grade I, 40.0% Grade II, 10.0% Grade III, 10.0% Grade IV as per the CIRSE Classification. 1-, 3-, and 5-year overall survival (OS) rate was 97%, 68%, and 61% respectively. Mean OS was 5.3 years (median 4.7). No difference in OS ( = .7) or local progression free survival ( = .5) between patients treated with MWA versus RFA. This study demonstrates excellent long-term response to TA, with acceptable complication profile. No difference in survival between RFA versus MWA.