Canadian Association of Radiologists Journal-Journal De L Association Canadienne Des Radiologistes最新文献

Introduction: Breast Imaging-Reporting and Data System (BI-RADS) density scores have been included in screening mammography reports in BC since 2018. Despite these density scores being present in screening mammography reports for numerous years, there remains insufficient evidence to guide supplemental testing for patients with dense breasts. Objective: The primary objective of this study was to evaluate how primary care providers in Canada utilize BI-RADS density scores reported on normal screening mammograms of average risk, asymptomatic patients in their clinical practice. The secondary objective of this study was to determine if there are any patterns related to primary care provider demographics and practice settings in BC that could be linked to differences in screening practices for patients based on BI-RADS density scores. Methods: A cross-sectional survey was conducted with family physicians (FPs) and nurse practitioners (NPs) practicing in BC. Descriptive statistics were calculated using percentages and further stratified by participant demographics. P values were derived from Fisher's exact test and results were regarded as statistically significant at P < .05. Results: Ninety-eight participants (85 FPs, 13 NPs) responded to the survey. The percentage of participants who ordered supplemental testing based on BI-RADS density scores alone was 8% for BI-RADS score D, 37% for BI-RADS scores C or D, and 2% for BI-RADS scores B, C, or D. Forty-eight percent of female participants and 45% of male participants would order supplemental testing based on BI-RADS density scores alone (P = 1). Forty-nine percent of FPs and 39% of NPs would order supplemental testing based on BI-RADS density scores (P = .56). Fifty-three percent of participants who had been in practice for more than 10 years, 50% of those who had been in practice for 6 to 10 years, and 36% of those in practice for 5 years or less would order supplemental testing (P = .34). Fifty-seven percent of those practicing in large urban centres, 43% of those practicing in medium-sized communities, and 32% of those in rural or remote communities would order testing (P = .17). Fifty-seven percent of participants were aware of the increased risk of breast cancer with higher breast density. Conclusion: Variations exist in how primary care providers in BC utilize the BI-RADS density scores reported on normal screening mammography of average risk, asymptomatic patients in their clinical practice. Further research in this area is needed to establish clearer clinical guidelines to educate and inform primary care providers on the need for supplemental testing for patients with dense breasts and to improve resources for breast cancer screening in BC.

Purpose: To determine the benefit of a FDG PET/CT scan prior to CT-guided lung biopsy on the rate of diagnosis, rate of complication, and the identification of potentially safer biopsy sites. Methods: This retrospective observational cross-sectional study evaluated consecutive adult patients who underwent CT-guided lung biopsy in 2020 or 2021 at 2 Canadian tertiary care hospitals. These patients were grouped into those that had PET/CT performed within 8 weeks prior to biopsy, within 8 weeks after biopsy, or no PET/CT scan within this time frame. Biopsy complication rates and pathology diagnostic rates were compared. The PET/CT images of those performed after biopsy were reviewed to determine if alternate safer biopsy sites could be identified. Categorical variables were compared using Pearson chi square test (P < .05 significant). Results: 547 patients who had CT-guided lung biopsy were included. Patients with lung masses (≥3 cm) who had a PET/CT scan prior to biopsy had a higher diagnostic rate (90.8%) compared to those that did not (80.2%). The overall post-biopsy pneumothorax rate was 43.3% with 11.3% overall requiring chest tube insertion and 13.9% requiring hospitalization. There was no difference in complication rate for those who had PET/CT prior to biopsy and those that did not. 28.9% to 42.1% of patients who had PET/CT after biopsy had safer sites amenable to biopsy identified retrospectively outside of the lungs. Conclusion: PET/CT prior to CT-guided lung biopsy improves the diagnostic rate in 10.6% of patients with lung masses (≥3 cm) and identifies alternate safer sites to biopsy in 28.9% to 42.1% of patients (any size lesion).

The Canadian Association of Radiologists (CAR) Pediatric Expert Panel is made up of pediatric physicians from the disciplines of radiology, emergency medicine, endocrinology, gastroenterology, general surgery, neurology, neurosurgery, respirology, orthopaedic surgery, otolaryngology, urology, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 50 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 32 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 133 recommendation statements across the 50 scenarios. This guideline presents the methods of development and the referral recommendations for head, neck, spine, hip, chest, abdomen, genitourinary, and non-accidental trauma clinical scenarios.

Peritoneal disease can be classified as either benign or malignant in nature. Malignant peritoneal disease can be further considered as either primary or secondary in origin. Primary peritoneal malignancy includes peritoneal mesothelioma, serous carcinoma, and desmoplastic small round cell tumour. Peritoneal carcinomatosis is the most commonly encountered secondary malignant peritoneal disease, typically of ovarian, gastric, colorectal, pancreatic, small bowel neuroendocrine, or breast origin. Others include peritoneal lymphomatosis and sarcomatosis. Benign peritoneal pathology may mimic malignant disease. Differentiating benign from malignant peritoneal pathology can be challenging, but is critical to guide appropriate care and avoid unnecessary intervention. Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) offers potentially curative treatment for patients with peritoneal carcinomatosis, pseudomyxoma peritonei, and peritoneal mesothelioma. For such patients, the radiologist provides crucial pre-operative information highlighting sites of disease involvement, particularly for sites which are challenging to assess at laparotomy or laparoscopy, including the hepatic dome, subdiaphragmatic space and mesenteric root. The radiologist is also essential to identify potential contraindications to surgery, as well as interpreting normal post-operative appearances, complications and assessing for disease recurrence.

The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising an improvement in healthcare delivery across multiple dimensions. For patients, this technological synergy has the potential to lower the number of unnecessary recalls and associated procedures such as biopsies, thereby reducing patient anxiety and improving overall experience without compromising diagnostic accuracy. For radiologists, the use of combined AI and DBT could significantly decrease workload and reduce fatigue by effectively highlighting breast imaging abnormalities, which is especially beneficial in high-volume clinical settings. Health systems stand to gain from streamlined workflows and the facilitated deployment of DBT, which is particularly valuable in areas with a scarcity of specialized breast radiologists. However, despite these potential benefits, substantial challenges remain. Bridging the gap between the development of complex AI algorithms and implementation into clinical practice requires ongoing research and development. This is essential to optimize the reliability of these systems and ensure they are accessible to healthcare providers and patients, who are the ultimate beneficiaries of this technological advancement. This article reviews the benefits of combined AI-DBT imaging, particularly the ability of AI to enhance the benefits of DBT and reduce its existing limitations.

Purpose: 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. Materials and Methods: 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 t-test was conducted. Results: 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 (P-value .0018). Conclusion: Incorporating tumour location probability maps into CNN models led to significant improvements for molecular subtype identification of pLGG.