Enlarging and shrinking focal perivascular spaces.

BACKGROUND AND PURPOSE: Perivascular spaces (PVS) are interstitial fluid-filled spaces surrounding blood vessels traversing the deep gray nuclei and white matter of the brain. These are commonly encountered on CT and MR imaging and are generally asymptomatic and of no clinical significance. However, occasional changes in the size of focal PVS, for example, when enlarging, may mimic pathologies including neoplasms and infections, hence potentially confounding radiological interpretation. Given these potential diagnostic issues, we sought to better characterize common clinical and imaging features of focal PVS demonstrating size fluctuations. MATERIALS AND METHODS: Upon institutional approval, we retrospectively identified 4 cases demonstrating PVS with size changes at our institution. To supplement our cases, we also performed a literature review, which identified an additional 14 cases. Their clinical and imaging data were analyzed to identify characteristic features. RESULTS: Of the 18 total cases (including the 4 institutional cases), 10 cases increased and 8 decreased in size. These focal PVS ranged from 0.4-4.5 cm in size. Whereas a decrease in size did not represent a diagnostic issue, focal increase in size of PVS led to concerning differential diagnoses in at least 30% of the radiology reports. These enlarging PVS were most found in the basal ganglia and temporal lobe, and in patients with previous brain radiation treatment. CONCLUSION: Focal size change of PVS can occur, especially years after brain radiation treatment. Being cognizant of this benign finding is important to consider in the differential diagnosis to avoid undue patient anxiety or unnecessary medical intervention.

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.

BACKGROUND AND PURPOSE: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field. MATERIALS AND METHODS: We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3. RESULTS: A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees. CONCLUSIONS: AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.

The effects of inulin-type fructans on cardiovascular disease risk factors: systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Inulin-type fructans (ITF) are the leading prebiotics in the market. Available evidence provides conflicting results regarding the beneficial effects of ITF on cardiovascular disease risk factors. OBJECTIVES: This study aimed to evaluate the effects of ITF supplementation on cardiovascular disease risk factors in adults. METHODS: We searched MEDLINE, EMBASE, Emcare, AMED, CINAHL, and the Cochrane Library databases from inception through May 15, 2022. Eligible randomized controlled trials (RCTs) administered ITF or placebo (for example, control, foods, diets) to adults for ≥2 weeks and reported one or more of the following: low, very-low, or high-density lipoprotein cholesterol (LDL-C, VLDL-C, HDL-C); total cholesterol; apolipoprotein A1 or B; triglycerides; fasting blood glucose; body mass index; body weight; waist circumference; waist-to-hip ratio; systolic or diastolic blood pressure; or hemoglobin A1c. Two reviewers independently and in duplicate screened studies, extracted data, and assessed risk of bias. We pooled data using random-effects model, and assessed the certainty of evidence (CoE) using the Grading of Recommendations, Assessment, Development and Evaluation approach. RESULTS: We identified 1767 studies and included 55 RCTs with 2518 participants in meta-analyses. The pooled estimate showed that ITF supplementation reduced LDL-C [mean difference (MD) -0.14 mmol/L, 95% confidence interval (95% CI: -0.24, -0.05), 38 RCTs, 1879 participants, very low CoE], triglycerides (MD -0.06 mmol/L, 95% CI: -0.12, -0.01, 40 RCTs, 1732 participants, low CoE), and body weight (MD -0.97 kg, 95% CI: -1.28, -0.66, 36 RCTs, 1672 participants, low CoE) but little to no significant effect on other cardiovascular disease risk factors. The effects were larger when study duration was ≥6 weeks and in pre-obese and obese participants. CONCLUSION: ITF may reduce low-density lipoprotein, triglycerides, and body weight. However, due to low to very low CoE, further well-designed and executed trials are needed to confirm these effects. PROSPERO REGISTRATION NUMBER: CRD42019136745.

Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.

INTRODUCTION: The use of artificial intelligence (AI) in urology is gaining significant traction. While previous reviews of AI applications in urology exist, there have been few attempts to synthesize existing literature on urothelial cancer (UC). METHODS: Comprehensive searches based on the concepts of "AI" and "urothelial cancer" were conducted in MEDLINE , EMBASE , Web of Science, and Scopus. Study selection and data abstraction were conducted by two independent reviewers. Two independent raters assessed study quality in a random sample of 25 studies with the prediction model risk of bias assessment tool (PROBAST) and the standardized reporting of machine learning applications in urology (STREAM-URO) framework. RESULTS: From a database search of 4581 studies, 227 were included. By area of research, 33% focused on image analysis, 26% on genomics, 16% on radiomics, and 15% on clinicopathology. Thematic content analysis identified qualitative trends in AI models employed and variables for feature extraction. Only 19% of studies compared performance of AI models to non-AI methods. All selected studies demonstrated high risk of bias for analysis and overall concern with Cohen's kappa (k)=0.68. Selected studies met 66% of STREAM-URO items, with k=0.76. CONCLUSIONS: The use of AI in UC is a topic of increasing importance; however, there is a need for improved standardized reporting, as evidenced by the high risk of bias and low methodologic quality identified in the included studies.

MR Imaging in Primary Sclerosing Cholangitis and Other Cholangitis.

Sclerosing cholangitis is characterized by irregular and ill-defined inflammation, fibrosis, and stricturing of the bile ducts and may be primary or secondary in cause. It causes progressive destruction of the biliary tree, progressive parenchymal cirrhosis, hepatic failure, and malignancy such as cholangiocarcinoma. The subtypes of sclerosing cholangitis can be broken down into primary sclerosing cholangitis (PSC), immune-mediated sclerosing cholangitis, infectious cholangitis, and ischemic cholangitis with other causes including metastatic disease and chemotherapy change. MR imaging is the gold standard test to investigate and differentiate PSC and other types of cholangitis.