Arterial spin-labeled (ASL) perfusion in children with Sturge-Weber syndrome: a retrospective cross-sectional study.

PURPOSE: Sturge-Weber syndrome (SWS) is a developmental disorder with venous hypertension and associated tissue responses including pial angiomatosis, cortical calcifications, and cerebral atrophy. Arterial spin-labeled (ASL) perfusion is an advanced MR sequence which can assess perfusion, without the need for contrast. We systematically evaluated the potential benefits of using ASL in Sturge-Weber syndrome, to determine the extent of intracranial perfusion abnormality and stage of disease, relevant for prognostication and surgical planning. METHODS: Two pediatric neuroradiologists retrospectively evaluated ASL perfusion imaging of 31 children with confirmed SWS and recorded the presence of hyper-perfusion, hypo-perfusion, or normal perfusion. The presence and distribution of ASL abnormality were compared against the presence and side of atrophy/calcification and pial angiomatosis on standard MR sequences. RESULTS: Thirty-one children (52% female, median age 16.7 months) with SWS had ASL imaging. Seven (23%) had hyper-perfusion, 15 (48%) had hypo-perfusion, and 9 (29%) had no perfusion abnormalities. ASL perfusion abnormality matched the location of SWS findings on conventional imaging in 86% (19/22). ASL demonstrated statistically significant increased perfusion in the early stage of the disease and decreased perfusion when there was atrophy. The parietal lobe was involved in 86% of cases. CONCLUSION: ASL perfusion imaging is an advanced technique which may contribute to earlier diagnosis and more accurate prognostication of Sturge-Weber syndrome, helping guide management and potential surgical planning.

Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE).

OBJECTIVE: There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed. METHODS: We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered. RESULTS: Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervised learning. The majority of studies undertook a segmentation task (39%). Performance comparison was with a state-of-the-art model in 37%. The most used established architecture was UNet (14%). The median performance for the most utilised evaluation metrics was Dice of 0.89 (range .49-.99), AUC of 0.903 (range 1.00-0.61) and Accuracy of 89.4 (range 70.2-100). Of the 77 studies that externally validated their results and allowed for direct comparison, performance on average decreased by 6% at external validation (range increase of 4% to decrease 44%). CONCLUSION: This systematic review has surveyed the major advances in AI as applied to clinical radiology. KEY POINTS: • While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic.

Radiology artificial intelligence, a systematic evaluation of methods (RAISE): a systematic review protocol.

INTRODUCTION: There has been a recent explosion of research into the field of artificial intelligence as applied to clinical radiology with the advent of highly accurate computer vision technology. These studies, however, vary significantly in design and quality. While recent guidelines have been established to advise on ethics, data management and the potential directions of future research, systematic reviews of the entire field are lacking. We aim to investigate the use of artificial intelligence as applied to radiology, to identify the clinical questions being asked, which methodological approaches are applied to these questions and trends in use over time. METHODS AND ANALYSIS: We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and by the Cochrane Collaboration Handbook. We will perform a literature search through MEDLINE (Pubmed), and EMBASE, a detailed data extraction of trial characteristics and a narrative synthesis of the data. There will be no language restrictions. We will take a task-centred approach rather than focusing on modality or clinical subspecialty. Sub-group analysis will be performed by segmentation tasks, identification tasks, classification tasks, pegression/prediction tasks as well as a sub-analysis for paediatric patients. ETHICS AND DISSEMINATION: Ethical approval will not be required for this study, as data will be obtained from publicly available clinical trials. We will disseminate our results in a peer-reviewed publication. Registration number PROSPERO: CRD42020154790.

Differentiating combined pulmonary fibrosis and emphysema from pure emphysema: utility of late gadolinium-enhanced MRI.

BACKGROUND: Differentiating combined pulmonary fibrosis with emphysema (CPFE) from pure emphysema can be challenging on high-resolution computed tomography (HRCT). This has antifibrotic therapy implications. METHODS: Twenty patients with suspected CPFE underwent late gadolinium-enhanced (LGE) thoracic magnetic resonance imaging (LGE-MRI) and HRCT. Data from twelve healthy control subjects from a previous study who underwent thoracic LGE-MRI were included for comparison. Quantitative LGE signal intensity (SI) was retrospectively compared in regions of fibrosis and emphysema in CPFE patients to similar lung regions in controls. Qualitative comparisons for the presence/extent of reticulation, honeycombing, and traction bronchiectasis between LGE-MRI and HRCT were assessed by two readers in consensus. RESULTS: There were significant quantitative differences in fibrosis SI compared to emphysema SI in CPFE patients (25.8, IQR 18.4-31.0 versus 5.3, IQR 5.0-8.1, p < 0.001). Significant differences were found between LGE-MRI and HRCT in the extent of reticulation (12.5, IQR 5.0-20.0 versus 25.0, IQR 15.0-26.3, p = 0.038) and honeycombing (5.0, IQR 0.0-10.0 versus 20.0, IQR 10.6-20.0, p = 0.001) but not traction bronchiectasis (10.0, IQR 5-15 versus 15.0, IQR 5-15, p = 0.878). Receiver operator curve analysis of fibrosis SI compared to similarly located regions in control subjects showed an area under the curve of 0.82 (p = 0.002). A SI cutoff of 19 yielded a sensitivity of 75% and specificity of 86% in differentiating fibrosis from similarly located regions in control subjects. CONCLUSION: LGE-MRI can differentiate CPFE from pure emphysema and may be a useful adjunct test to HRCT in patients with suspected CPFE.