This review summarises the evidence showcasing the weak direct correlation between truncal vein diameter and clinical severity of disease, and further elucidates the lack of association between diameter and health related quality of life. The authors report evidence that cautions against using predetermined diameters as thresholds for venous intervention and highlight the importance of both clinical and quality of life assessments in patients with venous disease. This review also highlights specific areas for further investigation, with a focus on the relationship between anatomical assessments and chronic venous disease progression.Objective/background: The aim was to summarise the evidence for the relationship between vein diameters and clinical severity, and elucidate the relationship between diameters and health related quality of life (HRQoL) Methods: A systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. The MEDLINE and Embase databases were searched from 1946 to 31 August 2018. Reference lists of included studies were searched for further relevant papers. Full text studies in English reporting the relationship between great and small saphenous vein diameters and clinical severity and/or HRQoL scores measured using validated instruments were included. All study designs were included. Studies that did not include relationships between these parameters, non-English studies, and studies focusing on non-truncal veins were excluded. Two reviewers independently performed the study selection, data extraction, and risk of bias assessment. Results: Eleven eligible studies were identified, reporting on 2,732 limbs (range 22e681). Four studies correlated truncal vein diameter with both clinical severity and HRQoL, while seven reported only on clinical severity measures. Multiple instruments were used to quantify HRQoL and clinical severity. Seven studies assessed the relationship with CEAP class, with the majority observing a positive correlation between vein diameter and disease severity. Four studies found weak correlations with VCSS, with one showing correlations with VCSS components. No significant relationship between diameters and HRQoL scores was reported. One study also revealed no correlation with Aberdeen Varicose Vein Questionnaire improvements post-treatment. The majority of studies failed to include C 0 and C 1 participants. Conclusions: While further studies are required to improve the level of evidence, the existing literature suggests that truncal vein diameters correlate with clinical severity. Diameters are a poor predictor of HRQoL, with no relationship to patients' perceived impact of chronic venous disease. As such, vein diameter should not be used as a measure to decide who needs venous intervention.
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.
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