AbstractRisk factors for deep-vein thrombosis have been shown not to be always the same as for pulmonary embolism. A well-known example is the factor V Leiden (FVL) paradox: the FVL mutation poses a clearly higher risk for deep-vein thrombosis (DVT) than for pulmonary embolism. We aimed to expand this paradox and therefore present risk estimates for several established risk factors for DVT and pulmonary embolism separately. When such separate risk estimates could not be retrieved from the literature, we calculated these risks in our own data, a large population-based case-control study on venous thrombosis (the MEGA study). Our results showed that the FVL paradox can be broadened (ie, the risk factors oral contraceptive use, pregnancy, puerperium, minor leg injuries, and obesity have an effect comparable with FVL). Furthermore, we found that pulmonary conditions, such as chronic obstructive pulmonary disease, pneumonia, and sickle cell disease, were risk factors with an opposite effect: a higher risk of pulmonary embolism, but little or no effect on DVT. These findings suggest that pulmonary embolism and DVT may not always have the same etiology, and encourage unraveling this phenomenon in further studies.

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The plain radiographic features of gout are well known; however, the sensitivity of plain radiographs alone for the detection of signs of gout is poor in acute disease. Radiographic abnormalities do not manifest until late in the disease process, after significant joint and soft tissue damage has already occurred. The advent of dual-energy computed tomography (DECT) has enabled the non-invasive diagnosis and quantification of gout by accurately confirming the presence and extent of urate crystals in joints and soft tissues, without the need for painful and often unreliable soft tissue biopsy or joint aspiration. Specific ultrasound findings have been identified and may also be used to aid diagnosis. Both ultrasound and magnetic resonance imaging (MRI) may be used for the measurement of disease extent, monitoring of disease activity or treatment response, although MRI findings are nonspecific. In this article we summarize the imaging findings and diagnostic utility of plain radiographs, ultrasound, DECT, MRI and nuclear medicine studies in the assessment as well as the implications and utility these tools have for measuring disease burden and therapeutic response.

Objective-Age is the strongest risk factor for venous thrombosis. Vessel wall changes such as thickening of venous valves may be one of the contributing mechanisms. We determined thickness and function of venous valves in the popliteal vein with ultrasound in 77 healthy individuals. Methods and Results-The study included 6 age groups ranging from 20 to 80 years old. Thickness of the valves was compared between age groups. Valve closure time was assessed as an indicator for valve function. In 69 of 77 participants, valve parameters could be measured. We found an increasing thickness of the valves with age, with a mean thickness of 0. Key Words: aging Ⅲ ultrasonic diagnosis Ⅲ venous thrombosis Ⅲ venous valves D eep-vein thrombosis (DVT) of the lower extremities is a disease with an annual incidence of 1 to 2 per 1000. 1,2 Risk factors for venous thrombosis can be divided into acquired and genetic risk factors. Among genetic risk factors are deficiencies of antithrombin, protein C, and protein S, which give a high risk for DVT but have low societal impact because of their low prevalence (Ͻ0.1%). 3 Risk factors such as the prothrombin 20210A and Factor V Leiden mutations are highly prevalent (3% to 8%) and are of intermediate strength. 4,5 Surgery and use of oral contraceptives are examples of highly prevalent acquired risk factors.Aging is the strongest risk factor for venous thrombosis. In people under the age of 40, the incidence is less than 1 per 10 000. 6 However, the incidence of venous thrombosis increases to 1 per 100 per year in elderly over the age of 75. 7 It is not clear why age is such an important risk factor. Explanations such as a decrease in mobility, an increase in prevalence of diseases with a high thrombotic risk (ie, malignancies, hip fractures), reduced venous compliance in the calf, and damaged venous valves have been suggested. 8 -10 Venous valves function to ensure that a proper inflow of blood reaches the heart during various cardiovascular adjustments. They can be regarded as flow modifiers that act and react constantly. 11,12 Venous valves are bicuspid and are positioned in a valve sinus, which is a local widening of the venous wall. The area between a valve leaflet and the vessel wall is called the valve pocket and is regarded as the place where thrombi originate. Low shear stress areas and stasis of blood flow predispose to thrombus formation. In the deepest part of the valve pockets, fluid circulates with very low velocities, creating a low shear field, thus allowing red cells to aggregate. Stagnation of blood leads to hypoxia, which subsequently causes endothelial damage. In case of nonpulsatile flow, a canine study showed that a thrombus was formed on a valve cusp after only 2 hours. 13 Age-related changes of the venous wall and valves have been described in renal veins. 14 Muscle fibers in the vessel wall atrophy with increasing age, whereas elastic fiber bundles hypertrophy. With respect to the valves, a gradual thickening with age was seen as a result of an increased number of ...

Summary Background Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. Methods One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. Findings After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). Interpretation Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. Funding ESSR Young Researchers Grant.