Search

Abstract Chyluria is an uncommon medical condition resulting from an abnormal communication between the abdominal lymphatic system and the urinary tract, which results in the presence of chyle in the urine, making it appear milky white. Proper diagnosis is demonstrated by the concentration of urinary lipids. Worldwide, chyluria is most commonly associated with the parasite Wuchereria bancrofti. However, in Europe and North America, where the condition is rare, non-parasitic etiologies predominate. Identifying the cause and location of the uro-lymphatic communication is essential in guiding therapeutic management, but imaging the lymphatic channels remains a challenge. Magnetic resonance (MR) lymphography, a non-invasive free-breathing 3D high-resolution fast-recovery fast spin-echo sequence similar to that used for 3D MR cholangiopancreatography, may demonstrate the cause and location of an abnormal communication between the lymphatic system and urinary tract. In parasitic causes of chyluria, dilated lymphatics vessels communicating with the lymphatic system are demonstrated. In non-parasitic causes of chyluria channel type lymphatic malformations are the most common. Markedly dilated and dysplastic lymphatic vessels communicating with the urinary tract are demonstrated. In addition, other cystic or channel type lymphatic malformations such as thoracic, soft tissue or bone abnormalities may be observed. This review describes the abdominal lymphatic diseases leading to chyluria and presents the technique and images obtained with non-enhanced MR lymphography to enable radiologists in identifying and classifying uro-lymphatic fistulae. Critical relevance statement: Non-enhanced MR lymphography enables the identification and categorization of uro-lymphatic fistulae. Graphical abstract

Abstract Background To train a machine-learning model to locate the transition zone (TZ) of adhesion-related small bowel obstruction (SBO) on CT scans. Materials and methods We used 562 CTs performed in 2005–2018 in 404 patients with adhesion-related SBO. Annotation of the TZs was performed by experienced radiologists and trained residents using bounding boxes. Preprocessing involved using a pretrained model to extract the abdominopelvic region. We modeled TZ localization as a binary classification problem by splitting the abdominopelvic region into 125 patches. We then trained a neural network model to classify each patch as containing or not containing a TZ. We coupled this with a trained probabilistic estimation of presence of a TZ in each patch. The models were first evaluated by computing the area under the receiver operating characteristics curve (AUROC). Then, to assess the clinical benefit, we measured the proportion of total abdominopelvic volume classified as containing a TZ for several different false-negative rates. Results The probability of containing a TZ was highest for the hypogastric region (56.9%). The coupled classification network and probability mapping produced an AUROC of 0.93. For a 15% proportion of volume classified as containing TZs, the probability of highlighted patches containing a TZ was 92%. Conclusion Modeling TZ localization by coupling convolutional neural network classification and probabilistic localization estimation shows the way to a possible automatic TZ detection, a complex radiological task with a major clinical impact.

Background & Aims: People with primary sclerosing cholangitis (PSC) have a variable and often progressive disease course that is associated with biliary and parenchymal changes. These changes are typically assessed by magnetic resonance imaging (MRI), including qualitative assessment of magnetic resonance cholangiopancreatography (MRCP). Our aim was to study the association of novel objective quantitative MRCP metrics with prognostic scores and patient outcomes. Methods: We performed a retrospective study including 77 individuals with large-duct PSC with baseline MRCP images, which were postprocessed to obtain quantitative measures of bile ducts using MRCP+™. The participants’ ANALI scores, liver stiffness by vibration-controlled transient elastography, and biochemical indices were collected at baseline. Adverse outcome-free survival was measured as the absence of decompensated cirrhosis, liver transplantation (LT), or liver-related death over a 12-year period. The prognostic value of MRCP+-derived metrics was assessed by Cox regression modelling. Results: During a total of 386 patients-years, 16 cases of decompensation, 2 LTs, and 5 liver-related deaths were recorded. At baseline, around 50% of the patients were classified as being at risk of developing disease complications. MRCP+ metrics, particularly those describing the severity of bile duct dilatations, were correlated with all prognostic factors. Univariate analysis showed that MRCP+ metrics representing duct diameter, dilatations, and the percentage of ducts with strictures and/or dilatations were associated with survival. In a multivariable-adjusted analysis, the median duct diameter was significantly associated with survival (hazard ratio 10.9, 95% CI 1.3–90.3). Conclusions: MRCP+ metrics in people with PSC correlate with biochemical, elastographic, and radiological prognostic scores and are predictive of adverse outcome-free survival. Lay summary: In this study, we assessed in people with primary sclerosing cholangitis (PSC) the association of novel objective quantitative MRCP metrics automatically provided by a software tool (MRCP+) with prognostic scores and patient outcomes. We observed that MRCP+ metrics in people with PSC correlate with biochemical, elastographic, and radiological prognostic scores and are predictive of adverse outcome-free survival.

Background & Aims: The low-phospholipid-associated cholelithiasis (LPAC) syndrome is a recently described peculiar form of cholelithiasis associated with the ATP-binding-cassette subfamily B, member 4 (ABCB4) gene deficiency. The purpose of our study was to analyse the relationship between magnetic resonance (MR) features and the genetic status of ABCB4 in people with LPAC syndrome. Methods: A total of 233 individuals with proven LPAC syndrome were enrolled between January 2003 and June 2018 in a retrospective single-centre study. Inclusion criteria included availability of clinical files, MR images, and genetic data. MR images were analysed by consensus among 3 senior radiologists blinded to the status of ABCB4 gene mutation. Results: A total of 125 individuals (mean age at first MR imaging 40.8 years; 66% females; 48% ABCB4 variant) were included. MR abnormalities were found in 61 (49%) of the 125 individuals. Forty (67%) of the 60 individuals with an ABCB4 gene variant had MR abnormalities as compared with 21 (33%) of the 65 individuals without an ABCB4 gene variant (odds ratio [OR] 4.1, 95% CI 1.9–9.5, p = 0.0001). Compared to individuals with no variant, individuals with an ABCB4 variant were more likely to show intrahepatic macrolithiasis (56 vs. 17%; OR 6.3, 95% CI 2.6–16.2, p

Background: Manual segmentation is currently the gold standard to assess white matter hyperintensities (WMH), but it is time consuming and subject to intra and inter-operator variability. Purpose: To compare automatic methods to segment white matter hyperintensities (WMH) in the elderly in order to assist radiologist and researchers in selecting the most relevant method for application on clinical or research data. Material and Methods: We studied a research dataset composed of 147 patients, including 97 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 database and 50 patients from ADNI 3 and a clinical routine dataset comprising 60 patients referred for cognitive impairment at the Pitié-Salpêtrière hospital (imaged using four different MRI machines). We used manual segmentation as the gold standard reference. Both manual and automatic segmentations were performed using FLAIR MRI. We compared seven freely available methods that produce segmentation mask and are usable by a radiologist without a strong knowledge of computer programming: LGA (Schmidt et al., 2012), LPA (Schmidt, 2017), BIANCA (Griffanti et al., 2016), UBO detector (Jiang et al., 2018), W2MHS (Ithapu et al., 2014), nicMSlesion (with and without retraining) (Valverde et al., 2019, 2017). The primary outcome for assessing segmentation accuracy was the Dice similarity coefficient (DSC) between the manual and the automatic segmentation software. Secondary outcomes included five other metrics. Results: A deep learning approach, NicMSlesion, retrained on data from the research dataset ADNI, performed best on this research dataset (DSC: 0.595) and its DSC was significantly higher than that of all others. However, it ranked fifth on the clinical routine dataset and its performance severely dropped on data with artifacts. On the clinical routine dataset, the three top-ranked methods were LPA, SLS and BIANCA. Their performance did not differ significantly but was significantly higher than that of other methods. Conclusion: This work provides an objective comparison of methods for WMH segmentation. Results can be used by radiologists to select a tool.