Fatigue after Liver Transplantation

Liver transplantation (LTx) has developed from an experimental procedure in the 1960’s to the preferred treatment for end-stage liver disease nowadays. The first human LTx was performed by Starlz and his team in 1963 in Colorado.[1] Unfortunately, this patient died within a few days. The first successful LTx was performed in 1967 by the same team; this patient survived one year. The most prevalent indications for LTx in Europe are virus-related cirrhosis (22%), alcoholic cirrhosis (19%), cancer (18%), cholestatic liver diseases (11%), acute hepatic failure (9%) and metabolic disease (6%).[2] The main complications in the immediate postoperative period are dysfunction and rejection of the graft, infections, bile duct complications and pulmonary or neurological problems. Long-term complications after LTx are typically a consequence of the prolonged immunosuppressive therapy, and include diabetes mellitus, infections, renal dysfunction, hypertension, osteoporosis, and de novo neoplasia.[3] Currently, three University Medical Centers are performing LTx’s in the Netherlands: Groningen, Leiden and Rotterdam

A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History

__Purpose:__ To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA. __Design:__ Prospective, multicenter, natural history study with up to 15 years of follow-up. __Participants:__ Four hundred nine CFIs of 238 eyes with GA from the Rotterdam Study (RS) and Blue Mountain Eye Study (BMES) for model development, and 3589 CFIs of 376 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate. __Methods:__ A deep learning model based on an ensemble of encoder–decoder architectures was implemented and optimized for the segmentation of GA in CFIs. Four experienced graders delineated, in consensus, GA in CFIs from the RS and BMES. These manual delineations were used to evaluate the segmentation model using 5-fold cross-validation. The model was applied further to CFIs from the AREDS to study the growth rate of GA. Linear regression analysis was used to study associations between structural biomarkers at baseline and the GA growth rate. A general estimate of the progression of GA area over time was made by combining growth rates of all eyes with GA from the AREDS set. __Main Outcome Measures:__ Automatically segmented GA and GA growth rate. __Results:__ The model obtained an average Dice coefficient of 0.72±0.26 on the BMES and RS set while comparing the automatically segmented GA area with the graders’ manual delineations. An intraclass correlation coefficient of 0.83 was reached between the automatically estimated GA area and the graders’ consensus measures. Nine automatically calculated structural biomarkers (area, filled area, convex area, convex solidity, eccentricity, roundness, foveal involvement, perimeter, and circularity) were significantly associated with growth rate. Combining all growth rates indicated that GA area grows quadratically up to an area of approximately 12 mm2, after which growth rate stabilizes or decreases. __Conclusions:__ The deep learning model allowed for fully automatic and robust segmentation of GA on CFIs. These segmentations can be used to extract structural characteristics of GA that predict its growth rate

Bone suppression increases the visibility of invasive pulmonary aspergillosis in chest radiographs

Objective: Chest radiographs (CXR) are an important diagnostic tool for the detection of invasive pulmonary aspergillosis (IPA) in critically ill patients, but their diagnostic value is limited by a poor sensitivity. By using advanced image processing, the aim of this study was to increase the value of chest radiographs in the diagnostic work up of neutropenic patients who are suspected of IPA. Methods: The frontal CXRs of 105 suspected cases of IPA were collected from four institutions. Radiographs could contain single or multiple sites of infection. CT was used as reference standard. Five radiologists and two residents participated in an observer study for the detection of IPA on CXRs with and without bone suppressed images (ClearRead BSI 3.2; Riverain Technologies). The evaluation was performed separately for the right and left lung, resulting in 78 diseased cases (or lungs) and 132 normal cases (or lungs). For each image, observers scored the likelihood of focal infectious lesions being present on a continuous scale (0-100). The area under the receiver operating characteristics curve (AUC) served as the performance measure. Sensitivity and specificity were calculated by considering only the lungs with a suspiciousness score of greater than 50 to be positive. Results: The average AUC for only CXRs was 0.815. Performance significantly increased, to 0.853, when evaluation was aided with BSI (p = 0.01). Sensitivity increased from 49% to 66% with BSI, while specificity decreased from 95% to 90%. Conclusion: The detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images. BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

Coronary artery calcium can predict all-cause mortality and cardiovascular events on low-dose ct screening for lung cancer

OBJECTIVE. Performing coronary artery calcium (CAC) screening as part of low-dose CT lung cancer screening has been proposed as an efficient strategy to detect people with high cardiovascular risk and improve outcomes of primary prevention. This study aims to investigate whether CAC measured on low-dose CT in a population of former and current heavy smokers is an independent predictor of all-cause mortality and cardiac events. SUBJECTS AND METHODS. We used a case-cohort study and included 958 subjects 50 years old or older within the screen group of a randomized controlled lung cancer screening trial. We used Cox proportional-hazard models to compute hazard ratios (HRs) adjusted for traditional cardiovascular risk factors to predict all-cause mortality and cardiovascular events. RESULTS. During a median follow-up of 21.5 months, 56 deaths and 127 cardiovascular events occurred. Compared with a CAC score of 0, multivariate-adjusted HRs for all-cause mortality for CAC scores of 1-100, 101-1000, and more than 1000 were 3.00 (95% CI, 0.61-14.93), 6.13 (95% CI, 1.35-27.77), and 10.93 (95% CI, 2.36-50.60), respectively. Multivariate- adjusted HRs for coronary events were 1.38 (95% CI, 0.39-4.90), 3.04 (95% CI, 0.95-9.73), and 7.77 (95% CI, 2.44-24.75), respectively. CONCLUSION. This study shows that CAC scoring as part of low-dose CT lung cancer screening can be used as an independent predictor of all-cause mortality and cardiovascular events

Improved arterial visualization in cerebral CT perfusion-derived arteriograms compared with standard CT angiography: A visual assessment study

BACKGROUND AND PURPOSE: Invasive cerebral DSA has largely been replaced by CTA, which is noninvasive but has a compromised arterial view due to superimposed bone and veins. The purpose of this study was to evaluate whether arterial visualization in CTPa is superior to standard CTA, which would eliminate the need for an additional CTA scan to assess arterial diseases and therefore reduce radiation dose. MATERIALS AND METHODS: In this study, we included 24 patients with subarachnoid hemorrhage for whom CTA and CTP were available. Arterial quality and presence of superimposed veins and bone in CTPa were compared with CTA and scored by 2 radiologists by using a VAS (0%-100%). Average VAS scores were determined and VAS scores per patient were converted to a 10-point NRS. Arterial visualization was considered to be improved when the highest rate (NRS 10, VAS > 90%) was scored for arterial quality, and the lowest rate (NRS 1, VAS < 10%), for the presence of superimposed veins and bone. A sign test with continuity correction was used to test whether the number of cases with these rates was significant. RESULTS: Average VAS scores in the proximal area were 94% (arterial quality), 4% (presence of bone), and 7% (presence of veins). In this area, the sign test showed that a significant number of cases scored NRS 10 for arterial quality (P < .02) and NRS 1 for the presence of superimposed veins and bone (P < .01). CONCLUSIONS: Cerebral CTPa shows improved arterial visualization in the proximal area compared with CTA, with similar arterial quality but no superimposed bone and veins