A comparative study of federated learning methods for COVID-19 detection.

Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.

Validation of an AI-based algorithm for measurement of the thoracic aortic diameter in low-dose chest CT.

OBJECTIVES: To evaluate the performance of artificial intelligence (AI) software for automatic thoracic aortic diameter assessment in a heterogeneous cohort with low-dose, non-contrast chest computed tomography (CT). MATERIALS AND METHODS: Participants of the Imaging in Lifelines (ImaLife) study who underwent low-dose, non-contrast chest CT (August 2017-May 2022) were included using random samples of 80 participants <50y, ≥80y, and with thoracic aortic diameter ≥40 mm. AI-based aortic diameters at eight guideline compliant positions were compared with manual measurements. In 90 examinations (30 per group) diameters were reassessed for intra- and inter-reader variability, which was compared to discrepancy of the AI system using Bland-Altman analysis, paired samples t-testing and linear mixed models. RESULTS: We analyzed 240 participants (63 ± 16 years; 50 % men). AI evaluation failed in 11 cases due to incorrect segmentation (4.6 %), leaving 229 cases for analysis. No difference was found in aortic diameter between manual and automatic measurements (32.7 ± 6.4 mm vs 32.7 ± 6.0 mm, p = 0.70). Bland-Altman analysis yielded no systematic bias and a repeatability coefficient of 4.0 mm for AI. Mean discrepancy of AI (1.3 ± 1.6 mm) was comparable to inter-reader variability (1.4 ± 1.4 mm); only at the proximal aortic arch showed AI higher discrepancy (2.0 ± 1.8 mm vs 0.9 ± 0.9 mm, p < 0.001). No difference between AI discrepancy and inter-reader variability was found for any subgroup (all: p > 0.05). CONCLUSION: The AI software can accurately measure thoracic aortic diameters, with discrepancy to a human reader similar to inter-reader variability in a range from normal to dilated aortas.

Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging because of the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them.

Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems.

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging.

Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models.

Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on deep learning algorithms for small datasets. Fifty CT scans were retrospectively selected and the whole heart, ventricles and atria were contoured. A two stage deep learning pipeline was trained on 41 non contrast planning CTs, tuned with 3 CT scans and validated on 6 CT scans. In the first stage, An InceptionResNetV2 network was used to identify the slices that contained cardiac structures. The second stage consisted of three deep learning models trained on the images containing cardiac structures to segment the structures. The three deep learning models predicted the segmentations/contours on axial, coronal and sagittal images and are combined to create the final prediction. The final accuracy of the pipeline was quantified on 6 volumes by calculating the Dice similarity coefficient (DC), 95% Hausdorff distance (95% HD) and volume ratios between predicted and ground truth volumes. Median DC and 95% HD of 0.96, 0.88, 0.92, 0.80 and 0.82, and 1.86, 2.98, 2.02, 6.16 and 6.46 were achieved for the whole heart, right and left ventricle, and right and left atria respectively. The median differences in volume were -4, -1, + 5, -16 and -20% for the whole heart, right and left ventricle, and right and left atria respectively. The automatic contouring pipeline achieves good results for whole heart and ventricles. Robust automatic contouring with deep learning methods seems viable for local centers with small datasets.

Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation: a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms.

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance.

Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance.

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downstream tasks such as quality improvement, epidemiological research, and monitoring guideline adherence. Class imbalance, variation in dataset size, variation in report complexity, and algorithm type all influence NLP performance but have not yet been systematically and interrelatedly evaluated. In this study, we investigate these factors on the performance of four types [a fully connected neural network (Dense), a long short-term memory recurrent neural network (LSTM), a convolutional neural network (CNN), and a Bidirectional Encoder Representations from Transformers (BERT)] of deep learning-based NLP. Two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (CT) studies (n = 2255) were split into training sets (80%) and testing sets (20%). The training data was used as a source to train all four model types in 84 experiments (Fracture-data) and 45 experiments (Chest-data) with variation in size and prevalence. The performance was evaluated on sensitivity, specificity, positive predictive value, negative predictive value, area under the curve, and F score. After the NLP of radiology reports, all four model-architectures demonstrated high performance with metrics up to > 0.90. CNN, LSTM, and Dense were outperformed by the BERT algorithm because of its stable results despite variation in training size and prevalence. Awareness of variation in prevalence is warranted because it impacts sensitivity and specificity in opposite directions.

Machine learning based natural language processing of radiology reports in orthopaedic trauma.

OBJECTIVES: To compare different Machine Learning (ML) Natural Language Processing (NLP) methods to classify radiology reports in orthopaedic trauma for the presence of injuries. Assessing NLP performance is a prerequisite for downstream tasks and therefore of importance from a clinical perspective (avoiding missed injuries, quality check, insight in diagnostic yield) as well as from a research perspective (identification of patient cohorts, annotation of radiographs). METHODS: Datasets of Dutch radiology reports of injured extremities (n = 2469, 33% fractures) and chest radiographs (n = 799, 20% pneumothorax) were collected in two different hospitals and labeled by radiologists and trauma surgeons for the presence or absence of injuries. NLP classification was applied and optimized by testing different preprocessing steps and different classifiers (Rule-based, ML, and Bidirectional Encoder Representations from Transformers (BERT)). Performance was assessed by F1-score, AUC, sensitivity, specificity and accuracy. RESULTS: The deep learning based BERT model outperforms all other classification methods which were assessed. The model achieved an F1-score of (95 ± 2)% and accuracy of (96 ± 1)% on a dataset of simple reports (n= 2469), and an F1 of (83 ± 7)% with accuracy (93 ± 2)% on a dataset of complex reports (n= 799). CONCLUSION: BERT NLP outperforms traditional ML and rule-base classifiers when applied to Dutch radiology reports in orthopaedic trauma.

Augmented Reality Visualization for Image-Guided Surgery: A Validation Study Using a Three-Dimensional Printed Phantom.

BACKGROUND: Oral and maxillofacial surgery currently relies on virtual surgery planning based on image data (CT, MRI). Three-dimensional (3D) visualizations are typically used to plan and predict the outcome of complex surgical procedures. To translate the virtual surgical plan to the operating room, it is either converted into physical 3D-printed guides or directly translated using real-time navigation systems. PURPOSE: This study aims to improve the translation of the virtual surgery plan to a surgical procedure, such as oncologic or trauma surgery, in terms of accuracy and speed. Here we report an augmented reality visualization technique for image-guided surgery. It describes how surgeons can visualize and interact with the virtual surgery plan and navigation data while in the operating room. The user friendliness and usability is objectified by a formal user study that compared our augmented reality assisted technique to the gold standard setup of a perioperative navigation system (Brainlab). Moreover, accuracy of typical navigation tasks as reaching landmarks and following trajectories is compared. RESULTS: Overall completion time of navigation tasks was 1.71 times faster using augmented reality (P = .034). Accuracy improved significantly using augmented reality (P < .001), for reaching physical landmarks a less strong correlation was found (P = .087). Although the participants were relatively unfamiliar with VR/AR (rated 2.25/5) and gesture-based interaction (rated 2/5), they reported that navigation tasks become easier to perform using augmented reality (difficulty Brainlab rated 3.25/5, HoloLens 2.4/5). CONCLUSION: The proposed workflow can be used in a wide range of image-guided surgery procedures as an addition to existing verified image guidance systems. Results of this user study imply that our technique enables typical navigation tasks to be performed faster and more accurately compared to the current gold standard. In addition, qualitative feedback on our augmented reality assisted technique was more positive compared to the standard setup.?>.

Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT.

PURPOSE: Phantom studies in CT emphysema quantification show that iterative reconstruction and deep learning-based noise reduction (DLNR) allow lower radiation dose. We compared emphysema quantification on ultra-low-dose CT (ULDCT) with and without noise reduction, to standard-dose CT (SDCT) in chronic obstructive pulmonary disease (COPD). METHOD: Forty-nine COPD patients underwent ULDCT (third generation dual-source CT; 70ref-mAs, Sn-filter 100kVp; median CTDIvol 0.38 mGy) and SDCT (64-multidetector CT; 40mAs, 120kVp; CTDIvol 3.04 mGy). Scans were reconstructed with filtered backprojection (FBP) and soft kernel. For ULDCT, we also applied advanced modelled iterative reconstruction (ADMIRE), levels 1/3/5, and DLNR, levels 1/3/5/9. Emphysema was quantified as Low Attenuation Value percentage (LAV%, ≤-950HU). ULDCT measures were compared to SDCT as reference standard. RESULTS: For ULDCT, the median radiation dose was 84 % lower than for SDCT. Median extent of emphysema was 18.6 % for ULD-FBP and 15.4 % for SDCT (inter-quartile range: 11.8-28.4 % and 9.2 %-28.7 %, p = 0.002). Compared to SDCT, the range in limits of agreement of emphysema quantification as measure of variability was 14.4 for ULD-FBP, 11.0-13.1 for ULD-ADMIRE levels and 10.1-13.9 for ULD-DLNR levels. Optimal settings were ADMIRE 3 and DLNR 3, reducing variability of emphysema quantification by 24 % and 27 %, at slight underestimation of emphysema extent (-1.5 % and -2.9 %, respectively). CONCLUSIONS: Ultra-low-dose CT in COPD patients allows dose reduction by 84 %. State-of-the-art noise reduction methods in ULDCT resulted in slight underestimation of emphysema compared to SDCT. Noise reduction methods (especially ADMIRE 3 and DLNR 3) reduced variability of emphysema quantification in ULDCT by up to 27 % compared to FBP.

Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-based Comparative Study.

RATIONALE AND OBJECTIVES: To describe the rational and design of a population-based comparative study. The objective of the study is to assess the screening performance of volume-based management of CT-detected lung nodule in comparison to diameter-based management, and to improve the effectiveness of CT screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), in addition to lung cancer, based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting. MATERIALS AND METHODS: A population-based comparative study is being performed, including 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population. Participants in the intervention group undergo a low-dose chest and cardiac CT scan at baseline and 1 year later, and are managed according to NELCIN-B3 protocol. Participants in the control group undergo a low-dose chest CT scan according to the routine CT protocol and are managed according to the clinical practice. Epidemiological data are collected through questionnaires. In the fourth year from baseline, the diagnosis of the three diseases will be collected. RESULTS: The unnecessary referral rate will be compared between NELCIN-B3 and standard protocol for managing early-detected lung nodules. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated. CONCLUSION: We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules, and will improve the early detection of COPD and CVD in a Chinese urban population. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03988322. Registered on 14 June 2019.

Application of artificial intelligence in cardiac CT: From basics to clinical practice.

Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the clinical workflow. This review is a comprehensive overview on the types of tasks and applications in which AI can aid the clinician in cardiac CT, and can be used as a primer for medical researchers starting in the field of AI. The applications of AI algorithms are explained and recent examples in cardiac CT of these algorithms are further elaborated on. The critical factors for implementation in the future are discussed.

Respiratory level tracking with visual biofeedback for consistent breath-hold level with potential application in image-guided interventions.

BACKGROUND: To present and evaluate a new respiratory level biofeedback system that aids the patient to return to a consistent breath-hold level with potential application in image-guided interventions. METHODS: The study was approved by the local ethics committee and written informed consent was waived. Respiratory motion was recorded in eight healthy volunteers in the supine and prone positions, using a depth camera that measures the mean distance to thorax, abdomen and back. Volunteers were provided with real-time visual biofeedback on a screen, as a ball moving up and down with respiratory motion. For validation purposes, a conversion factor from mean distance (in mm) to relative lung volume (in mL) was determined using spirometry. Subsequently, without spirometry, volunteers were given breathing instructions and were asked to return to their initial breath-hold level at expiration ten times, in both positions, with and without visual biofeedback. For both positions, the median and interquartile range (IQR) of the absolute error in lung volume from initial breath-hold were determined with and without biofeedback and compared using Wilcoxon signed rank tests. RESULTS: Without visual biofeedback, the median difference from initial breath-hold was 124.6 mL (IQR 55.7-259.7 mL) for the supine position and 156.3 mL (IQR 90.9-334.7 mL) for the prone position. With the biofeedback, the difference was significantly decreased to 32.7 mL (IQR 12.8-59.6 mL) (p < 0.001) and 22.3 mL (IQR 7.7-47.0 mL) (p < 0.001), respectively. CONCLUSIONS: The use of a depth camera to provide visual biofeedback increased the reproducibility of breath-hold expiration level in healthy volunteers, with a potential to eliminate targeting errors caused by respiratory movement during lung image-guided procedures.

Improvement of radiology reporting in a clinical cancer network: impact of an optimised multidisciplinary workflow.

PURPOSE: To assess the effectiveness of implementing a quality improvement project in a clinical cancer network directed at the response assessment of oncology patients according to RECIST-criteria. METHODS: Requests and reports of computed tomography (CT) studies from before (n = 103) and after (n = 112) implementation of interventions were compared. The interventions consisted of: a multidisciplinary working agreement with a clearly described workflow; subspecialisation of radiologists; adaptation of the Picture Archiving and Communication System (PACS); structured reporting. RESULTS: The essential information included in the requests and the reports improved significantly after implementation of the interventions. In the requests, mentioning start date increased from 2% to 49%; date of baseline CT from 7% to 64%; nadir date from 1% to 41%. In the reports, structured layout increased from 14% to 86%; mentioning target lesions from 18% to 80% and non-target lesions from 11% to 80%; measurements stored in PACS increased from 76% to 97%; labelled key images from 38% to 95%; all p values < 0.001. CONCLUSION: The combination of implementation of an optimised workflow, subspecialisation and structured reporting led to significantly better quality radiology reporting for oncology patients receiving chemotherapy. The applied multifactorial approach can be used within other radiology subspeciality areas as well. KEY POINTS: • Undeveloped subspecialisation makes adherence to RECIST guidelines difficult in general hospitals. • A clinical cancer network provides opportunities to improve healthcare. • Optimised workflow, subspecialisation and structured reporting substantially improve request and report quality. • Good interdisciplinary communication between oncologists, radiologists and others contributes to quality improvement.

Perception of radiology reporting efficacy by neurologists in general and university hospitals.

AIM: To investigate how neurologists perceive the value of the radiology report and to analyse the relation with the neurologists own expertise in radiology and the level of subspecialisation of radiologists. MATERIALS AND METHODS: A web-based survey was distributed to neurologists. The level of subspecialisation was assessed by the percentage of fellowship-trained radiologists and the percentage of radiologists that were members of the Dutch Society of Neuroradiology. RESULTS: Most neurologists interpret all computed tomography (CT) and magnetic resonance imaging (MRI) studies themselves, and their self-confidence in making correct interpretations is high. Residents gave higher scores than neurologists for "Radiologist report answers the question" (p=0.039) and for "Radiologist reports give helpful advice" (p=0.001). Neurologists from university hospitals stated more frequently that the report answered their questions than neurologists from general hospitals (p=0.008). The general appreciation for radiology reports was higher for neurologists from university hospitals than from general hospitals (8.2 versus 7.2; p=0.003). Radiologists at university hospitals have a higher level of subspecialisation than those at general hospitals. CONCLUSION: Subspecialisation of radiologists leads to higher quality of radiology reporting as perceived by neurologists. Because of their expertise in radiology, neurologists are valuable sources of feedback for radiologists. Paying attention to the clinical questions and giving advice tailored to the needs of the referring physicians are opportunities to improve radiology reporting.

Non-invasive assessment of coronary artery geometry using coronary CTA.

AIM: To assess the association of coronary artery geometry with the severity of coronary artery disease (CAD). METHODS: 73 asymptomatic individuals at increased risk of CAD due to peripheral vascular disease (18 women, mean age 63.5 ±â€¯8.2 years) underwent coronary computed tomography angiography (coronary CTA) using first generation dual-source CT. Curvature and tortuosity of the coronary arteries were quantified using semi-automatically generated centerlines. Measurements were performed for individual segments and for the entire artery. Coronary segments were labeled according to the presence of significant stenosis, defined as >70% luminal narrowing, and the presence of plaque. Comparisons were made by segment and by artery, using linear mixed models. RESULTS: Overall, median curvature and tortuosity were, respectively, 0.094 [0.071; 0.120] and 1.080 [1.040; 1.120] on a per-segment level, and 0.096 [0.078; 0.118] and 1.175 [1.090; 1.420] on a per-artery level. Curvature was associated with significant stenosis at a per-segment (p < 0.001) and per-artery level (p = 0.002). Curvature was 16.7% higher for segments with stenosis, and 13.8% higher for arteries with stenosis. Tortuosity was associated with significant stenosis only at the per-segment level (p = 0.002). Curvature was related to the presence of plaque at the per-segment (p < 0.001) and per-artery level (p < 0.001), tortuosity was only related to plaque at the per-segment level (p < 0.001). CONCLUSION: Coronary artery geometry as derived from coronary CTA is related to the presence of plaque and significant stenosis.

Implementation and Validation of PACS Integrated Peer Review for Discrepancy Recording of Radiology Reporting.

The purpose of this work is to demonstrate the possibility of implementation of a PACS-integrated peer review system based on RADPEER™ classification providing a step-wise implementation plan utilizing features already present in the standard PACS implementation and without the requirement of additional software development. Furthermore, we show the usage and effects of the system during the first 30 months of usage. To allow fast and easy implementation into the daily workflow the key-word feature of the PACS was used. This feature allows to add a key-word to an imaging examination for easy searching in the PACS database (e.g. by entering keywords for different kinds of pathology). For peer review we implemented a keyword structure including a code for each of the existing RADPEER™ scoring language terms and a keyword with the phrase "second reading" followed by the name of the individual radiologist. The use of the short-keys to enter the codes in relation to the peer review was a simple to use solution. During the study 599 reports were peer reviewed. The active participation in this study of the radiologists varies and ranges from 3 to 327 reviews per radiologist. The number of peer review is highest in CT and CR. There are no significant technical obstacles to implement a PACS-integrated RADPEER™ -system based on key-words allowing easy integration of peer review into the daily routine without the requirement of additional software. Peer review implemented in a non-random setting based on relevant priors could already help in increasing the quality of radiological reporting and serve as continuing education among peers. Decisiveness, tact and trust are needed to promote use of the system and collaborative discussion of the results by radiologist.

Quantitative Myocardial Perfusion with Dynamic Contrast-Enhanced Imaging in MRI and CT: Theoretical Models and Current Implementation.

Technological advances in magnetic resonance imaging (MRI) and computed tomography (CT), including higher spatial and temporal resolution, have made the prospect of performing absolute myocardial perfusion quantification possible, previously only achievable with positron emission tomography (PET). This could facilitate integration of myocardial perfusion biomarkers into the current workup for coronary artery disease (CAD), as MRI and CT systems are more widely available than PET scanners. Cardiac PET scanning remains expensive and is restricted by the requirement of a nearby cyclotron. Clinical evidence is needed to demonstrate that MRI and CT have similar accuracy for myocardial perfusion quantification as PET. However, lack of standardization of acquisition protocols and tracer kinetic model selection complicates comparison between different studies and modalities. The aim of this overview is to provide insight into the different tracer kinetic models for quantitative myocardial perfusion analysis and to address typical implementation issues in MRI and CT. We compare different models based on their theoretical derivations and present the respective consequences for MRI and CT acquisition parameters, highlighting the interplay between tracer kinetic modeling and acquisition settings.

Image De-Identification Methods for Clinical Research in the XDS Environment.

To investigate possible de-identification methodologies within the Cross-Enterprise Document Sharing for imaging (XDS-I) environment in order to provide strengthened support for image data exchange as part of clinical research projects. De-identification, using anonymization or pseudonymization, is the most common method to perform information removal within DICOM data. However, it is not a standard part of the XDS-I profiles. Different methodologies were observed to define how and where de-identification should take place within an XDS environment used for scientific research. De-identification service can be placed in three locations within the XDS-I framework: 1) within the Document Source, 2) between the Document Source and Document Consumer, and 3) within the Document Consumer. First method has a potential advantage with respect to the exposure of the images to outside systems but has drawbacks with respect to additional hardware and configuration requirements. Second and third method have big concern in exposing original documents with all identifiable data being intact after leaving the Document Source. De-identification within the Document Source has more advantages compared to the other methods. On the contrary, it is less recommended to perform de-identification within the Document Consumer since it has the highest risk of the exposure of patients identity due to the fact that images are exposed without de-identification during the transfers.

Is aortoiliac calcification linked to colorectal anastomotic leakage? A case-control study.

BACKGROUND: Anastomotic leakage in bowel surgery remains a devastating complication. Various risk factors have been uncovered, however, high anastomotic leakage rates are still being reported. This study describes the use of calcification markers of the central abdominal arteries as a prognostic factor for colorectal anastomotic leakage. METHODS: This case-control study includes clinical data from three different hospitals. Calcium volume and calcium score of the aortoiliac tract were determined by CT-scan analysis. Cases were all patients with anastomotic leakage after a left-sided anastomosis (n = 30). Three controls were randomly matched for each case. Only patients with a contrast-enhanced pre-operative CT-scan were included. RESULTS: The measurements of the calcium score and calcium volume of the different trajectories showed that there was one significant difference with regard to the right external iliac artery. Multiple regression analysis showed a significant different negative odds ratio of the presence of calcium in the right external iliac artery. CONCLUSION: This study demonstrates that calcium volume and calcium score of the aortoiliac trajectory does not correlate with the risk of colorectal anastomotic leakage after a left-sided anastomosis.