An Effective Health System Approach to End TB: Implementing the Double X Strategy in Vietnam.

Countries that are high burden for TB must reverse the COVID-19 pandemic's devastating effects to accelerate progress toward ending TB. Vietnam's Double X (2X) strategy uses chest radiography (CXR) and GeneXpert (Xpert) rapid diagnostic testing to improve early detection of TB disease. Household contacts and vulnerable populations (e.g., individuals aged 60 years and older, smokers, diabetics, those with alcohol use disorders, and those previously treated for TB) with and without TB symptoms were screened in community campaigns using CXRs, followed by Xpert for those with a positive screen. In public non-TB district facilities, diabetics, respiratory outpatients, inpatients with lung disease, and other vulnerable populations underwent 2X evaluation. During COVID-19 restrictions in Vietnam, the 2X strategy improved access to TB services by decentralization to commune health stations, the lowest level of the health system, and enabling self-screening using a quick response mobile application. The number needed to screen (NNS) with CXRs to diagnose 1 person with TB disease was calculated for all 2X models and showed the highest yield among self-screeners (11 NNS with CXR), high yield for vulnerable populations in communities (60 NNS) and facilities (19 NNS), and moderately high yield for household contacts in community campaigns (154 NNS). Computer-aided diagnosis for CXRs was incorporated into community and facility implementation and improved physicians' CXR interpretations and Xpert referral decisions. Integration of TB infection and TB disease evaluation increased eligibility for TB preventive treatment among household contacts, a major challenge during implementation. The 2X strategy increased the rational use of Xpert, employing a health system-wide approach that reached vulnerable populations with and without TB symptoms in communities and facilities for early detection of TB disease. This strategy was effectively adapted to different levels of the health system during COVID-19 restrictions and contributed to post-pandemic TB recovery in Vietnam.

Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam's District Health Facilities: An Implementation Study.

In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing.

Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning.

SUMMARY: Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was utilized for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the sub-group of normal wrist radiographs, and 91.3% among the sub-group of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, specificity of 93.3%, and accuracy of 93.4%. The AUC was 0.986. We have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.

Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were > 0.99, with mean error of < 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of < 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.

Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

The Level of Expression of Anxiety and Depression in Clinical Health Care Workers during the COVID-19 Outbreak in 2 Hospitals in Hanoi, Vietnam.

The disease caused by the SARS-Cov 2 virus has spread to most areas of the world with high rates of infection and deaths. Facing the complicated developments of the epidemic, clinical medical staff (CMS) are at risk of suffering psychological pressure. This study aimed to investigate the situation of anxiety, depression, and related factors affecting CMS during the COVID-19 pandemic at Dong Da General Hospital and Dong Anh General Hospital in Hanoi. A cross-sectional study was conducted from April to July 2020 using self-administered questionnaires amongst 341 CMS. The participants' anxiety levels were assessed using the standardized General Anxiety Disorder-7 (GAD-7) toolkit and levels of depression expression were assessed based on the standardized Patient Health Questionnaire-9 (PHQ-9) toolkit. Of the CMS who completed the questionnaire, 33.1% had an anxiety disorder and 23.2% exhibited mild to very severe depression. The factors associated with anxiety and depression were department of work, shortage of human resources, and discrimination from the community that directly affects the family of the CMS. The study results highlight the need for a training session to equip CMS with the skills required to cope with psychological stress in all circumstances in general and during the pandemic in particular. This training is especially important for those working in at-risk departments which are susceptible to infection.

Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing.

The ideal radiology report reduces diagnostic uncertainty, while avoiding ambiguity whenever possible. The purpose of this study was to characterize the use of uncertainty terms in radiology reports at a single institution and compare the use of these terms across imaging modalities, anatomic sections, patient characteristics, and radiologist characteristics. We hypothesized that there would be variability among radiologists and between subspecialities within radiology regarding the use of uncertainty terms and that the length of the impression of a report would be a predictor of use of uncertainty terms. Finally, we hypothesized that use of uncertainty terms would often be interpreted by human readers as "hedging." To test these hypotheses, we applied a natural language processing (NLP) algorithm to assess and count the number of uncertainty terms within radiology reports. An algorithm was created to detect usage of a published set of uncertainty terms. All 642,569 radiology report impressions from 171 reporting radiologists were collected from 2011 through 2015. For validation, two radiologists without knowledge of the software algorithm reviewed report impressions and were asked to determine whether the report was "uncertain" or "hedging." The relationship between the presence of 1 or more uncertainty terms and the human readers' assessment was compared. There were significant differences in the proportion of reports containing uncertainty terms across patient admission status and across anatomic imaging subsections. Reports with uncertainty were significantly longer than those without, although report length was not significantly different between subspecialities or modalities. There were no significant differences in rates of uncertainty when comparing the experience of the attending radiologist. When compared with reader 1 as a gold standard, accuracy was 0.91, sensitivity was 0.92, specificity was 0.9, and precision was 0.88, with an F1-score of 0.9. When compared with reader 2, accuracy was 0.84, sensitivity was 0.88, specificity was 0.82, and precision was 0.68, with an F1-score of 0.77. Substantial variability exists among radiologists and subspecialities regarding the use of uncertainty terms, and this variability cannot be explained by years of radiologist experience or differences in proportions of specific modalities. Furthermore, detection of uncertainty terms demonstrates good test characteristics for predicting human readers' assessment of uncertainty.

Effects of treatment temperatures on redox potential and sensory evaluation of different spices and herbs applied to cooked pork sausages.

BACKGROUND: This study aimed to investigate the effects of treatment temperatures (22, 78, 100 °C) on the antioxidant activity of 13 types of dried ground spices and herbs (black mustard, black pepper, blackberries, onion, cumin, galangal, lemon balm, lovage, marjoram, oregano, parsley, rosemary and watercress) through measurements of redox potential. Four different combinations of spices and herbs were created and applied to cooked pork sausages, then sensory evaluation was carried out. RESULTS: The redox potential was temperature dependent. A temperature of 78 °C was chosen to produce the cooked pork sausages with the addition of the spice and herb combinations. The combinations were black mustard, onion, and cumin (at a 1:1:1 ratio); onion, marjoram, and parsley (at a 1:1:1 ratio); black pepper, lemon balm, and parsley (at a 1:2.35:1.65 ratio) and black pepper, cumin, and lovage (at a 1:2:2 ratio). In pork sausages cooked at 78 °C, the variants at 12 g kg-1 had a more intense aroma and taste than those at 6 g kg-1 spice and herb combinations, and received a superior sensory evaluation in total. CONCLUSIONS: The most desirable treatment temperature possibly applied in food products was 78 °C as it gave the highest number of negative results in redox potential of water extracts. The addition of the tested spice and herb combinations contributed to the increase of antioxidant possibility of 78 °C-cooked pork sausages. Further investigation of the redox potential in other meat products (raw meat products at 22 °C, sausages from cooked meat at 100 °C) with the addition of the current spice and herb combinations will be undertaken in subsequent research. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Deep Learning for Automated Classification of Inferior Vena Cava Filter Types on Radiographs.

PURPOSE: To demonstrate the feasibility and evaluate the performance of a deep-learning convolutional neural network (CNN) classification model for automated identification of different types of inferior vena cava (IVC) filters on radiographs. MATERIALS AND METHODS: In total, 1,375 cropped radiographic images of 14 types of IVC filters were collected from patients enrolled in a single-center IVC filter registry, with 139 images withheld as a test set and the remainder used to train and validate the classification model. Image brightness, contrast, intensity, and rotation were varied to augment the training set. A 50-layer ResNet architecture with fixed pre-trained weights was trained using a soft margin loss over 50 epochs. The final model was evaluated on the test set. RESULTS: The CNN classification model achieved a F1 score of 0.97 (0.92-0.99) for the test set overall and of 1.00 for 10 of 14 individual filter types. Of the 139 test set images, 4 (2.9%) were misidentified, all mistaken for other filter types that appear highly similar. Heat maps elucidated salient features for each filter type that the model used for class prediction. CONCLUSIONS: A CNN classification model was successfully developed to identify 14 types of IVC filters on radiographs and demonstrated high performance. Further refinement and testing of the model is necessary before potential real-world application.

MRI UTE-T2* shows high incidence of cartilage subsurface matrix changes 2 years after ACL reconstruction.

Alteration of deep cartilage matrix has been observed following anterior cruciate ligament (ACL) injury, evidenced by elevated MRI UTE-T2* values measured in small, 2-D cartilage regions of interest. This Level I diagnostic study seeks to more thoroughly evaluate deep cartilage matrix changes to medial tibiofemoral UTE-T2* maps 2 years after ACL reconstruction and examine the relative utilities of 3-D compared to 2-D assessments of cartilage UTE-T2* maps. Thirty-eight ACL-reconstructed and 20 uninjured subjects underwent MRI UTE-T2* mapping. "Small" single mid-sagittal 2-D and larger 3-D "tread mark" regions of interest were manually segmented and found to be correlated in medial cartilage (r > 0.58, p < 0.005). 3-D analyses of UTE-T2* maps showed differences to medial tibial cartilage between ACL-reconstructed and uninjured subjects (p = 0.007) that were not detected by smaller 2-D regions (p > 0.46). Quantitative comparisons show 14/38 (37%) ACL-reconstructed subjects have values >2 standard deviations higher than uninjured controls. Among a subset of ACL-reconstructed subjects with no morphologic MRI evidence of medial tibiofemoral cartilage or meniscal pathology (n = 12), elevated UTE-T2* values in "small" 2-D femoral (p = 0.011), but not larger 3-D tread mark regions of interest (p > 0.13), were observed. These data show the utility of 2-D UTE-T2* assessments of mid-sagittal weight-bearing regions of medial femoral cartilage for identifying subclinical deep cartilage matrix changes 2 years after ACLR. Clinical Significance: Mid-sagittal single slice 2-D UTE-T2* mapping may be an efficient means to assess medial femoral cartilage for subsurface matrix changes early after ACL reconstruction while 3-D assessments provide additional sensitivity to changes in the medial tibial plateau. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:370-377, 2019.

Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs.

BACKGROUND: The majority of current medical CBIR systems perform retrieval based only on "imaging signatures" generated by extracting pixel-level quantitative features, and only rarely has a feedback mechanism been incorporated to improve retrieval performance. In addition, current medical CBIR approaches do not routinely incorporate semantic terms that model the user's high-level expectations, and this can limit CBIR performance. METHOD: We propose a retrieval framework that exploits a hybrid feature space (HFS) that is built by integrating low-level image features and high-level semantic terms, through rounds of relevance feedback (RF) and performs similarity-based retrieval to support semi-automatic image interpretation. The novelty of the proposed system is that it can impute the semantic features of the query image by reformulating the query vector representation in the HFS via user feedback. We implemented our framework as a prototype that performs the retrieval over a database of 811 radiographic images that contains 69 unique types of bone tumors. RESULTS: We evaluated the system performance by conducting independent reading sessions with two subspecialist musculoskeletal radiologists. For the test set, the proposed retrieval system at fourth RF iteration of the sessions conducted with both the radiologists achieved mean average precision (MAP) value ∼0.90 where the initial MAP with baseline CBIR was 0.20. In addition, we also achieved high prediction accuracy (>0.8) for the majority of the semantic features automatically predicted by the system. CONCLUSION: Our proposed framework addresses some limitations of existing CBIR systems by incorporating user feedback and simultaneously predicting the semantic features of the query image. This obviates the need for the user to provide those terms and makes CBIR search more efficient for inexperience users/trainees. Encouraging results achieved in the current study highlight possible new directions in radiological image interpretation employing semantic CBIR combined with relevance feedback of visual similarity.

Mechanically stimulated biomarkers signal cartilage changes over 5 years consistent with disease progression in medial knee osteoarthritis patients.

Using serum biomarkers to assess osteoarthritis (OA) disease state and risks of progression remain challenging. This study tested the hypothesis that changes to serum biomarkers in response to a mechanical stimulus in patients with medial knee OA signal cartilage thickness changes 5 years later. Specifically, serum concentrations of a collagen degradation marker (C1,2C) and a chondroitin sulfate synthesis marker (CS846) were measured 0.5 and 5.5 hours after a 30-min walk in 16 patients. Regional cartilage thickness changes measured from magnetic resonance images obtained at study entry and at 5-year follow-up were tested for correlations with baseline biomarker changes after mechanical stimulus, and for differences between groups stratified based on whether biomarker levels increased or decreased. Results showed that an increase in the degradation biomarker C1,2C correlated with cartilage thinning of the lateral tibia (R = -0.63, p = 0.009), whereas an increase in the synthesis marker CS846 correlated with cartilage thickening of the lateral femur (R = 0.76, p = 0.001). Changes in C1,2C and CS846 were correlated (R2 = 0.28, p = 0.037). Subjects with increased C1,2C had greater (p = 0.05) medial tibial cartilage thinning than those with decreased C1,2C. In conclusion, the mechanical stimulus appeared to metabolically link the biomarker responses where biomarker increases signaled more active OA disease states. The findings of medial cartilage thinning for patients with increases in the degradation marker and correlation of cartilage thickening in the less involved lateral femur with increases in the synthetic marker were consistent with progression of medial compartment OA. Thus, the mechanical stimulus facilitated assessing OA disease states using serum biomarkers. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:891-897, 2018.

Detection of nociceptive-related metabolic activity in the spinal cord of low back pain patients using 18F-FDG PET/CT.

BACKGROUND: Over the past couple of decades, a number of centers in the brain have been identified as important sites of nociceptive processing and are collectively known as the 'pain matrix.' Imaging tools such as functional magnetic resonance imaging (MRI) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) have played roles in defining these pain-relevant, physiologically active brain regions. Similarly, certain segments of the spinal cord are likely more metabolically active in the setting of pain conditions, the location of which is dependent upon location of symptoms. However, little is known about the physiologic changes in the spinal cord in the context of pain. This study aimed to determine whether uptake of 18F-FDG in the spinal cord on positron emission tomography/computed tomography (PET/CT) of patients with low back pain (LBP) differs from that of patients without LBP. METHODS: We conducted a retrospective review of 18F-FDG PET/CT scans of 26 patients with non-central nervous system cancers, 13 of whom had reported LBP and 13 of whom were free of LBP (controls). No patients had spinal stenosis or significant 18F-FDG contribution of degenerative changes of the spine into the spinal canal. Circular regions of interests were drawn within the spinal canal on transaxial images, excluding bony or discal elements of the spine, and the maximum standardized uptake value (SUVmax) of every slice from spinal nerves C1 to S1 was obtained. SUVmax were normalized by subtracting the SUVmax of spinal nerve L5, as minimal neural tissue is present at this level. Normalized SUVmax of LBP patients were compared to those of LBP-free patients at each vertebral level. RESULTS: We found the normalized SUVmax of patients with LBP to be significantly greater than those of control patients when jointly tested at spinal nerves of T7, T8, T9 and T10 (p<0.001). No significant difference was found between the two groups at other levels of the spinal cord. Within the two groups, normalized SUVmax generally decreased cephalocaudally. CONCLUSIONS: Patients with LBP show increased uptake of 18F-FDG in the caudal aspect of the thoracic spinal cord, compared to patients without LBP. IMPLICATIONS: This paper demonstrates the potential of 18F-FDG PET/CT as a biomarker of increased metabolic activity in the spinal cord related to LBP. As such, it could potentially aid in the treatment of LBP by localizing physiologically active spinal cord regions and guiding minimally invasive delivery of analgesics or stimulators to relevant levels of the spinal cord.

Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features.

Because many bone tumors have a variety of appearances and are uncommon, few radiologists develop sufficient expertise to guide optimal management. Bayesian inference can guide decision-making by computing probabilities of multiple diagnoses to generate a differential. We built and validated a naïve Bayes machine (NBM) that processes 18 demographic and radiographic features. We reviewed over 1664 analog radiographic cases of bone tumors and selected 811 cases (66 diagnoses) for annotation using a quantitative imaging platform. Leave-one-out cross validation was performed. Primary accuracy was defined as the correct pathological diagnosis as the top machine prediction. Differential accuracy was defined as whether the correct pathological diagnosis was within the top three predictions. For the 29 most common diagnoses (710 cases), primary accuracy was 44%, and differential accuracy was 60%. For the top 10 most common diagnoses (478 cases), primary accuracy was 62%, and differential accuracy was 80%. The machine returned relevant diagnoses for the majority of unknown test cases and may be a feasible alternative to machine learning approaches such as deep neural networks or support vector machines that typically require larger training data (our model required a minimum of five samples per diagnosis) and are "black boxes" (our model can provide details of probability calculations to identify features that most significantly contribute to truth diagnoses). Finally, our Bayes model was designed to scale and "learn" from external data, enabling incorporation of outside knowledge such as Dahlin's Bone Tumors, a reference of anatomic and demographic statistics of more than 10,000 tumors.

Apical Hypertrophic Cardiomyopathy: Case Report and Literature Review.

BACKGROUND Apical hypertrophic cardiomyopathy (ApHCM) is a relatively rare form of hypertrophic cardiomyopathy that predominantly affects the apex of the left ventricle and typically has a nonobstructive physiology. Its variable presentation and clinical course render ApHCM a commonly delayed or missed diagnosis. CASE REPORT A 53-year-old Caucasian woman presented with chronic progressive chest pain. She was initially started on treatment for acute coronary syndrome. Diagnosis of ApHCM was initially missed on echocardiography, but made on subsequent cardiac catheterization and cardiac MRI. She improved clinically with metoprolol, had a work-up for implantable cardioverter-defibrillator placement, and was referred for genetic testing. CONCLUSIONS Despite earlier studies suggesting a more benign clinical course of ApHCM, recent studies report increased morbidity and mortality, which is comparable to the prognosis of other variants of hypertrophic cardiomyopathy such as hypertrophic obstructive cardiomyopathy. Thus, when formulating a differential diagnosis for chest pain, it is important to include structural heart disease including apical and other variants of hypertrophic cardiomyopathy as part of that differential, as appropriate management can prevent these devastating sequelae. Furthermore, when screening tests such as echocardiography cannot adequately establish the diagnosis of ApHCM, then cardiac MRI or invasive hemodynamic testing is necessary to establish or refute the diagnosis.

Ascending aortic dissection in a young patient using a synephrine-containing workout supplement.

Aortic dissection in young patients presents a clinical and diagnostic challenge. Atypical symptoms of ascending aortic dissection can delay presentation and diagnosis. Here, we describe a patient with delayed diagnosis of an atypical presentation of ascending aortic dissection after using a synephrine-containing pre-workout supplement. The diagnosis was initially missed on computed tomography, but subsequently made on echocardiography. This is the first reported case of ascending aortic dissection in the setting of synephrine supplementation. This case illustrates a potential cardiovascular adverse effect of synephrine and highlights the need for clinical trials without conflicts of interest assessing its safety. .

Lund-Mackay and modified Lund-Mackay score for sinus surgery in children with cystic fibrosis.

OBJECTIVE: Patients with cystic fibrosis (CF) frequently present with severe sinonasal disease often requiring radiologic imaging and surgical intervention. Few studies have focused on the relationship between radiologic scoring systems and the need for sinus surgery in this population. The objective of this study is to evaluate the Lund-Mackay (LM) and modified Lund-Mackay (m-LM) scoring systems in predicting the need for sinus surgery or revision surgery in patients with CF. METHODS: We performed a retrospective chart review of CF patients undergoing computed tomography (CT) sinus imaging at a tertiary care pediatric hospital from 1995 to 2008. Patient scans were scored using both the LM and m-LM systems and compared to the rate of sinus surgery or revision surgery. Receiver-operator characteristics curves (ROC) were used to analyze the radiological scoring systems. RESULTS: A total of 41 children with CF were included in the study. The mean LM score for patients undergoing surgery was 17.3 (±3.1) compared to 11.5 (±6.2) for those treated medically (p<0.01). For the m-LM, the mean score of patients undergoing surgery was 20.3 (±3.5) and 13.5 (±7.3) for those medically treated (p<0.01). Using a ROC curve with a threshold score of 13 for the LM, the sensitivity was 89.3% (95% CI of 72-98) and specificity of 69.2% (95% CI of 39-91). At an optimal score of 19, the m-LM system produced a sensitivity of 67.7% (95% CI of 48-84) and specificity of 84.6% (95% CI of 55-98). CONCLUSION: The modified Lund-Mackay score provides a high specificity while the Lund-Mackay score a high sensitivity for CF patients who required sinus surgery. The combination of both radiologic scoring systems can potentially predict the need for surgery in this population.

Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS.

Information search has changed the way we manage knowledge and the ubiquity of information access has made search a frequent activity, whether via Internet search engines or increasingly via mobile devices. Medical information search is in this respect no different and much research has been devoted to analyzing the way in which physicians aim to access information. Medical image search is a much smaller domain but has gained much attention as it has different characteristics than search for text documents. While web search log files have been analysed many times to better understand user behaviour, the log files of hospital internal systems for search in a PACS/RIS (Picture Archival and Communication System, Radiology Information System) have rarely been analysed. Such a comparison between a hospital PACS/RIS search and a web system for searching images of the biomedical literature is the goal of this paper. Objectives are to identify similarities and differences in search behaviour of the two systems, which could then be used to optimize existing systems and build new search engines. Log files of the ARRS GoldMiner medical image search engine (freely accessible on the Internet) containing 222,005 queries, and log files of Stanford's internal PACS/RIS search called radTF containing 18,068 queries were analysed. Each query was preprocessed and all query terms were mapped to the RadLex (Radiology Lexicon) terminology, a comprehensive lexicon of radiology terms created and maintained by the Radiological Society of North America, so the semantic content in the queries and the links between terms could be analysed, and synonyms for the same concept could be detected. RadLex was mainly created for the use in radiology reports, to aid structured reporting and the preparation of educational material (Lanlotz, 2006) [1]. In standard medical vocabularies such as MeSH (Medical Subject Headings) and UMLS (Unified Medical Language System) specific terms of radiology are often underrepresented, therefore RadLex was considered to be the best option for this task. The results show a surprising similarity between the usage behaviour in the two systems, but several subtle differences can also be noted. The average number of terms per query is 2.21 for GoldMiner and 2.07 for radTF, the used axes of RadLex (anatomy, pathology, findings, …) have almost the same distribution with clinical findings being the most frequent and the anatomical entity the second; also, combinations of RadLex axes are extremely similar between the two systems. Differences include a longer length of the sessions in radTF than in GoldMiner (3.4 and 1.9 queries per session on average). Several frequent search terms overlap but some strong differences exist in the details. In radTF the term "normal" is frequent, whereas in GoldMiner it is not. This makes intuitive sense, as in the literature normal cases are rarely described whereas in clinical work the comparison with normal cases is often a first step. The general similarity in many points is likely due to the fact that users of the two systems are influenced by their daily behaviour in using standard web search engines and follow this behaviour in their professional search. This means that many results and insights gained from standard web search can likely be transferred to more specialized search systems. Still, specialized log files can be used to find out more on reformulations and detailed strategies of users to find the right content.

Optimal imaging surveillance schedules after liver-directed therapy for hepatocellular carcinoma.

PURPOSE: To optimize surveillance schedules for the detection of recurrent hepatocellular carcinoma (HCC) after liver-directed therapy. MATERIALS AND METHODS: New methods have emerged that allow quantitative analysis and optimization of surveillance schedules for diseases with substantial rates of recurrence such as HCC. These methods were applied to 1,766 consecutive chemoembolization, radioembolization, and radiofrequency ablation procedures performed on 910 patients between 2006 and 2011. Computed tomography or magnetic resonance imaging performed just before repeat therapy was set as the time of "recurrence," which included residual and locally recurrent tumor as well as new liver tumors. Time-to-recurrence distribution was estimated by Kaplan-Meier method. Average diagnostic delay (time between recurrence and detection) was calculated for each proposed surveillance schedule using the time-to-recurrence distribution. An optimized surveillance schedule could then be derived to minimize the average diagnostic delay. RESULTS: Recurrence is 6.5 times more likely in the first year after treatment than in the second. Therefore, screening should be much more frequent in the first year. For eight time points in the first 2 years of follow-up, the optimal schedule is 2, 4, 6, 8, 11, 14, 18, and 24 months. This schedule reduces diagnostic delay compared with published schedules and is cost-effective. CONCLUSIONS: The calculated optimal surveillance schedules include shorter-interval follow-up when there is a higher probability of recurrence and longer-interval follow-up when there is a lower probability. Cost can be optimized for a specified acceptable diagnostic delay or diagnostic delay can be optimized within a specified acceptable cost.

MR imaging near metallic implants using MAVRIC SL: initial clinical experience at 3T.

RATIONALE AND OBJECTIVES: To compare the effectiveness of multiacquisition with variable resonance image combination selective (MAVRIC SL) with conventional two-dimensional fast spin-echo (2D-FSE) magnetic resonance (MR) techniques at 3T in imaging patients with a variety of metallic implants. MATERIALS AND METHODS: Twenty-one 3T MR studies were obtained in 19 patients with different types of metal implants. Paired MAVRIC SL and 2D-FSE sequences were reviewed by two radiologists and compared for in-plane and through-plane metal artifact, visualization of the bone implant interface and surrounding soft tissues, blurring, and overall image quality using a two-tailed Wilcoxon signed rank test. The area of artifact on paired images was measured and compared using a paired Wilcoxon signed rank test. Changes in patient management resulting from MAVRIC SL imaging were documented. RESULTS: Significantly less in-plane and through-plane artifact was seen with MAVRIC SL, with improved visualization of the bone-implant interface and surrounding soft tissues, and superior overall image quality (P = .0001). Increased blurring was seen with MAVRIC SL (P = .0016). MAVRIC SL significantly decreased the image artifact compared to 2D-FSE (P = .0001). Inclusion of MAVRIC SL to the imaging protocol determined the need for surgery or type of surgery in five patients and ruled out the need for surgery in 13 patients. In three patients, the area of interest was well seen on both MAVRIC SL and 2D-FSE images, so the addition of MAVRIC had no effect on patient management. CONCLUSIONS: Imaging around metal implants with MAVRIC SL at 3T significantly improved image quality and decreased image artifact compared to conventional 2D-FSE imaging techniques and directly impacted patient management.