Perinatal Factors Associated with Successful Pharmacologic Closure of the Patent Ductus Arteriosus in Premature Infants.

The patent ductus arteriosus (PDA) is associated with significant morbidity and mortality in preterm infants. While pharmacologic closure of the PDA is common and effective, it can be difficult to identify which patients will respond. As such, the objective of this study was to identify factors associated with successful pharmacologic closure of the PDA. We hypothesized that clinical factors such as gestational age, birth weight, and hypertensive disorders of pregnancy would be associated with successful closure. We performed a retrospective cohort study of preterm infants who received pharmacologic treatment for a PDA at two large neonatal intensive care units in Boston, MA between January 2016 and December 2021. Infants were excluded if they received prophylactic indomethacin, had early termination of therapy, did not have an echocardiogram prior to therapy, or had congenital heart disease. The primary outcome was closure after initial course. Relevant perinatal data were collected on enrolled infants. Of the 215 enrolled infants, 131 (61%) had successful closure. Older gestational age (OR, 1.23; 95% CI,1.03-1.47), male sex (OR, 2.17; 95% CI,1.18-3.99), and maternal preeclampsia (OR, 2.75; 95% CI,1.07-7.02) were associated with successful closure. Infants who received postnatal steroids (OR, 0.49; 95% CI,0.25-0.96) were less likely to have had successful closure. In this study, we identified previously established associations of gestational age and male sex with successful pharmacologic closure. However, the associations with maternal preeclampsia and postnatal steroids are novel. While further investigation is warranted, these associations can help inform decision-making around management of the PDA.

De Novo Exposomic Geospatial Assembly of Chronic Disease Regions with Machine Learning & Network Analysis.

BACKGROUND: Determining spatial relationships between disease and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States. METHODS: Using aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation of these clusters with counties with high rates of disease was calculated. Pollution correlation matrices were used together with network analysis to identify the strongest disease-pollution relationships. Results were compared to LISA, Moran's I, univariate, elastic net, and random forest regression. FINDINGS: aPEER produced 68,820 maps with human interpretable, distinct pollution-derived regions. Diseases with the strongest pollution associations were hypertension (J=0.5316, p=3.89x10-208), COPD (J=0.4545, p=8.27x10-131), stroke (J=0.4517, p=1.15x10-127), stroke mortality (J=0.4445, p=4.28x10-125), and diabetes mellitus (J=0.4425, p=2.34x10-127). Methanol, acetaldehyde, and formaldehyde were identified as strongly associated with stroke, COPD, stroke mortality, hypertension, and diabetes mellitus in the southeast United States (which correlated with both the Stroke and Diabetes Belt). Pollutants were strongly predictive of chronic disease geography and outperformed conventional prediction models based on preventive services and social determinants of health (using elastic net and random forest regression). INTERPRETATION: aPEER used machine learning to identify disease and air pollutant relationships with similar or superior AUCs compared to social determinants of health (SDOH) and healthcare preventive service models. These findings highlight the utility of aPEER in epidemiological and geospatial analysis as well as the emerging role of exposomics in understanding chronic disease pathology. FUNDING: Boston Public Health Commission, NHLBI (R03 HL157890) and the CDC.

Artificial intelligence-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance.

AIMS: This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value to predict death above traditional prognosticators. METHODS AND RESULTS: Between 2016 and 2018, we conducted a longitudinal study that included all consecutive patients referred for vasodilator stress CMR. LVEFAI was assessed using AI algorithm combines multiple deep learning networks for LV segmentation. The primary outcome was all-cause death assessed using the French National Registry of Death. Cox regression was used to evaluate the association of stress LVEFAI with death after adjustment for traditional risk factors and CMR findings. In 9712 patients (66 ± 15 years, 67% men), there was an excellent correlation between stress LVEFAI and LVEF measured by expert (LVEFexpert) (r = 0.94, P < 0.001). Stress LVEFAI was associated with death [median (interquartile range) follow-up 4.5 (3.7-5.2) years] before and after adjustment for risk factors [adjusted hazard ratio, 0.84 (95% confidence interval, 0.82-0.87) per 5% increment, P < 0.001]. Stress LVEFAI had similar significant association with death occurrence compared with LVEFexpert. After adjustment, stress LVEFAI value showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.11; net reclassification improvement = 0.250; integrative discrimination index = 0.049, all P < 0.001; likelihood-ratio test P < 0.001), with an incremental prognostic value over LVEFAI determined at rest. CONCLUSION: AI-based fully automated LVEF measured at stress is independently associated with the occurrence of death in patients undergoing stress CMR, with an additional prognostic value above traditional risk factors, inducible ischaemia and late gadolinium enhancement.

Overview of Spanlastics: A Groundbreaking Elastic Medication Delivery Device with Versatile Prospects for Administration via Various Routes.

When compared to the challenges associated with traditional dosage forms, medication delivery systems based on nanotechnology have been a huge boon. One such candidate for medication delivery is spanlastics, an elastic nanovesicle that can transport a diverse array of medicinal compounds. The use of spanlastics has been associated with an increase in interest in alternative administration methods. The non-ionic surfactant or surfactant blend is the main component of spanlastics. The purpose of this review was primarily to examine the potential of spanlastics as a delivery system for a variety of medication classes administered via diverse routes. Science Direct, Google Scholar, and Pubmed were utilized to search the academic literature for this review. Several studies have demonstrated that spanlastics greatly improve therapeutic effectiveness, increase medication absorption, and decrease drug toxicity. This paper provides a summary of the composition and structure of spanlastics along with their utility in the delivery of various therapeutic agents by adopting different routes. Additionally, it provides an overview of the numerous disorders that may be treated using drugs that are contained in spanlastic vesicles.

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

PURPOSE: We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. MATERIALS AND METHODS: In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). RESULTS: Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). CONCLUSIONS: The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

Association of enlarged extra-axial spaces and subdural hemorrhage in preterm infants at term-equivalent age.

To determine the incidence of enlarged extra-axial space (EES) and its association with subdural hemorrhage (SDH) in a regional cohort of preterm infants. As part of a prospective cohort study of 395 preterm infants, brain magnetic resonance imaging (MRI) was collected on each infant at term-equivalent age. Six preterm infants showed evidence of SDH. We reviewed the MRIs to identify the incidence of EES in these 6 infants and the cohort broadly. We then completed a retrospective chart review of the 6 infants to identify any concerns for non-accidental trauma (NAT) since the MRI was obtained. The incidence of SDH in the cohort was 1.6%. The incidence of EES was 48.1% including all 6 infants with SDH. The incidence of SDH in infants with EES was 3.2%. The retrospective chart review of the 6 infants did not yield any evidence of NAT. The incidence of EES and SDH in our cohort was significantly higher than similar cohorts of term infants, demonstrating an increased risk in preterm infants. The incidence of SDH in infants with EES was greater than in the total cohort, suggesting that it is a risk factor for asymptomatic SDH in preterm infants.

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Who Still Gets Ligated? Reasons for Persistence of Surgical Ligation of the Patent Ductus Arteriosus Following Availability of Transcatheter Device Occlusion for Premature Neonates.

(1) Background: To identify reasons for the persistence of surgical ligation of the patent ductus arteriosus (PDA) in premature infants after the 2019 Food and Drug Administration (FDA) approval of transcatheter device closure; (2) Methods: We performed a 10-year (2014-2023) single-institution retrospective study of premature infants (<37 weeks) and compared clinical characteristics and neonatal morbidities between neonates that underwent surgical ligation before (epoch 1) and after (epoch 2) FDA approval of transcatheter closure; (3) Results: We identified 120 premature infants that underwent surgical ligation (n = 94 before, n = 26 after FDA approval). Unfavorable PDA morphology, active infection, and recent abdominal pathology were the most common reasons for surgical ligation over device occlusion in epoch 2. There were no differences in demographics, age at closure, or outcomes between infants who received surgical ligation in the two epochs; (4) Conclusions: Despite increasing trends for transcatheter PDA closure in premature infants, surgical ligation persists due to unfavorable ductal morphology, active infection, or abdominal pathology.

Self-supervised learning for interventional image analytics: toward robust device trackers.

Purpose: The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion. Approach: To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model. Results: Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a 3× faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance. Conclusions: The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.

A highly conserved tRNA modification contributes to C. albicans filamentation and virulence.

tRNA modifications play important roles in maintaining translation accuracy in all domains of life. Disruptions in the tRNA modification machinery, especially of the anticodon stem loop, can be lethal for many bacteria and lead to a broad range of phenotypes in baker's yeast. Very little is known about the function of tRNA modifications in host-pathogen interactions, where rapidly changing environments and stresses require fast adaptations. We found that two closely related fungal pathogens of humans, the highly pathogenic Candida albicans and its much less pathogenic sister species, Candida dubliniensis, differ in the function of a tRNA-modifying enzyme. This enzyme, Hma1, exhibits species-specific effects on the ability of the two fungi to grow in the hypha morphology, which is central to their virulence potential. We show that Hma1 has tRNA-threonylcarbamoyladenosine dehydratase activity, and its deletion alters ribosome occupancy, especially at 37°C-the body temperature of the human host. A C. albicans HMA1 deletion mutant also shows defects in adhesion to and invasion into human epithelial cells and shows reduced virulence in a fungal infection model. This links tRNA modifications to host-induced filamentation and virulence of one of the most important fungal pathogens of humans.IMPORTANCEFungal infections are on the rise worldwide, and their global burden on human life and health is frequently underestimated. Among them, the human commensal and opportunistic pathogen, Candida albicans, is one of the major causative agents of severe infections. Its virulence is closely linked to its ability to change morphologies from yeasts to hyphae. Here, this ability is linked-to our knowledge for the first time-to modifications of tRNA and translational efficiency. One tRNA-modifying enzyme, Hma1, plays a specific role in C. albicans and its ability to invade the host. This adds a so-far unknown layer of regulation to the fungal virulence program and offers new potential therapeutic targets to fight fungal infections.

Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms.

Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Despite the recently demonstrated power of deep learning to bring a significant boost in performance in a wide range of medical imaging tasks, there are still very few published researches on automatic pulmonary embolism detection. Herein we introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection in CTPA. Our method brings novel contributions along three orthogonal axes: (1) automatic detection of anatomical structures; (2) anatomical aware pretraining, and (3) a dual-hop deep neural net for PE detection. We obtain state-of-the-art results on the publicly available multicenter large-scale RSNA dataset.

A model of human neural networks reveals NPTX2 pathology in ALS and FTLD.

Human cellular models of neurodegeneration require reproducibility and longevity, which is necessary for simulating age-dependent diseases. Such systems are particularly needed for TDP-43 proteinopathies1, which involve human-specific mechanisms2-5 that cannot be directly studied in animal models. Here, to explore the emergence and consequences of TDP-43 pathologies, we generated induced pluripotent stem cell-derived, colony morphology neural stem cells (iCoMoNSCs) via manual selection of neural precursors6. Single-cell transcriptomics and comparison to independent neural stem cells7 showed that iCoMoNSCs are uniquely homogenous and self-renewing. Differentiated iCoMoNSCs formed a self-organized multicellular system consisting of synaptically connected and electrophysiologically active neurons, which matured into long-lived functional networks (which we designate iNets). Neuronal and glial maturation in iNets was similar to that of cortical organoids8. Overexpression of wild-type TDP-43 in a minority of neurons within iNets led to progressive fragmentation and aggregation of the protein, resulting in a partial loss of function and neurotoxicity. Single-cell transcriptomics revealed a novel set of misregulated RNA targets in TDP-43-overexpressing neurons and in patients with TDP-43 proteinopathies exhibiting a loss of nuclear TDP-43. The strongest misregulated target encoded the synaptic protein NPTX2, the levels of which are controlled by TDP-43 binding on its 3' untranslated region. When NPTX2 was overexpressed in iNets, it exhibited neurotoxicity, whereas correcting NPTX2 misregulation partially rescued neurons from TDP-43-induced neurodegeneration. Notably, NPTX2 was consistently misaccumulated in neurons from patients with amyotrophic lateral sclerosis and frontotemporal lobar degeneration with TDP-43 pathology. Our work directly links TDP-43 misregulation and NPTX2 accumulation, thereby revealing a TDP-43-dependent pathway of neurotoxicity.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines.

PURPOSE: To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA). PATIENTS AND METHODS: A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers. RESULTS: A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%. CONCLUSIONS: The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Artificial intelligence in the neonatal intensive care unit: the time is now.

Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.

World Health Organization MPOWER for tobacco control in India: a 6-year retrospective analysis.

Background: Prevalence and practices of tobacco usage in India are diverse and incongruent and Government of India has enacted various laws to overcome this burden. To make tobacco control measures effective and powerful, WHO introduced MPOWER in 2004 and India was one of the first countries that implemented the MPOWER. Objective: This study is aimed to quantify the implementation of MPOWER tobacco control policies in India. Material and Methods: In this retrospective analysis, data was gathered from the WHO MPOWER of India from 2015 to 2021. This analysis was based on the checklist which was designed previously by Iranian and international tobacco control specialists in their study on tobacco control. Results: In the present comparative analysis, India was categorized by scores and these were acquired from each indicator for each activity and 2021 year got the highest scores as compared to the previous year scores i.e. 27 in 2015. In context to individual indicators, noticeable increase in scores has been seen in both health warning on cigarette packages and adult daily smoking prevalence, whereas no progress was observed in smoking related policies. Conclusion: Although MPOWER programmes are widely accepted by the Indian government, but still substantial improvement in fewer sections is required.

Performing Quality Control on Magnetic Resonance Imaging Liver Fat/Iron Quantification Studies: A Critical Requirement.

OBJECTIVE: Nonalcoholic fatty liver and iron overload can lead to cirrhosis requiring early detection. Magnetic resonance (MR) imaging utilizing chemical shift-encoded sequences and multi-Time of Echo single-voxel spectroscopy (SVS) are frequently used for assessment. The purpose of this study was to assess various quality factors of technical acceptability and any deficiencies in technologist performance in these fat/iron MR quantification studies. METHODS: Institutional review board waived retrospective quality improvement review of 87 fat/iron MR studies performed over a 6-month period was evaluated. Technical acceptability/unacceptability for chemical shift-encoded sequences (q-Dixon and IDEAL-IQ) included data handling errors (missing maps), liver field coverage, fat/water swap, motion, or other artifacts. Similarly, data handling (missing table/spectroscopy), curve-fit, fat- and water-peak separation, and water-peak sharpness were evaluated for SVS technical acceptability. RESULTS: Data handling errors were found in 11% (10/87) of studies with missing maps or entire sequence (SVS or q-Dixon). Twenty-seven percent (23/86) of the q-Dixon/IDEAL-IQ were technically unacceptable (incomplete liver-field [39%], other artifacts [35%], significant/severe motion [18%], global fat/water swap [4%], and multiple reasons [4%]). Twenty-eight percent (21/75) of SVS sequences were unacceptable (water-peak broadness [67%], poor curve-fit [19%] overlapping fat and water peaks [5%], and multiple reasons [9%]). CONCLUSIONS: A high rate of preventable errors in fat/iron MR quantification studies indicates the need for routine quality control and evaluation of technologist performance and technical deficiencies that may exist within a radiology practice. Potential solutions such as instituting a checklist for technologists during each acquisition procedure and routine auditing may be required.

AI-based, automated chamber volumetry from gated, non-contrast CT.

BACKGROUND: Accurate chamber volumetry from gated, non-contrast cardiac CT (NCCT) scans can be useful for potential screening of heart failure. OBJECTIVES: To validate a new, fully automated, AI-based method for cardiac volume and myocardial mass quantification from NCCT scans compared to contrasted CT Angiography (CCTA). METHODS: Of a retrospectively collected cohort of 1051 consecutive patients, 420 patients had both NCCT and CCTA scans at mid-diastolic phase, excluding patients with cardiac devices. Ground truth values were obtained from the CCTA scans. RESULTS: The NCCT volume computation shows good agreement with ground truth values. Volume differences [95% CI ] and correlation coefficients were: -9.6 [-45; 26] mL, r â€‹= â€‹0.98 for LV Total, -5.4 [-24; 13] mL, r â€‹= â€‹0.95 for LA, -8.7 [-45; 28] mL, r â€‹= â€‹0.94 for RV, -5.2 [-27; 17] mL, r â€‹= â€‹0.92 for RA, -3.2 [-42; 36] mL, r â€‹= â€‹0.91 for LV blood pool, and -6.7 [-39; 26] g, r â€‹= â€‹0.94 for LV wall mass, respectively. Mean relative volume errors of less than 7% were obtained for all chambers. CONCLUSIONS: Fully automated assessment of chamber volumes from NCCT scans is feasible and correlates well with volumes obtained from contrast study.