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The development of comprehensive picture archive and communication systems (PACS) has mainly been limited to proprietary developments by vendors, though a number of freely available software projects have addressed specific image management tasks. The openSourcePACS project aims to provide an open source, common foundation upon which not only can a basic PACS be readily implemented, but to also support the evolution of new PACs functionality through the development of novel imaging applications and services. open Source PACS consists of four main software modules: 1) image order entry, which enables the ordering and tracking of structured image requisitions; 2) an agent-based image server framework that coordinates distributed image services including routing, image processing, and querying beyond the present digital image and communications in medicine (DICOM) capabilities; 3) an image viewer, supporting standard display and image manipulation tools, DICOM presentation states, and structured reporting; and 4) reporting and result dissemination, supplying web-based widgets for creating integrated reports. All components are implemented using Java to encourage cross-platform deployment. To demonstrate the usage of openSourcePACS, a preliminary application supporting primary care/specialist communication was developed and is described herein. Ultimately, the goal of openSourcePACS is to promote the wide-scale development and usage of PACS and imaging applications within academic and research communities.

Background:Vestibular schwannomas are a rare cause of asymmetrical hearing loss, and routine screening with magnetic resonance imaging can be costly. This paper reports results on vestibular schwannoma screening at our institution and compares the cost of screening to a utility of hearing benefit.Method:All screening examinations with magnetic resonance imaging performed for asymmetrical hearing loss between 2006 and 2011 were retrospectively reviewed. The cost per new vestibular schwannoma diagnosis was calculated. The cost per patient for those who benefitted from intervention was estimated based on rates of hearing preservation reported in the literature.Results:Forty-five (4.3 per cent) of 1050 screening examinations with magnetic resonance imaging performed for asymmetrical hearing loss were positive for vestibular schwannoma, and the cost per new diagnosis was $11 436. The estimated screening cost per patient for those who benefitted from surgery or radiation was $147 030, while US federal compensation for unilateral hearing loss was $44 888.Conclusion:Although we achieved a lower screening cost per new diagnosis than reported in the current literature, there remains disparity between the screening cost per benefitted patient and the ‘benefit’ of hearing. [ABSTRACT FROM PUBLISHER]

Background and Purpose: Elevation in pulsatility indices (PIs) as measured by transcranial Doppler (TCD) have been postulated to reflect downstream increased vascular resistance caused by small-vessel ischemic disease.Methods: The authors retrospectively compared TCD PIs and magnetic resonance imaging (MRI) manifestations of small-vessel disease in 55 consecutive patients who underwent TCD studies and brain MRI within 6 months of each other during a 2-year period.Results: Correlations between TCD middle cerebral artery PIs and MRI measures were as follows: periventricular hyperintensity (PVH) = 0.52 (P < .0001), deep white matter hyperintensity (DWMH) = 0.54 (P < .0001), lacunar disease = 0.31 (P = .02), and combined PVH/DWMH/lacunes = 0.54 (P < .0001). Correlation between pontine ischemia and vertebrobasilar PIs was 0.46 (P = .0004). Univariate analysis showed that age, elevated PI, and hypertension strongly correlated with white matter disease measures. After adjusting for these factors in a multivariate Poisson regression analysis, PI remained an independent predictor of white matter disease. Receiver operator curve analyses identified PI cut points that allowed discrimination of PVH with 89% sensitivity and 86% specificity and discrimination of DWMH with 70% sensitivity and 73% specificity.Conclusions: Elevation in PIs as measured by TCD shows strong correlation with MRI evidence of small-vessel disease. TCD may be a useful physiologic index of the presence and severity of diffuse small-vessel disease. [ABSTRACT FROM AUTHOR]

Background and Purpose: Treatment of acute ischemic stroke is heavily contingent upon time, as there is a strong relationship between time clock and tissue progression. Work has established imaging biomarker assessments as surrogates for time since stroke (TSS), namely, by comparing signal mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) imaging. Our goal was to develop an automatic technique for determining TSS from imaging that does not require subspecialist radiology expertise., Methods: Using 772 patients (66 ± 9 years, 319 women), we developed and externally evaluated a deep learning network for classifying TSS from MR images and compared algorithm predictions to neuroradiologist assessments of DWI-FLAIR mismatch. Models were trained to classify TSS within 4.5 hours and performance metrics with confidence intervals were reported on both internal and external evaluation sets., Results: Three board-certified neuroradiologists' DWI-FLAIR mismatch assessments, based on majority vote, yielded a sensitivity of .62, a specificity of .86, and a Fleiss' kappa of .46 when used to classify TSS. The deep learning method performed similarly to radiologists and outperformed previously reported methods, with the best model achieving an average evaluation accuracy, sensitivity, and specificity of .726, .712, and .741, respectively, on an internal cohort and .724, .757, and .679, respectively, on an external cohort., Conclusion: Our model achieved higher generalization performance on external evaluation datasets than the current state-of-the-art for TSS classification. These results demonstrate the potential of automatic assessment of onset time from imaging without the need for expertly trained radiologists., (© 2022 American Society of Neuroimaging.)

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Motivation: Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these 'radiogenomic' studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes., Results: We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value., Availability and Implementation: https://github.com/novasmedley/deepRadiogenomics., Contact: whsu@mednet.ucla.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871 ± 0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.

Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient's treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden representations from the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional imaging features. Finally, we discuss a strategy to visualize the learned features from the proposed deep learning model. The cross-validation results show that our best classifier achieved an area under the curve of 0.68, which improves significantly over current clinical methods (0.58), demonstrating the potential benefit of using advanced machine learning methods in TSS classification.

Our work facilitates the identification of veterans who may be at risk for abdominal aortic aneurysms (AAA) based on the 2007 mandate to screen all veteran patients that meet the screening criteria. The main research objective is to automatically index three clinical conditions: pertinent negative AAA, pertinent positive AAA, and visually unacceptable image exams. We developed and evaluated a ConText-based algorithm with the GATE (General Architecture for Text Engineering) development system to automatically classify 1402 ultrasound radiology reports for AAA screening. Using the results from JAPE (Java Annotation Pattern Engine) transducer rules, we developed a feature vector to classify the radiology reports with a decision table classifier. We found that ConText performed optimally on precision and recall for pertinent negative (0.99 (0.98-0.99), 0.99 (0.99-1.00)) and pertinent positive AAA detection (0.98 (0.95-1.00), 0.97 (0.92-1.00)), and respectably for determination of non-diagnostic image studies (0.85 (0.77-0.91), 0.96 (0.91-0.99)). In addition, our algorithm can determine the AAA size measurements for further characterization of abnormality. We developed and evaluated a regular expression based algorithm using GATE for determining the three contextual conditions: pertinent negative, pertinent positive, and non-diagnostic from radiology reports obtained for evaluating the presence or absence of abdominal aortic aneurysm. ConText performed very well at identifying the contextual features. Our study also discovered contextual trigger terms to detect sub-standard ultrasound image quality. Limitations of performance included unknown dictionary terms, complex sentences, and vague findings that were difficult to classify and properly code.

Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701-0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process.

Imaging is one of the most important sources of clinically observable evidence that provides broad coverage, can provide insight on low-level scale properties, is noninvasive, has few side effects, and can be performed frequently. Thus, imaging data provides a viable observable that can facilitate the instantiation of a theoretical understanding of a disease for a particular patient context by connecting imaging findings to other biologic parameters in the model (e.g., genetic, molecular, symptoms, and patient survival). These connections can help inform their possible states and/or provide further coherent evidence. The field of radiomics is particularly dedicated to this task and seeks to extract quantifiable measures wherever possible. Example properties of investigation include genotype characterization, histopathology parameters, metabolite concentrations, vascular proliferation, necrosis, cellularity, and oxygenation. Important issues within the field include: signal calibration, spatial calibration, preprocessing methods (e.g., noise suppression, motion correction, and field bias correction), segmentation of target anatomic/pathologic entities, extraction of computed features, and inferencing methods connecting imaging features to biological states.

Advanced liver disease has long been associated with cerebral abnormalities. These abnormalities, termed acquired hepatocerebral degeneration, are typically visualized as T1 weighted hyperintensity on MRI in the deep gray matter of the basal ganglia. Recent reports, however, have demonstrated that a subset of patients with chronic alcoholic liver disease may also develop white matter abnormalities. Thus far, the morphology of these changes is not well characterized. Previous studies have described these changes as patchy, sporadic white matter abnormalities but have not posited localization of these changes to any particular white matter tracts. This paper hypothesizes that the white matter findings associated with advanced alcoholic liver disease localize to the corticocerebellar tracts. As an initial investigation of this hypothesis, 78 patients with a diagnosis of liver cirrhosis and an MRI showing clearly abnormal T1 weighted hyperintensity in the bilateral globus pallidus, characteristic of chronic liver disease, were examined for white matter signal abnormalities in the corticocerebellar tracts using FLAIR and T2 weighted images. The corticocerebellar tracts were subdivided into two regions: periventricular white matter (consisting of the sum of the centrum-semiovale and corona radiata), and lower white matter (consisting of the corona radiata, internal capsules, middle cerebral peduncles, middle cerebellar peduncles and cerebellum). As compared to matched controls, significantly greater signal abnormalities in both the periventricular white matter and lower white matter regions of the corticocerebellar tracts were observed in patients with known liver cirrhosis and abnormal T1 W hyperintensity in the globi pallidi. This difference was most pronounced in the lower white matter region of the corticocerebellar tract, with statistical significance of p<0.0005. Furthermore, the pathophysiologic mechanism underlying these changes remains unknown. This paper hypothesizes that the etiology of white matter changes associated with advanced liver disease may resemble that of white matter findings in subacute combined degeneration secondary to vitamin B12 deficiency. Specifically, significant evidence suggests that dysfunctional methionine metabolism as well as dysregulated cytokine production secondary to B12 deficiency play a major role in the development of subacute combined degeneration. Similar dysfunction of methionine metabolism and cytokine regulation is seen in alcoholic liver disease and is hypothesized in this paper to, at least in part, lead to white matter findings associated with alcoholic liver disease., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Objectives: Outcome studies among post-menopausal females with calcified carotid artery plaque (CCAP) on their panoramic images have not been previously undertaken. We sought to compare the extent of abdominal aortic calcification (AAC) on lateral lumbar spine radiographs (LLSRs), among groups of females with (CCAP+) and without (CCAP-) carotid lesions on their panoramic images. "Severe" levels of AAC have previously been validated as a risk indicator of future adverse cardiovascular events., Methods: This cross-sectional case-control study included a "CCAP+ group" consisting of females more than 50 years of age having the carotid lesion diagnosed by their dentists and an atherogenic risk factor (age, body mass index, hypertension, diabetes and dyslipidaemia)-matched "CCAP- group". A physician radiologist, using the Framingham index, evaluated the LLSRs for the magnitude of AAC. Summary statistics for key variables were computed and conditional logistic regression techniques were considered., Results: Members of the CCAP+ group were significantly (p=0.038) more likely to demonstrate "severe" levels of AAC on their LLSRs than members of the CCAP group., Conclusions: This is the first published study demonstrating that CCAP on panoramic images of post-menopausal females is significantly associated with "severe" levels of AACs on LLSRs independent of traditional risk factors. Given that these levels of AAC are a validated risk indicator of future myocardial infarction and stroke, dentists must evaluate the panoramic images of post-menopausal females for the presence of CCAP. Patients with carotid atheromas should be referred to their physicians for further evaluation given the systemic implications.

Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Objective: With the increased routine use of advanced imaging in clinical diagnosis and treatment, it has become imperative to provide patients with a means to view and understand their imaging studies. We illustrate the feasibility of a patient portal that automatically structures and integrates radiology reports with corresponding imaging studies according to several information orientations tailored for the layperson., Methods: The imaging patient portal is composed of an image processing module for the creation of a timeline that illustrates the progression of disease, a natural language processing module to extract salient concepts from radiology reports (73% accuracy, F1 score of 0.67), and an interactive user interface navigable by an imaging findings list. The portal was developed as a Java-based web application and is demonstrated for patients with brain cancer., Results and Discussion: The system was exhibited at an international radiology conference to solicit feedback from a diverse group of healthcare professionals. There was wide support for educating patients about their imaging studies, and an appreciation for the informatics tools used to simplify images and reports for consumer interpretation. Primary concerns included the possibility of patients misunderstanding their results, as well as worries regarding accidental improper disclosure of medical information., Conclusions: Radiologic imaging composes a significant amount of the evidence used to make diagnostic and treatment decisions, yet there are few tools for explaining this information to patients. The proposed radiology patient portal provides a framework for organizing radiologic results into several information orientations to support patient education.

With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief network was defined and fitted to data from our institution's observational quality improvement database for acute stroke patients. The resulting network validates known relationships between variables, treatment decisions and outcomes, and enables the exploration of new correlative relationships not defined in current guidelines.

Purpose: To determine whether radiology reports describe clinically significant carotid arterial stenosis in a consistent format that is actionable by ordering clinicians., Materials and Methods: This study was HIPAA compliant. Informed consent was waived. Institutional review board approval was obtained for this retrospective chart review, which included radiology reports of carotid artery imaging for patients hospitalized with ischemic stroke at 127 Veterans Affairs medical centers in 2006-2007. "Clinically significant results" were defined as results with at least 50% stenosis or at least moderate stenosis, excluding complete occlusion. How often clinically significant results were reported as an exact percentage stenosis (such as 60%), range (such as 50%-69%), or category (such as moderate) was determined. Among results reported as a range, how often the range bracketed clinical thresholds of 50% and 70% (typically used to determine appropriateness of carotid arterial revascularization) was determined., Results: Among 2675 patients, there were 6618 carotid imaging results, of which 1015 (15%) were considered clinically significant. Among 695 clinically significant results at ultrasonography (US), 348 (50%) were described as a range, and another 314 (45%) were reported as an exact percentage stenosis. Among the 348 clinically significant US results reported as a range, 259 (74%) bracketed the thresholds of 50% or 70%. For magnetic resonance angiographic results, 48% (106 of 221) qualitatively described clinically significant results as a category, 38% (84 of 221) as an exact percentage stenosis, and 14% (31 of 221) as a range., Conclusion: In this national health care system, the manner in which clinically significant carotid arterial stenosis was reported varied widely., (RSNA, 2012)

Background: The basal ganglia (BG) are involved in executive language functions (i.e., verbal fluency) through their connections with cortical structures. The caudate and putamen receive separate inputs from prefrontal and premotor cortices, and may differentially contribute to verbal fluency performance. We examined BG integrity in relation to lexico-semantic verbal fluency performance among older HIV infected adults., Method: 20 older (50+ years) HIV+ adults underwent MRI and were administered measures of semantic and phonemic fluency. BG (caudate, putamen) regions of interest were extracted., Results: Performance on phonemic word generation significantly predicted caudate volume, whereas performance on phonemic switching predicted putamen volume., Conclusions: These findings suggest a double dissociation of BG involvement in verbal fluency tasks with the caudate subserving word generation and the putamen associated with switching. As such, verbal fluency tasks appear to be selective to BG function., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Beginning with a case report of nitrous oxide (N₂O)-induced B₁₂ deficiency myelopathy, this article reviews the clinical biochemistry of vitamin B₁₂, and examines the pathogenetic mechanisms by which B₁₂ deficiency leads to neurologic damage, and how this damage is potentiated by N₂O exposure. The article systematically examines the available experimental data relating to the two main coenzyme mechanisms that are usually suggested in clinical articles, particularly the deficient methylation hypothesis. The article demonstrates that neither of these mechanisms is fully consistent with the available data. The article then presents a novel mechanism based on new data from the neuroimmunology basic science literature which suggests that the pathogenesis of B₁₂ deficiency myelopathy may not be related to its role as a coenzyme, but rather to newly discovered functions of B₁₂ in regulating cytokines and growth factors., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Sentences and phrases that represent a certain meaning often exhibit patterns of variation where they differ from a basic structural form by one or two words. We present an algorithm that utilizes multiple sequence alignments (MSAs) to generate a representation of groups of phrases that possess the same semantic meaning but also share in common the same basic word sequence structure. The MSA enables the determination not only of the words that compose the basic word sequence, but also of the locations within the structure that exhibit variation. The algorithm can be utilized to generate patterns of text sequences that can be used as the basis for a pattern-based classifier, as a starting point to bootstrap the pattern building process for a regular expression-based classifiers, or serve to reveal the variation characteristics of sentences and phrases within a particular domain.

We describe the development of a prototype tool for the construction of longitudinal cases studies that can be used for teaching files, construction of clinical databases, and for patient education. The test domain is neuro-oncology. The features of the tool include: 1) natural language processing tools to assist structuring report information; 2) integration of imaging data; 3) integration of drug information; 4) target data model that includes the dimensions of space, time, existence, and causality; 5) user interface that provides three levels of information including overview, filtered summarization, and details on demand. The results of this preliminary work include a full prototype for neuro-oncology patients that allow users an efficient means for scanning a patients imaging and support data.

Careful examination of the medical record of brain-tumor patients can be an overwhelming task for the neuroradiologist. The number of clinical documents alone may approach 100 for a patient that has a 3-year-old brain tumor. The neuroradiologist's evaluation of a patient's brain tumor involves examining the current imaging exam and checking for previous imaging exams that may occur pre- or post-treatment. The goal of this research is to develop an effective method to review all of the pertinent patient information from the medical record. We have designed and developed a medical system that incorporates Hospital Information Systems, Radiology Information Systems, and Picture Archiving and Communications Systems information. Our research improves clinical review of patient's data by organizing image display, removing unnecessary documents, and mining for key clinical scenarios that are important in the assessment and care of brain-tumor patients.

The communication of imaging findings to a referring physician is an important role of the radiologist. However, communication between onsite and offsite physicians is a time-consuming process that can obstruct work flow and frequently involves no exchange of visual information, which is especially problematic given the importance of radiologic images for diagnosis and treatment. A prototype World Wide Web-based image documentation and reporting system was developed for use in supporting a "communication loop" that is based on the concept of a classic "wet-read" system. The proposed system represents an attempt to address many of the problems seen in current communication work flows by implementing a well-documented and easily accessible communication loop that is adaptable to different types of imaging study evaluation. Images are displayed in a native (DICOM) Digital Imaging and Communications in Medicine format with a Java applet, which allows accurate presentation along with use of various image manipulation tools. The Web-based infrastructure consists of a server that stores imaging studies and reports, with Web browsers that download and install necessary client software on demand. Application logic consists of a set of PHP (hypertext preprocessor) modules that are accessible with an application programming interface. The system may be adapted to any clinician-specialist communication loop, and, because it integrates radiologic standards with Web-based technologies, can more effectively communicate and document imaging data., (RSNA, 2007)

Our research addresses how to improve physician to physician communication of patient information, and how to prevent lapses of patient care as they are referred to other clinicians within the healthcare system. The wet read consultation is defined as a rapid response to a clinical question posed by a referring physician to a clinical specialist. This research involves the development of an imaging-based wet read consultation system called StructConsult (SC), which facilitates communication between non-imaging specialist (i.e., primary care physician (PCP), emergency room (ER) physician, or referring physician), and an imaging specialist-radiologist. To facilitate data mining and effective recall, SC utilizes a data model based on the Digital Image Communications in Medicine (DICOM) standard for grayscale presentation state and structured reporting. SC requires information from four sources: (a) patient-specific demographics, clinical hypothesis, and reason for exam, (b) sentinel image capture from a DICOM image study, (c) direct capture of radiologist's image operations and annotations, and (d) radiologist's response to the chief compliant, and the reason for examination. SC allows users to add additional functionality to a Picture Archiving System to improve patient care.

A patient's electronic medical record contains a large number of medical reports and imaging studies. Identifying the relevant information in order to make a diagnosis can be a time consuming process that can easily overwhelm the physician. Summarizing key clinical information for physicians evaluating brain tumor patients is an ongoing research project at our institution. Notably, identifying documents associated with brain tumor is an important step in collecting the data relevant for summarization. Current electronic medical record systems lack meta-information which is useful in structuring heterogeneous medical information. Thus, identifying reports relevant to a particular task cannot be easily retrieved from a structured database. This necessitates content analysis methods for identifying relevant reports. This paper reports a system designed to identify brain-tumor related reports from an assorted collection of clinical reports. A large collection of clinical reports was obtained from our university hospital database. A domain expert manually annotated the documents classifying them into `related' and ùnrelated' categories. A multinomial naïve Bayes classifier was trained to use word level and UMLS concept level features from the reports to identify brain tumor related reports from the assorted collection. The system was trained on 90% and tested on 10% of the manually annotated corpus. A ten-fold cross validation is reported. Performance of the system was best (f-score 94.7) when the system was trained using both word level and UMLS concept level features. Using UMLS concepts improved classifier accuracy., Competing Interests: Competing interests The authors declare no competing interests.

This work proposes a methodology for content-based image retrieval of glioblastoma multiforme (GBM) and non-GBM tumors. Regions containing GBM lesions from 40 patients and non-GBM lesions from 20 patients were manually segmented from MR imaging studies (T1 post-contrast and T2 weighted channels) to form the training set. In addition to the two acquired channels, a composite image was formed by an image fusion method. Data reduction techniques, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied on the training sets (T1 post, T2, composite, and multi-channel combining the PCA features from T1 post and T2). The retrieval accuracy was evaluated using a 'leave-one-out' strategy with query images belonging to 'normal', 'GBM' and 'non-GBM' classes. Several combinations of the similarity metric and classifier were used: Euclidean similarity measures with k-means classifier for the PCA and LDA features and support vector machine (SVM) nonlinear classifier (radial basis function kernel) with the PCA derived features. The SVM classifier served as a comparison of nonlinear techniques vs. linear ones. Multi-channel PCA was 100% accurate in classifying a query image as either 'normal' or 'abnormal'. The highest accuracy in classification of tumor grade (GBM or other Grade 3) was 77% and was achieved by SVM coupled with the PCA features. The proposed algorithm intent is to be integrated into an automated decision support system for MR brain tumor studies.

Rationale and Objectives: A streamlined process of care supported by technology and imaging may be effective in managing the overall healthcare process and costs. This study examined the effect of an imaging-based electronic process of care on costs and rates of hospitalization, emergency room (ER) visits, specialist diagnostic referrals, and patient satisfaction., Materials and Methods: A healthcare process was implemented for an employer group, highlighting improved patient access to primary care plus routine use of imaging and teleconsultation with diagnostic specialists. An electronic infrastructure supported patient access to physicians and communication among healthcare providers. The employer group, a self-insured company, manages a healthcare plan for its employees and their dependents: 4,072 employees were enrolled in the test group, and 7,639 in the control group. Outcome measures for expenses and frequency of hospitalizations, ER visits, traditional specialist referrals, primary care visits, and imaging utilization rates were measured using claims data over 1 year. Homogeneity tests of proportions were performed with a chi-square statistic, mean differences were tested by two-sample t-tests. Patient satisfaction with access to healthcare was gauged using results from an independent firm., Results: Overall per member/per month costs post-implementation were lower in the enrolled population (126 dollars vs 160 dollars), even though occurrence of chronic/expensive diseases was higher in the enrolled group (18.8% vs 12.2%). Lower per member/per month costs were seen for inpatient (33.29 dollars vs 35.59 dollars); specialist referrals (21.36 dollars vs 26.84 dollars); and ER visits (3.68 dollars vs 5.22 dollars). Moreover, the utilization rate for hospital admissions, ER visits, and traditional specialist referrals were significantly lower in the enrolled group, although primary care and imaging utilization were higher. Comparison to similar employer groups showed that the company's costs were lower than national averages (119.24 dollars vs 146.32 dollars), indicating that the observed result was not attributable to normalization effects. Patient satisfaction with access to healthcare ranked in the top 21st percentile., Conclusion: A streamlined healthcare process supported by technology resulted in higher patient satisfaction and cost savings despite improved access to primary care and higher utilization of imaging.

The problem-oriented electronic medical record has been investigated as an alternative to source-oriented organization of patient data. At the core of a problem-oriented view is the medical problem list. Maintenance of the medical problem list is often manual, making it highly user dependent. We detail the beginnings of an automated medical problem list generator based on ICD-9: given a set of ICD-9 codes associated with a patient record, the system maps the codes (and related data) to an anatomy-centric hierarchy. 1 million patient encounters from an outpatient setting were reviewed to generate a unique set of 7,890 ICD-9 codes. Natural language processing of the ICD-9 string descriptions identified 1,981 anatomical terms, which were subsequently mapped to one of 21 anatomical categories. The output of the medical problem list generator was then used to create a problem-oriented, gestalt view of a patient's medical record. Preliminary evaluation of the generator revealed 100% recall, but only 60% precision. This initial work has highlighted several issues in defining a medical problem list, including questions of granularity and performance trade-offs.

We present a MR brain atlas for structure and function (diffusion weighted images). The atlas is customizable for contrast and orientation to match the current patient images. In addition, the atlas also provides normative values of MR parameters (T1, T2 and ADC values). The atlas is designed on informatics principles to provide context sensitive decision support at the time of primary image interpretation. Additional support for diagnostic interpretation is provided by a list of expert created most relevant 'Image Finding Descriptors' that will serve as cues to the user. The architecture of the atlas module is integrated into the image workflow of a radiology department to provide support at the time of primary diagnosis.

Reviewing brain tumor patients' complete medical record is a daunting task for any clinician. In current practice, the radiologist examines the most recent documents and then dictates an assessment of the patient's condition based on a review of the most current imaging study and compared with the most recent previous image study. Occasionally, the radiologist searches other clinical documents when more precise detail is needed. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information in three distinct ways: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.

We have developed a system to structure free-text neuroradiology reports using a natural language processing program and formatted the output into the digital image and communication in medicine (DICOM) standard for structured reporting (SR). DICOM SR formats the correspondence of pertinent diagnostic images to the radiologist's dictated report of clinical findings. In addition, DICOM SR allows the information to be organized into a tree structure. Individual nodes of the tree can contain individual items or lists. Structuring the content of free-text information allows the creation of hierarchies with defined relationships between the concepts contained within the report.

Rationale and Objectives: The purpose of this study was to determine the electronic requirements for supporting evidence-based radiology in today's medical environment., Materials and Methods: A software engineering technique, use case modeling, was performed for several clinical settings to determine the use of imaging and its role in evidence-based practice, with particular attention to issues relating to data access and the usage of clinical information. From this basic understanding, the analysis was extended to encompass evidence-based radiologic research and teaching., Results: The analysis showed that a system supporting evidence-based radiology must (a) provide a single point of access to multiple clinical data sources so that patient data can be readily used and incorporated into comprehensive radiologic consults and (b) provide quick access to external evidence in the way of similar patient cases and published medical literature, thus supporting evidence-based practice., Conclusion: Information infrastructures that aim to support evidence-based radiology not only must address issues related to the integration of clinical data from heterogeneous databases, but must facilitate access and filtering of patient data in order to improve radiologic consultation.

Background: Ischemic stroke commonly presents with sudden onset of focal deficit that is maximal at onset. Symptom onset marked by the spread of symptoms to contiguous body parts may suggest migraine, seizure or cerebral amyloid angiopathy (CAA) that is mimicking ischemic symptoms., Objective: To assess (1) if the spread of symptoms to contiguous body parts is an uncommon presentation of ischemic stroke and transient ischemic attack (TIA) and (2) if patients presenting with this symptom complex frequently have migraine, seizure or CAA mimicking stroke or TIA., Methods: 110 consecutive patients presenting with stroke-like symptoms were prospectively evaluated for symptoms at onset, abnormalities on cerebral imaging, risk factors for stroke, discharge diagnosis, and development of subsequent TIA/stroke, migraine, seizure, or cerebral hemorrhage during follow-up., Results: Of patients able to give a history of symptoms at onset, 23% described symptoms spreading to contiguous body parts. None had a history of migraine or seizure. None had clinical course or imaging features typical of CAA. During follow-up, 1 was diagnosed with migrainous stroke and none had suffered seizure or intracranial hemorrhage., Conclusions: The spread of symptoms to contiguous body parts is not uncommon at the onset of ischemic TIA/stroke. In our series, migrainous stroke was much less common and none had evidence of seizure or CAA., (Copyright 2002 S. Karger AG, Basel)

Purpose: To evaluate ultrasonography (US) and magnetic resonance (MR) angiography in the differentiation between occlusion and near occlusion of internal carotid artery (ICA)., Materials and Methods: Consecutive patients with occlusion or near occlusion of ICA at catheter angiography and who underwent MR angiography and US were included. MR angiography and US were compared with catheter angiography, the standard, for the ability to help distinguish occlusion from near occlusion. Noninvasive examinations were evaluated for the ability to classify near occlusions as having severe focal stenosis with distal luminal collapse versus diffuse nonfocal disease. The 95% CIs were calculated., Results: In 55 of 274 patients with 548 ICAs, catheter angiography depicted 37 total occlusions and 21 near occlusions. US depicted all total occlusions; MR angiography depicted 34 (92%) (95% CI: 0.78, 0.98). US depicted 18 (86%) of 21 (95% CI: 0.64, 0.97) near occlusions; MR angiography depicted all (100%). Of 18 vessels that were determined to be patent at US, 17 (94%) (95% CI: 0.73, 0.99) were classified as having focal stenosis or diffuse disease. Because flow gaps were identified in vessels with focal and diffuse disease, MR angiography was not effective in helping to differentiate these lesions., Conclusion: Assuming US is the initial imaging examination, when occlusion is diagnosed, MR angiography can depict it. If occlusion is confirmed, no further imaging is necessary. US performed well in helping to differentiate vessels with focal severe stenosis from those with diffuse disease. MR angiography added little in this group. Catheter angiography remains beneficial for vessels with diffuse nonfocal narrowing.

Clinical data sets for neuroradiological cases can be quite large. A typical brain tumor patient at UCLA will undergo 8-10 separate studies over a 2 year period, each study will produce 60-100 magnetic resonance (MR) images. Gathering and sorting through a patient s imaging events during the course of treatment can be both overwhelming and time consuming. The purpose of this research is to develop an intelligent pre-fetch and hanging protocol that automatically gathers the relevant prior examinations from a picture archiving, and communication systems (PACS) archive and sends the pertinent historical images to the diagnostic display station where the new examination is subsequently read out. The intelligent hanging protocol describes the type of layout and sequence for image display. We have developed a classification scheme to organize the pertinent patient information to selectively pre-fetch and intelligently present the images to review brain tumor cases for a diagnostic neuroradiology workstation.