Grid Databases for Shared Image Analysis in the MammoGrid Project

The MammoGrid project aims to prove that Grid infrastructures can be used for collaborative clinical analysis of database-resident but geographically distributed medical images. This requires: a) the provision of a clinician-facing front-end workstation and b) the ability to service real-world clinician queries across a distributed and federated database. The MammoGrid project will prove the viability of the Grid by harnessing its power to enable radiologists from geographically dispersed hospitals to share standardized mammograms, to compare diagnoses (with and without computer aided detection of tumours) and to perform sophisticated epidemiological studies across national boundaries. This paper outlines the approach taken in MammoGrid to seamlessly connect radiologist workstations across a Grid using an "information infrastructure" and a DICOM-compliant object model residing in multiple distributed data stores in Italy and the UKComment: 10 pages, 5 figure

A study to understand the acceptance of DICOM Structured Reports on Breast Imaging

Purpose: To create a Digital Imaging and Communication in Medicine Structured Reports (DICOM-SR) Repository and compare the acceptance of Free Text (FT) versus Structured Reports (SR) in communication of Breast Imaging findings. Materials and Methods: It was conceptualized the MamoCatalogue to the structuring of the Reports and the SR were converted into DICOM-SR and integrated with Dicoogle. After that, seven representative Breast Imaging Reports were selected and evaluated by a group of 25 Physicians. Each Physician evaluated the seven Reports, in FT and SR with a 3 months timelag, about their, Structure, Clarity and assertiveness, Diagnostic/Recommendations, Easiness of reading, Full reading, Partially reading with Breast Imaging Reporting and Data System (BI-RADS) focus and Ambiguity. Results: A DICOM-SR Repository was created and the assessment of the acceptance of the FT vs. SR revealed that there is a global trend favoring FT. Nevertheless, a group wise analysis revealed that for Gynaecologists and General Practitioners (GP) the differences between FT and SR weren't significant, unlike what happens with Radiologists. Conclusion: The DICOM-SR Repository allows the query/retrieve data for Reports and the communication with Gynaecologists and GP by SR was satisfactory. Although, Radiologists acceptance must be reinforced upon global communication and management strategy

A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer

The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. This work was partially supported by the Vicerectorat d'Investigacio de la Universitat Politecnica de Valencia (UPVLC) to develop the project "Mejora del proceso diagnostico del cancer de mama" with reference UPV-FE-2013-8.Medina, R.; Torres Serrano, E.; Segrelles Quilis, JD.; Blanquer Espert, I.; Martí Bonmatí, L.; Almenar-Cubells, D. (2015). A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer. Journal of Digital Imaging. 28(2):132-145. doi:10.1007/s10278-014-9728-6S132145282Ratib O: Imaging informatics: From image management to image navigation. Yearb Med Inform 2009; 167–172Oakley J. Digital Imaging: A Primer for Radiographers, Radiologists and Health Care Professionals. Cambridge University Press, 2003.Prokosch HU, Dudeck J: Hospital information systems: Design and development characteristics, impact and future architecture. Elsevier health sciences, 1995Foster I, Kesselman C, Tuecke S. The anatomy of the grid: Enabling scalable virtual organizations. Int J High Perform Comput Appl 2001; 15(3):200–222.Oram A: Peer-to-Peer: Harnessing the power of disruptive technologies. O’Reilly Media, 2001National Institute of Standards and Technology. The NIST Definition of Cloud Computing. 2011. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (accessed 29 Jan 2013)Oster S, Langella S, Hastings S, Ervin D, Madduri R, Phillips J, Kurc T, Siebenlist F, Covitz P, Shanbhag K, Foster I, Saltz J. caGrid 1.0: An enterprise grid infrastructure for biomedical research. J Am Med Inform Assoc 2008; 15:138–149.Natter MD, Quan J, Ortiz DM, et al. An i2b2-based, generalizable, open source, self-scaling chronic disease registry. J Am Med Inform Assoc 2013; 20:172–179.Ohno-Machado L, Bafna V, Boxwala AA, et al. iDASH: Integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 2012; 19:196–201.Channin DS, Mongkolwat P, Kleper V, Rubin DL. Computing human image annotation. Conf Proc IEEE Eng Med Biol Soc 2009; 1:7065–8.Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41(2):387–392.Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Med Syst 2012; 36(5):3029–3049.Rubin DL. Creating and curating a terminology for radiology: Ontology modeling and analysis. J Digit Imaging 2008; 21(4):355–362.Kahn CE, Jr., Langlotz CP, Burnside ES, Carrino JA, Channin DS, Hovsepian DM, et al. Toward best practices in radiology reporting. Radiology 2009; 252(3):852–856.Taira PK, Soderlang SG, JAbovits RM. Automatic structuring of radiology free-text reports. Radiographics 2001; 21(1); 237–245.Fujii H, Yamagishi H, Ando Y, Tsukamoto N, Kawaguchi O, Kasamatsu T, et al. Structuring of free-text diagnostic report. Stud. Health Technol. Inform. 2007; 129: 669–673.Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; 306(8):848–855.Clunie DA: DICOM structured reporting. PixelMed Publishing, 2000D’Avolio LW, Nguyen TM, Farwell WR, Chen Y, Fitzmeyer F, Harris OM, Fiore LD. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). J Am Med Inform Assoc 2012; 17:375–382.Napel SA, Beaulieu CF, Redriguez C, Cui J, Xu J, Grupta A, et al. Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology 2010; 256(1): 243–252.Langlotz CP. RadLex: A new method for indexing online educational materials. Radiographics 2006; 26(6):1595–1597.Crestania F, Vegas J, de la Fuente P. A graphical user interface for the retrieval of hierarchically structured documents. Inf Process Manag 2004; 40(2):269–289.Weiss DL, Langlotz CP. Structured reporting: Patient care enhancement or productivity nightmare? Radiology 2008. 249(3):739–747.Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19(3):413–422.Patrick R, Julien G, Christian L, Antoine G. Automatic medical encoding with SNOMED categories. BMC Med Inform Decis Mak 2008; 8(Suppl 1): S1–S6.Lopez-Garcia P, Boeker M, Illarramendi A, Schulz S. Usability-driven pruning of large ontologies: The case of SNOMED CT, J Am Med Inform Assoc 2012; 19:e102-e109.World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/apps/icd/icd10online/ (accessed 29 Jan 2013)American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas)World Health Organization. International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3). http://www.who.int/classifications/icd/adaptations/oncology/en/index.html (accessed 29 Jan 2013)Greene FL. TNM: Our language of cancer. CA Cancer J Clin 2004; 54(3):129–130.American Joint Committee of Cancer (AJCC). AJCC Cancer Staging Manual. Seventh Edition. Springer, 2010Hussein R, Engelmann U, Schroeter A, Meinzer HP. DICOM structured reporting: Part 1. Overview and characteristics, Radiographics 2004; 24(3):891–896.Sluis D, Lee KP, Mankovich N. DICOM SR - integrating structured data into clinical information systems. Medicamundi 2002; 46(2):31–36.Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 2012; 19(5):913–916.Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, Catarzi S, Lamberini MP, Marcelli G, Pellizzoni R, Pesce B, Risso G, Russo F, Scorsolini A. Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience). Breast 2006; 15(1):44–51.National Electrical Manufacturers Association (NEMA). Digital Imaging and Communications in Medicine (DICOM). Part 16: Content Mapping Resource. http://medical.nema.org/dicom/2004/04_16PU.PDF (accessed 29 Jan 2013)Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shvo AS. HL7 clinical document architecture, release 2. J Am Med Inform Assoc 2006; 13:30–39.Blanquer I, Hernández V, Meseguer JE, Segrelles D. Content-based organisation of virtual repositories of DICOM objects. Future Gener Comput Syst 2009; 25(6):627–637.Blanquer I, Hernández V, Segrelles D, Torres E. Enhancing privacy and authorization control scalability in the grid through ontologies. IEEE Trans Inf Technol Biomed 2009; 12(1):16–24.Salavert J, Maestre C, Segrelles D, Blanquer I, Hernández V, Medina R, Martí L: Grid prototype to support cancer of breast diagnostics in clinic practice. Proc of the 4th. Iberian Grid Infrastructure Conf. Netbiblo, 2010Segrelles D, Franco JM, Medina R, Blanquer I, Salavert J, Hernandez V, Martí L, Díaz G, Ramos R, Guevara MA, González N, Loureiro J, Ramos I. Exchanging data for breast cancer diagnosis on heterogeneous grid platforms. Computing and Informatics 2012; 31(1):3–15.Ali MS, Consens M, Lalmas M. Extended structural relevance framework: A framework for evaluating structured document retrieval. Inf Retrieval 2012; 15:558–590.Welter P, Riesmeier J, Fischer B, Grouls C, Kuhl C, Deserno, TM. Bridging the integration gap between imaging and information systems: A uniform data concept for content-based image retrieval in computer-aided diagnosis. J Am Med Inform Assoc 2011; 18:506–510.Jenkins CW. Application prototyping: A case study. Perform Eval Rev 1981; 10(1):21–27.Generalitat Valenciana. Conselleria de Sanitat. Oncoguía de Cáncer de Mama Comunidad Valenciana. http://publicaciones.san.gva.es/publicaciones/documentos/V.2478-2006.pdf (accessed 29 Jan 2013)Maestre C, Segrelles-Quilis JD, Torres E, Blanquer I, Medina R, Hernández V, Martí L. Assessing the usability of a science gateway for medical knowledge bases with TRENCADIS. J Grid Computing 2012; 10:665–688.Lewis J. IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use. Int J Hum-Comput Interact 1995; 7(1):57–78.Lewis JR. Psychometric evaluation of the PSSUQ using data from five years of usability studies. Int J Hum-Comput Interact 2002; 14(3–4):463–488.Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika 1965; 52(3–4):591–611.Chhatwal J, Alagoz O, Lindstrom MJ, Kahn Jr CE, Shaffer KA, Burnside ES. A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol 2009; 192:1117–1127

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source

Towards structured sharing of raw and derived neuroimaging data across existing resources

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery

PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Description and Experience of the Clinical Testbeds

This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

A timely computer-aided detection system for acute ischemic and hemorrhagic stroke on CT in an emergency environment

Standalone Presentations: no. LL-IN1105BACKGROUND: When a patient is accepted in the emergency room suspected of stroke, time is of the most importance. The infarct brain area suffers irreparable damage as soon as three hours after the onset of stroke symptoms. Non-contrast CT scan is the standard first line of investigation used to identify hemorrhagic stroke cases. However, CT brain images do not show hyperacute ischemia and small hemorrhage clearly and thus may be missed by emergency physicians. We reported a timely computer-aided detection (CAD) system for small hemorrhages on CT that has been successfully developed as an aid to ER physicians to help improve detection for Acute Intracranial Hemorrhage (AIH). This CAD system has been enhanced for diagnosis of acute ischemic stroke in addition to hemorrhagic stroke, which becomes a more complete and clinically useful tool for assisting emergency physicians and radiologists. In the detection algorithm, brain matter is first segmented, realigned, and left-right brain symmetry is evaluated. As in the AIH system, the system confirms hemorrhagic stroke by detecting blood presence with anatomical and medical knowledge-based criteria. For detecting ischemia, signs such as regional hypodensity, blurring of grey and white matter differentiation, effacement of cerebral sulci, and hyperdensity in middle cerebral artery, are evaluated …published_or_final_versio

Information Technologies for the Healthcare Delivery System

That modern healthcare requires information technology to be efficient and fully effective is evident if one spends any time observing the delivery of institutional health care. Consider the observation of a practitioner of the discipline, David M. Eddy, MD, PhD, voiced in Clinical Decision Making, JAMA 263:1265-75, 1990, . . .All confirm what would be expected from common sense: The complexity of modern medicine exceeds the inherent limitations of the unaided human mind. The goal of this thesis is to identify the technological factors that are required to enable a fully sufficient application of information technology (IT) to the modern institutional practice of medicine. Perhaps the epitome of healthcare IT is the fully integrated, fully electronic patient medical record. Although, in 1991 the Institute of Medicine called for such a record to be standard technology by 2001, it has still not materialized. The author will argue that some of the technology and standards that are pre-requisite for this achievement have now arrived, while others are still evolving to fully sufficient levels. The paper will concentrate primarily on the health care system in the United States, although much of what is contained is applicable to a large degree, around the world. The paper will illustrate certain of these pre-requisite IT factors by discussing the actual installation of a major health care computer system at the University of Rochester Medical Center (URMC) in Rochester, New York. This system is a Picture Archiving and Communications System (PACS). As the name implies, PACS is a system of capturing health care images in digital format, storing them and communicating them to users throughout the enterprise