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Raboudi A, Allanic M, Balvay D, Hervé PY, Viel T, Yoganathan T, Certain A, Hilbey J, Charlet J, Durupt A, Boutinaud P, Eynard B, Tavitian B. The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology. J Biomed Inform 2022; 127:104007. [DOI: 10.1016/j.jbi.2022.104007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/24/2021] [Accepted: 01/28/2022] [Indexed: 11/16/2022]
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Imaging Biomarker Ontology (IBO): A Biomedical Ontology to Annotate and Share Imaging Biomarker Data. JOURNAL ON DATA SEMANTICS 2018. [DOI: 10.1007/s13740-018-0093-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Anvari A, Halpern EF, Samir AE. Essentials of Statistical Methods for Assessing Reliability and Agreement in Quantitative Imaging. Acad Radiol 2018; 25:391-396. [PMID: 29241596 PMCID: PMC5834361 DOI: 10.1016/j.acra.2017.09.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 09/08/2017] [Accepted: 09/09/2017] [Indexed: 10/18/2022]
Abstract
Quantitative imaging is increasing in almost all fields of radiological science. Modern quantitative imaging biomarkers measure complex parameters including metabolism, tissue microenvironment, tissue chemical properties or physical properties. In this paper, we focus on measurement reliability assessment in quantitative imaging. We review essential concepts related to measurement such as measurement variability and measurement error. We also discuss reliability study methods for intraobserver and interobserver variability, and the applicable statistical tests including: intraclass correlation coefficient, Pearson correlation coefficient, and Bland-Altman graphs and limits of agreement, standard error of measurement, and coefficient of variation.
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Affiliation(s)
- Arash Anvari
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114.
| | - Elkan F Halpern
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114
| | - Anthony E Samir
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114
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Iyappan A, Younesi E, Redolfi A, Vrooman H, Khanna S, Frisoni GB, Hofmann-Apitius M. Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features. J Alzheimers Dis 2017; 59:1153-1169. [PMID: 28731430 PMCID: PMC5611802 DOI: 10.3233/jad-161148] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.
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Affiliation(s)
- Anandhi Iyappan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Erfan Younesi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany
| | - Alberto Redolfi
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Henri Vrooman
- Departments of Radiology and Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC University Medical Center, The Netherlands
| | - Shashank Khanna
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
| | - Giovanni B Frisoni
- Laboratory of Epidemiology and Neuroimaging, IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and Laboratoire de Neuroimagerie du Vieillissement (LANVIE), University Hospitals and University of Geneva, Geneva, Switzerland
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for Information Technology, Bonn, Germany
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Gurcan MN, Tomaszewski J, Overton JA, Doyle S, Ruttenberg A, Smith B. Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem. J Biomed Inform 2016; 66:129-135. [PMID: 28003147 DOI: 10.1016/j.jbi.2016.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 11/30/2016] [Accepted: 12/13/2016] [Indexed: 01/04/2023]
Abstract
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.
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Affiliation(s)
- Metin N Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
| | - John Tomaszewski
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14214, USA
| | | | - Scott Doyle
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14214, USA
| | - Alan Ruttenberg
- School of Dental Medicine, University at Buffalo, Buffalo NY, 14214, USA
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY 14260, USA
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Oberkampf H, Zillner S, Overton JA, Bauer B, Cavallaro A, Uder M, Hammon M. Semantic representation of reported measurements in radiology. BMC Med Inform Decis Mak 2016; 16:5. [PMID: 26801764 PMCID: PMC4722630 DOI: 10.1186/s12911-016-0248-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 01/20/2016] [Indexed: 12/23/2022] Open
Abstract
Background In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. Methods We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. Results The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. Conclusions The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements.
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Affiliation(s)
- Heiner Oberkampf
- Department of Computer Science, Software Methodologies for Distributed Systems, University of Augsburg, Universitätsstraße 6a, 86159, Augsburg, Germany. .,Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, 81739, Münech, Germany.
| | - Sonja Zillner
- Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, 81739, Münech, Germany. .,School of International Business and Entrepreneurship, Steinbeis University, Kalkofenstraße 53, 71083, Herrenberg, Germany.
| | | | - Bernhard Bauer
- Department of Computer Science, Software Methodologies for Distributed Systems, University of Augsburg, Universitätsstraße 6a, 86159, Augsburg, Germany.
| | - Alexander Cavallaro
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054, Erlangen, Germany.
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054, Erlangen, Germany.
| | - Matthias Hammon
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 1, 91054, Erlangen, Germany.
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Turner JA, Pasquerello D, Turner MD, Keator DB, Alpert K, King M, Landis D, Calhoun VD, Potkin SG, Tallis M, Ambite JL, Wang L. Terminology development towards harmonizing multiple clinical neuroimaging research repositories. DATA INTEGRATION IN THE LIFE SCIENCES : ... INTERNATIONAL WORKSHOP, DILS ... : PROCEEDINGS. DILS (CONFERENCE) 2015; 9162:104-117. [PMID: 26688838 PMCID: PMC4682911 DOI: 10.1007/978-3-319-21843-4_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Data sharing and mediation across disparate neuroimaging repositories requires extensive effort to ensure that the different domains of data types are referred to by commonly agreed upon terms. Within the SchizConnect project, which enables querying across decentralized databases of neuroimaging, clinical, and cognitive data from various studies of schizophrenia, we developed a model for each data domain, identified common usable terms that could be agreed upon across the repositories, and linked them to standard ontological terms where possible. We had the goal of facilitating both the current user experience in querying and future automated computations and reasoning regarding the data. We found that existing terminologies are incomplete for these purposes, even with the history of neuroimaging data sharing in the field; and we provide a model for efforts focused on querying multiple clinical neuroimaging repositories.
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Affiliation(s)
- Jessica A. Turner
- Georgia State University, Atlanta, Georgia, USA
- Mind Research Network, Albuquerque, New Mexico, USA
| | | | | | | | | | | | - Drew Landis
- Mind Research Network, Albuquerque, New Mexico, USA
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, New Mexico, USA
- University of New Mexico, Albuquerque, New Mexico, USA
| | | | - Marcelo Tallis
- University of Southern California, Los Angeles, California, USA
| | | | - Lei Wang
- Northwestern University, Chicago, Illinois, USA
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Smith B, Arabandi S, Brochhausen M, Calhoun M, Ciccarese P, Doyle S, Gibaud B, Goldberg I, Kahn CE, Overton J, Tomaszewski J, Gurcan M. Biomedical imaging ontologies: A survey and proposal for future work. J Pathol Inform 2015; 6:37. [PMID: 26167381 PMCID: PMC4485195 DOI: 10.4103/2153-3539.159214] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 04/30/2015] [Indexed: 12/24/2022] Open
Abstract
Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.
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Affiliation(s)
- Barry Smith
- Department of Philosophy, The State University of New York at Buffalo, Buffalo, NY 14260, USA
| | | | - Mathias Brochhausen
- Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Michael Calhoun
- Department of Health and Human Performance, Elon University, Elon, NC 27244, USA
| | - Paolo Ciccarese
- Harvard Medical School, Massachusetts General Hospital, PerkinElmer Innovation Labs, Boston, MA 02115, USA
| | - Scott Doyle
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Bernard Gibaud
- Laboratoire du Traitement du Signal et de l'Image (LTSI), Inserm Unit 1099, University of Rennes 1, Rennes, France
| | - Ilya Goldberg
- National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Charles E Kahn
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - John Tomaszewski
- Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
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