Identifying Breast Cancer Concepts in SNOMED-CT Using Large Text Corpus

2011 ◽  
pp. 27-34
Author(s):  
Zharko Aleksovski ◽  
Merlijn Sevenster
Keyword(s):  
Breast Cancer ◽  
Snomed Ct ◽  
Text Corpus

Toward early diagnosis decision support for breast cancer: Ontology-based semantic interoperability.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18072-e18072 ◽  
Author(s):  
Mengchun Gong ◽  
Zhi Wang ◽  
Yefu Liu ◽  
Hong Zhou ◽  
Fang Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Cancer Diagnosis ◽  
Laboratory Tests ◽  
Treatment Guidelines ◽  
Breast Cancer Diagnosis ◽  
Clinical Findings ◽  
Semantic Interoperability ◽  
Snomed Ct ◽  
Imaging Results

e18072 Background: Breast cancer is ranked as the most common cancer among women in China, cases in China account for about 300,000 of all newly diagnosed breast cancers each year. Developing Clinical Decision Supporting System (CDSS) to assist early diagnosis breast cancer is valuable, however, lack of semantic interoperability in CDSS has been identified as the main obstacle for broad adoption of CDSS. Ontology is considered to be one of the effective approaches to bridge the terminology gap between various clinical systems and data sources. In this study, we describe our efforts in transforming breast cancer clinical knowledge into computable ontology to support breast cancer early diagnosis. Methods: We have built a breast cancer ontology (BCO) to support our CDSS pipeline. A number of NCCN clinical practice guidelines in Oncology for Breast Cancer and the breast cancer diagnosis and treatment guidelines of China are the main knowledge sources of the BCO. BCO is a manually curated resource that contains concepts and relations of breast cancer clinical findings, demographics, laboratory tests, imaging results, treatments, pathologies, cancer stages, body structure and follow-up information. These curated knowledges are annotated through interoperable standard vocabulary SNOMED CT. Two physicians reviewed and evaluated the BCO. Results: Concepts in BCO currently contains 79 clinical findings, 8 demographics, 42 laboratory tests, 105 imaging results, 332 treatments, 141 pathology and cancer stages, 30 body structures and 3 follow-ups. Relations are defined such as finding site, associated with, etc. The concepts hierarchies and relationships were built by using Protégé to support OWL representation. As an initial evaluation results, most of concepts could be mapped to SNOMED CT, but there are some concepts could not be exactly mapped to SNOMED CT. We created local Chinese Vocabularies for these local terms. Conclusions: Based on interoperable BCO, a group of computable rules are developed by adding statements according to early diagnosis criteria of breast cancer. Diagnosis assistance around complex patients is supported though connecting the Electronic Health Record (EHR). This is an ongoing development that the BCO is continuingly enriched with standard concepts, relations and integrate with individual instances to support broader adoptability.

Semantic Integration of Patient Data for Clinical Decision Support in Breast Cancer Care

2013 ◽  
pp. 250-267
Author(s):  
Leonardo Lezcano ◽  
Miguel-Ángel Sicilia ◽  
Eydel Rivero
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Clinical Guidelines ◽  
Clinical Decision ◽  
Semantic Integration ◽  
Semantic Interoperability ◽  
Snomed Ct ◽  
Life Threatening ◽  
Delays In Diagnosis ◽  
Clinical Terminologies

Achieving semantic interoperability between heterogeneous healthcare systems and integrating clinical guidelines in the automatic decision support of healthcare institutions are two key priorities of current medical informatics. They can lead to a significant improvement on patient safety by reducing medical risks and delays in diagnosis, facilitating continuity of care and preventing life threatening adverse events. The present chapter describes a project that addresses those two priorities in the field of Breast Cancer for which effective clinical guidelines are available, as well as the clinical data to apply them. However, the deployment of semantic interoperability techniques based on clinical terminologies such as SNOMED-CT and EHR exchange models such as openEHR and HL7 is required to meaningfully combine the available data. Then data mining techniques are capable of automatically adapting the parameters of clinical guidelines to the particular conditions of each healthcare environment.

scholarly journals Multicenter Breast Cancer Collaborative Registry

2011 ◽  
Vol 10 ◽  
pp. CIN.S7845 ◽  
Author(s):  
Simon Sherman ◽  
Oleg Shats ◽  
Elizabeth Fleissner ◽  
George Bascom ◽  
Kevin Yiee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Research ◽  
Web Application ◽  
Biomedical Informatics ◽  
Controlled Vocabulary ◽  
Snomed Ct ◽  
Web Based ◽  
Systematized Nomenclature Of Medicine ◽  
Jsp Technology ◽  
Web Based System

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute's Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC).

scholarly journals Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822198939
Author(s):  
Euisung Jung ◽  
Hemant Jain ◽  
Atish P Sinha ◽  
Carmelo Gaudioso
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Text Mining ◽  
Language Processing ◽  
Snomed Ct ◽  
Lexical Resources ◽  
Named Entities ◽  
Domain Experts ◽  
Trial Subject

A natural language processing (NLP) application requires sophisticated lexical resources to support its processing goals. Different solutions, such as dictionary lookup and MetaMap, have been proposed in the healthcare informatics literature to identify disease terms with more than one word (multi-gram disease named entities). Although a lot of work has been done in the identification of protein- and gene-named entities in the biomedical field, not much research has been done on the recognition and resolution of terminologies in the clinical trial subject eligibility analysis. In this study, we develop a specialized lexicon for improving NLP and text mining analysis in the breast cancer domain, and evaluate it by comparing it with the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). We use a hybrid methodology, which combines the knowledge of domain experts, terms from multiple online dictionaries, and the mining of text from sample clinical trials. Use of our methodology introduces 4243 unique lexicon items, which increase bigram entity match by 38.6% and trigram entity match by 41%. Our lexicon, which adds a significant number of new terms, is very useful for matching patients to clinical trials automatically based on eligibility matching. Beyond clinical trial matching, the specialized lexicon developed in this study could serve as a foundation for future healthcare text mining applications.

scholarly journals A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC

2017 ◽  
Vol 25 (3) ◽  
pp. 259-266 ◽  
Author(s):  
Walter S Campbell ◽  
Daniel Karlsson ◽  
Daniel J Vreeman ◽  
Audrey J Lazenby ◽  
Geoffrey A Talmon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Information Exchange ◽  
Medical Center ◽  
Pathology Report ◽  
Snomed Ct ◽  
Development Organization ◽  
Health And Social Care ◽  
Data Elements ◽  
Synoptic Reporting

Abstract Background The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP. Methods Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska Lexicon© SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. Results UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank. Discussion The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.

scholarly journals SNOMED CT Annotation for Improved Pathological Decisions in Breast Cancer Domain

2019 ◽  
Vol 8 (3) ◽  
pp. 8400-8406
Keyword(s):  
Breast Cancer ◽  
Decision Support ◽  
Natural Language ◽  
Clinical Decision Support Systems ◽  
Pathology Report ◽  
Cancer Stage ◽  
Snomed Ct ◽  
Natural Language Text ◽  
Pathological Classification ◽  
Language Text

Breast cancer pathology reports are used in the diagnosis of the disease and determination of the stage of cancer in a patient. These reports are written or electronically generated by the Pathologist in English. The contents of a Pathology report generated by the Pathologist are usually in unstructured natural language form. The contents of a report are used to determine the Pathological classification and Cancer stage of a patient. Information extraction and making pathological decisions from natural language text is a complex process due to the heterogeneity of the report structure and its contents. The reports can be homogenized using the global annotation standard Systematized Nomenclature of Medicine – Clinical Terms, SNOMED-CT. It enables consistent representations of medical terms and can be used for clinical decision support systems (CDSS) and cancer reporting. SNOMED is a vast repository and its enormity and complexity necessitates extraction of a subset for a particular domain before using it for annotation. The annotation is performed either in online mode at the time of generation of the report or in offline mode on a batch of archived reports. A CDSS prototype is developed for breast cancer domain, which provides support to the Pathologist to determine the Pathological Classification and Cancer Staging on both natural language text and SNOMED-annotated text. With regard to Pathological decisions, a hypothesis is formulated that Annotation using SNOMED does not improve the system’s performance in determining the cancer stage of a patient. For annotating the text, the system initially extracts a SNOMED subset for the domain. Performance Analysis of the decision support processes was done by determining Precision, Recall, Specificity, Accuracy, F-measure and Error. The analysis indicates that the annotation feature improved the accuracy of automated Pathological decisions presented by the CDSS to the Clinician for finalizing his decisions. In the future, the CDSS feature can be applied to other cancer domains and thus provide a means to improve decision-making related to those domains.

Virus-Like Particles in a Human Breast Cancer: Structural Similarity to Previously Reported Particles

1973 ◽  
Vol 31 ◽  
pp. 378-379
Author(s):  
G. Kasnic ◽  
S. E. Stewart ◽  
C. Urbanski
Keyword(s):  
Breast Cancer ◽  
Biological Activity ◽  
Human Breast Cancer ◽  
Osteogenic Sarcoma ◽  
Human Tumor ◽  
Structural Similarity ◽  
Cell Tumor ◽  
Human Breast ◽  
Human Tumor Cell ◽  
Type Particle

We have reported the maturation of an intracisternal A-type particle in murine plasma cell tumor cultures and three human tumor cell cultures (rhabdomyosarcoma, lung adenocarcinoma, and osteogenic sarcoma) after IUDR-DMSO activation. In all of these studies the A-type particle seems to develop into a form with an electron dense nucleoid, presumably mature, which is also intracisternal. A similar intracisternal A-type particle has been described in leukemic guinea pigs. Although no biological activity has yet been demonstrated for these particles, on morphologic grounds, and by the manner in which they develop within the cell, they may represent members of the same family of viruses.

Further Ultrastructural Observations on Metastatic Nodules of Human Breast Cancer

1968 ◽  
Vol 26 ◽  
pp. 224-225
Author(s):  
John L. Swedo ◽  
R. W. Talley ◽  
John H. L. Watson
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Estrogen Therapy ◽  
Specimen Preparation ◽  
Human Breast ◽  
Autophagic Vacuoles ◽  
Previous Communication ◽  
Linear Profiles ◽  
Metastatic Nodule

Since the report, which described the ultrastructure of a metastatic nodule of human breast cancer after estrogen therapy, additional ultrastructural observations, including some which are correlative with pertinent findings in the literature concerning mycoplasmas, have been recorded concerning the same subject. Specimen preparation was identical to that in.The mitochondria possessed few cristae, and were deteriorated and vacuolated. They often contained particulates and fibrous structures, sometimes arranged in spindle-shaped bundles, Fig. 1. Another apparent aberration was the occurrence, Fig. 2 (arrows) of linear profiles of what seems to be SER, which lie between layers of RER, and are often recognizably continuous with them.It was noted that the structure of the round bodies, interpreted as within autophagic vacuoles in the previous communication, and of vesicular bodies, described morphologically closely resembled those of some mycoplasmas. Specifically, they simulated or reflected the various stages of replication reported for mycoplasmas grown on solid nutrient. Based on this observation, they are referred to here as “mycoplasma-like” structures, in anticipation of confirmatory evidence from investigations now in progress.

The proliferation of breast cancer cells are inhibited by the human intrabody targeting cyclinD1

2010 ◽  
Vol 34 (8) ◽  
pp. S49-S49
Author(s):  
Lei Wang ◽  
Xun Zhou ◽  
Lihong Zhou ◽  
Yong Chen ◽  
Xun Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells
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