Automatic extraction of candidate nomenclature terms using the doublet method

BACKGROUND: New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature. RESULTS: A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature. CONCLUSION: The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article

Tumor taxonomy for the developmental lineage classification of neoplasms

BACKGROUND: The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. METHODS: The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. RESULTS: The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. CONCLUSIONS: This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article

Classifying the precancers: A metadata approach

BACKGROUND: During carcinogenesis, precancers are the morphologically identifiable lesions that precede invasive cancers. In theory, the successful treatment of precancers would result in the eradication of most human cancers. Despite the importance of these lesions, there has been no effort to list and classify all of the precancers. The purpose of this study is to describe the first comprehensive taxonomy and classification of the precancers. As a novel approach to disease classification, terms and classes were annotated with metadata (data that describes the data) so that the classification could be used to link precancer terms to data elements in other biological databases. METHODS: Terms in the UMLS (Unified Medical Language System) related to precancers were extracted. Extracted terms were reviewed and additional terms added. Each precancer was assigned one of six general classes. The entire classification was assembled as an XML (eXtensible Mark-up Language) file. A Perl script converted the XML file into a browser-viewable HTML (HyperText Mark-up Language) file. RESULTS: The classification contained 4700 precancer terms, 568 distinct precancer concepts and six precancer classes: 1) Acquired microscopic precancers; 2) acquired large lesions with microscopic atypia; 3) Precursor lesions occurring with inherited hyperplastic syndromes that progress to cancer; 4) Acquired diffuse hyperplasias and diffuse metaplasias; 5) Currently unclassified entities; and 6) Superclass and modifiers. CONCLUSION: This work represents the first attempt to create a comprehensive listing of the precancers, the first attempt to classify precancers by their biological properties and the first attempt to create a pathologic classification of precancers using standard metadata (XML). The classification is placed in the public domain, and comment is invited by the authors, who are prepared to curate and modify the classification

The development of common data elements for a multi-institute prostate cancer tissue bank: The Cooperative Prostate Cancer Tissue Resource (CPCTR) experience

BACKGROUND: The Cooperative Prostate Cancer Tissue Resource (CPCTR) is a consortium of four geographically dispersed institutions that are funded by the U.S. National Cancer Institute (NCI) to provide clinically annotated prostate cancer tissue samples to researchers. To facilitate this effort, it was critical to arrive at agreed upon common data elements (CDEs) that could be used to collect demographic, pathologic, treatment and clinical outcome data. METHODS: The CPCTR investigators convened a CDE curation subcommittee to develop and implement CDEs for the annotation of collected prostate tissues. The draft CDEs were refined and progressively annotated to make them ISO 11179 compliant. The CDEs were implemented in the CPCTR database and tested using software query tools developed by the investigators. RESULTS: By collaborative consensus the CPCTR CDE subcommittee developed 145 data elements to annotate the tissue samples collected. These included for each case: 1) demographic data, 2) clinical history, 3) pathology specimen level elements to describe the staging, grading and other characteristics of individual surgical pathology cases, 4) tissue block level annotation critical to managing a virtual inventory of cases and facilitating case selection, and 5) clinical outcome data including treatment, recurrence and vital status. These elements have been used successfully to respond to over 60 requests by end-users for tissue, including paraffin blocks from cases with 5 to 10 years of follow up, tissue microarrays (TMAs), as well as frozen tissue collected prospectively for genomic profiling and genetic studies. The CPCTR CDEs have been fully implemented in two major tissue banks and have been shared with dozens of other tissue banking efforts. CONCLUSION: The freely available CDEs developed by the CPCTR are robust, based on "best practices" for tissue resources, and are ISO 11179 compliant. The process for CDE development described in this manuscript provides a framework model for other organ sites and has been used as a model for breast and melanoma tissue banking efforts

The tissue microarray data exchange specification: A community-based, open source tool for sharing tissue microarray data

BACKGROUND: Tissue Microarrays (TMAs) allow researchers to examine hundreds of small tissue samples on a single glass slide. The information held in a single TMA slide may easily involve Gigabytes of data. To benefit from TMA technology, the scientific community needs an open source TMA data exchange specification that will convey all of the data in a TMA experiment in a format that is understandable to both humans and computers. A data exchange specification for TMAs allows researchers to submit their data to journals and to public data repositories and to share or merge data from different laboratories. In May 2001, the Association of Pathology Informatics (API) hosted the first in a series of four workshops, co-sponsored by the National Cancer Institute, to develop an open, community-supported TMA data exchange specification. METHODS: A draft tissue microarray data exchange specification was developed through workshop meetings. The first workshop confirmed community support for the effort and urged the creation of an open XML-based specification. This was to evolve in steps with approval for each step coming from the stakeholders in the user community during open workshops. By the fourth workshop, held October, 2002, a set of Common Data Elements (CDEs) was established as well as a basic strategy for organizing TMA data in self-describing XML documents. RESULTS: The TMA data exchange specification is a well-formed XML document with four required sections: 1) Header, containing the specification Dublin Core identifiers, 2) Block, describing the paraffin-embedded array of tissues, 3)Slide, describing the glass slides produced from the Block, and 4) Core, containing all data related to the individual tissue samples contained in the array. Eighty CDEs, conforming to the ISO-11179 specification for data elements constitute XML tags used in the TMA data exchange specification. A set of six simple semantic rules describe the complete data exchange specification. Anyone using the data exchange specification can validate their TMA files using a software implementation written in Perl and distributed as a supplemental file with this publication. CONCLUSION: The TMA data exchange specification is now available in a draft form with community-approved Common Data Elements and a community-approved general file format and data structure. The specification can be freely used by the scientific community. Efforts sponsored by the Association for Pathology Informatics to refine the draft TMA data exchange specification are expected to continue for at least two more years. The interested public is invited to participate in these open efforts. Information on future workshops will be posted at (API we site)

An informatics model for tissue banks – Lessons learned from the Cooperative Prostate Cancer Tissue Resource

BACKGROUND: Advances in molecular biology and growing requirements from biomarker validation studies have generated a need for tissue banks to provide quality-controlled tissue samples with standardized clinical annotation. The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) is a distributed tissue bank that comprises four academic centers and provides thousands of clinically annotated prostate cancer specimens to researchers. Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control. METHODS: Data managers review the medical records to collect and continuously update information for the 145 clinical, pathological and inventorial CDEs that the Resource maintains for each case. An Access-based data entry tool provides de-identification and a standard communication mechanism between each group and a central CPCTR database. Standardized automated quality control audits have been implemented. Centrally, an Oracle database has web interfaces allowing multiple user-types, including the general public, to mine de-identified information from all of the sites with three levels of specificity and granularity as well as to request tissues through a formal letter of intent. RESULTS: Since July 2003, CPCTR has offered over 6,000 cases (38,000 blocks) of highly characterized prostate cancer biospecimens, including several tissue microarrays (TMA). The Resource developed a website with interfaces for the general public as well as researchers and internal members. These user groups have utilized the web-tools for public query of summary data on the cases that were available, to prepare requests, and to receive tissues. As of December 2005, the Resource received over 130 tissue requests, of which 45 have been reviewed, approved and filled. Additionally, the Resource implemented the TMA Data Exchange Specification in its TMA program and created a computer program for calculating PSA recurrence. CONCLUSION: Building a biorepository infrastructure that meets today's research needs involves time and input of many individuals from diverse disciplines. The CPCTR can provide large volumes of carefully annotated prostate tissue for research initiatives such as Specialized Programs of Research Excellence (SPOREs) and for biomarker validation studies and its experience can help development of collaborative, large scale, virtual tissue banks in other organ systems

Modern classification of neoplasms: reconciling differences between morphologic and molecular approaches

BACKGROUND: For over 150 years, pathologists have relied on histomorphology to classify and diagnose neoplasms. Their success has been stunning, permitting the accurate diagnosis of thousands of different types of neoplasms using only a microscope and a trained eye. In the past two decades, cancer genomics has challenged the supremacy of histomorphology by identifying genetic alterations shared by morphologically diverse tumors and by finding genetic features that distinguish subgroups of morphologically homogeneous tumors. DISCUSSION: The Developmental Lineage Classification and Taxonomy of Neoplasms groups neoplasms by their embryologic origin. The putative value of this classification is based on the expectation that tumors of a common developmental lineage will share common metabolic pathways and common responses to drugs that target these pathways. The purpose of this manuscript is to show that grouping tumors according to their developmental lineage can reconcile certain fundamental discrepancies resulting from morphologic and molecular approaches to neoplasm classification. In this study, six issues in tumor classification are described that exemplify the growing rift between morphologic and molecular approaches to tumor classification: 1) the morphologic separation between epithelial and non-epithelial tumors; 2) the grouping of tumors based on shared cellular functions; 3) the distinction between germ cell tumors and pluripotent tumors of non-germ cell origin; 4) the distinction between tumors that have lost their differentiation and tumors that arise from uncommitted stem cells; 5) the molecular properties shared by morphologically disparate tumors that have a common developmental lineage, and 6) the problem of re-classifying morphologically identical but clinically distinct subsets of tumors. The discussion of these issues in the context of describing different methods of tumor classification is intended to underscore the clinical value of a robust tumor classification. SUMMARY: A classification of neoplasms should guide the rational design and selection of a new generation of cancer medications targeted to metabolic pathways. Without a scientifically sound neoplasm classification, biological measurements on individual tumor samples cannot be generalized to class-related tumors, and constitutive properties common to a class of tumors cannot be distinguished from uninformative data in complex and chaotic biological systems. This paper discusses the importance of biological classification and examines several different approaches to the specific problem of tumor classification

Tumor classification: molecular analysis meets Aristotle

BACKGROUND: Traditionally, tumors have been classified by their morphologic appearances. Unfortunately, tumors with similar histologic features often follow different clinical courses or respond differently to chemotherapy. Limitations in the clinical utility of morphology-based tumor classifications have prompted a search for a new tumor classification based on molecular analysis. Gene expression array data and proteomic data from tumor samples will provide complex data that is unobtainable from morphologic examination alone. The growing question facing cancer researchers is, "How can we successfully integrate the molecular, morphologic and clinical characteristics of human cancer to produce a helpful tumor classification?" DISCUSSION: Current efforts to classify cancers based on molecular features ignore lessons learned from millennia of experience in biological classification. A tumor classification must include every type of tumor and must provide a unique place for each tumor within the classification. Groups within a classification inherit the properties of their ancestors and impart properties to their descendants. A classification was prepared grouping tumors according to their histogenetic development. The classification is simple (reducing the complexity of information received from the molecular analysis of tumors), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. The clinical and research value of this historical approach to tumor classification is discussed. SUMMARY: This manuscript reviews tumor classification and provides a new and comprehensive classification for neoplasia that preserves traditional nomenclature while incorporating information derived from the molecular analysis of tumors. The classification is provided as an open access XML document that can be used by cancer researchers to relate tumor classes with heterogeneous experimental and clinical tumor databases

Availability and quality of paraffin blocks identified in pathology archives: A multi-institutional study by the Shared Pathology Informatics Network (SPIN)

BACKGROUND: Shared Pathology Informatics Network (SPIN) is a tissue resource initiative that utilizes clinical reports of the vast amount of paraffin-embedded tissues routinely stored by medical centers. SPIN has an informatics component (sending tissue-related queries to multiple institutions via the internet) and a service component (providing histopathologically annotated tissue specimens for medical research). This paper examines if tissue blocks, identified by localized computer searches at participating institutions, can be retrieved in adequate quantity and quality to support medical researchers. METHODS: Four centers evaluated pathology reports (1990–2005) for common and rare tumors to determine the percentage of cases where suitable tissue blocks with tumor were available. Each site generated a list of 100 common tumor cases (25 cases each of breast adenocarcinoma, colonic adenocarcinoma, lung squamous carcinoma, and prostate adenocarcinoma) and 100 rare tumor cases (25 cases each of adrenal cortical carcinoma, gastro-intestinal stromal tumor [GIST], adenoid cystic carcinoma, and mycosis fungoides) using a combination of Tumor Registry, laboratory information system (LIS) and/or SPIN-related tools. Pathologists identified the slides/blocks with tumor and noted first 3 slides with largest tumor and availability of the corresponding block. RESULTS: Common tumors cases (n = 400), the institutional retrieval rates (all blocks) were 83% (A), 95% (B), 80% (C), and 98% (D). Retrieval rate (tumor blocks) from all centers for common tumors was 73% with mean largest tumor size of 1.49 cm; retrieval (tumor blocks) was highest-lung (84%) and lowest-prostate (54%). Rare tumors cases (n = 400), each institution's retrieval rates (all blocks) were 78% (A), 73% (B), 67% (C), and 84% (D). Retrieval rate (tumor blocks) from all centers for rare tumors was 66% with mean largest tumor size of 1.56 cm; retrieval (tumor blocks) was highest for GIST (72%) and lowest for adenoid cystic carcinoma (58%). CONCLUSION: Assessment shows availability and quality of archival tissue blocks that are retrievable and associated electronic data that can be of value for researchers. This study serves to compliment the data from which uniform use of the SPIN query tools by all four centers will be measured to assure and highlight the usefulness of archival material for obtaining tumor tissues for research