The document discusses building an ontology to support brain tumor study. It begins with background on brain tumors and issues with current information retrieval systems. The author then explains why ontology was chosen as a solution and the methodology used, which included identifying terminology, analyzing, synthesizing, and standardizing terms. The ontology structure with top-level branches for tumor types, symptoms, causes, and treatments is described. Potential uses for physicians and researchers are discussed.

1. Ontology-based Support
for
Brain Tumour Study
Subhashis Das
MSLIS 2011-13
DRTC, Indian Statistical Institute

2. Presentation Structure
 Introduction and background
 Problems of current informaton retrieval systems
 Why I chose Ontology
 Ontology building method
 Use
 Conclusion

3. Introduction
For the diagnosis and detection of brain tumour
Computer based diagnosis system proves to be helpful
Brain tumour is one of the most deadly diseases in India
It contributes significantly to morbidity
Poor prognosis

4. Background
 Diagnosis using MRI & MRS is the main way of detection
 Brain tumours remain an important cause of morbidity and mortality and
afflict a large percentage of the World population.
 In children over 1 year of age, brain tumours are the most common solid
malignancies that cause disease-related death.
MRI: Magnetic Resonance Imaging; MRS: Magnetic Resonance Scan

5. Why I chose brain tumours?
 Clinical importance
 Important cause of morbidity and mortality in adults
and children
 Few improvements in outcome
 New approaches to management needed via greater
understanding

6. Problems in IR
 Current information retrieval systems mostly
 Keyword search,
 Low precision.
 Junk retrieval

8. So what is the solution?
 Ontology based information system

9. What an ontology is and is
not?

10. Rumours about ontologies
 Ontologies are overly publicised:
 “Ontology” is becoming a buzz word
 “Ontology” can “say” whatever one intends to say
 “Ontology” means inference
 “Ontology” is the ultimate solution for
interoperability

11. Ontology
 A common language/vocabulary/terminology for various
participants
 Formalised in an unambiguous representation
 For software agents, human experts, patients
 To assist in communication between humans and
computer
 To achieve interoperability
 To improve the design and quality of software systems

12. Ontology
 A “static” conceptualisation of the world
 “What is?” rather than “How does?”
 Allows reasoning which respects the translation of
concepts as sets of (possible) individuals
 Provides the underlying knowledge model for other
types of reasoning, e.g. Rule Based, Case Based etc.
 Ontology enhance the semantics of terms by providing
richer relationships between the terms of vocabulary

13. Why OWL (Web Ontology
Language)?
 Reasoning capability
 Subsumption relationship (is-a)
 Relies on necessary and sufficient definition of
concepts
 Good for maintaining a consistent ontology
 W3C standard
 Good support: existing systems and tools
 Good compatibility:
 Many ontologies are developed in owl or will be
translated into owl
 Good extensibility

14. Benefits
 The analysis and combination of the information the result will
be presented in a way that makes it easier for the user to have
an overview of the up-to-date knowledge about brain tumour.
 The inherited organization of ontologies adds taxonomical
context to search result making it easier for the research to
spot conceptual relationships in data.
 Any one can find relationship between different factors that
are responsible for brain tumour.

15. Other benefits
 Eliminating redundant effort
 Significant head-start
 Interoperability with other ontologies
 Community acceptance

16. Methodology
 Identification of the terminology
 Analysis
 Synthesis
 Standardization
 Ordering
Sources: Giunchiglia, Fausto; Dutta, Biswanath;Maltese, Vincenzo and Farazi, Feroz (2012): A facet-based
methodology for the construction of a large-scale geospatial ontology

17. Identification of the
terminology
 Information sources
 National Brain Tumor Society (http://www.braintumor.org)-USA)
 American Brain Tumor Association (http://www.abta.org/)
 Brain Tumor Foundation of Canada (http://www.braintumour.ca/)
 Brain Tumor Association of Western Australia
(http://braintumourwa.com)
 Resource pre-processing
 Mapping the resources
 Integration of the resources

18. Analysis and synthesis
 The formal terms collected during the previous
phase are analyzed per genus.
 With the synthesis, formal terms are arrange into
facets

19. Standardization
SNOMED CT®
Systematized Nomenclature of Medicine-Clinical Term (SNOMED CT)
 more than 311,000 active concepts with unique meanings and formal
logic-based definitions organized into 19 hierarchies.
Medical Subject Headings (MeSH)
The MeSH is a controlled vocabulary developed by the National Library
of Medicine (NLM) for indexing and retrieval of biomedical literature,
including MEDLINE
More than 1,77,000 entry
Sources: http://www.ncbi.nlm.nih.gov/mesh and http://viw2.vetmed.vt.edu/sct/menu.cfm

20. Principles of building brain
tumour ontology
The constructed brain tumour ontology has four main branches
Types- Describing different types of brain tumour
Symptoms- Describing symptoms of brain tumour
Causes- Causes responsible for brain tumour which are mainly
environmental and genetic
Treatments- Giving an overview of all treatments possible for that
particular type of brain tumour

21. Types
 Primary tumors of the brain
 Gliomas
 Lowest grade tumors
 Lower grade malignancies
 Higher-grade malignancies
 Highest-grade malignancies
 Meningioma
 Primitive neuroectodermal tumors (PNET)
 Pituitary tumors
 Pineal Tumors
 Choroid plexus tumors
 Other, more benign primary tumors
 Tumors of nerves and/or nerve sheaths
 Cyst
 Other primary tumors, including skull base
 Primary Central Nervous System Lymphoma (PCNSL)
 Metastatic brain tumors and carcinomatous meningitis

23. Symptoms
 A new seizure in an adult
 Gradual loss of movement or sensation in an arm or leg
 Unsteadiness or imbalance, especially if it is associated with
headache
 Loss of vision in one or both eyes, especially if the vision loss is more
peripheral
 Double vision, especially if it is associated with headache
 Hearing loss with or without dizziness
 Speech difficulty of gradual onset
 Other symptoms may also include nausea or vomiting that is most
severe in the morning, confusion and disorientation, and memory
loss.
 The following symptoms are usually not caused by a brain tumor, but
may sometimes be:
 Headache
 A change in behavior

24. Genetic-Causes
 The ontology explain that brain tumour have different types which also further divided
into subtypes. Brain tumour is caused by causes which can be genetic or environmental.

25. Environmental causes

26. Treatment
 There is a corresponding symptoms of observable characteristics of an ill individual and
treatment possible for the disorder that can be chemotherapy, surgery, psychotherapy or
medication.

27. Demo
 Now I show you how I build ontology using Protégé 4.1 ontology editor

28. Use
 For physician
 If a medical practitioner queries the system, she/he will mainly be
interested in
 Symptoms
 Possible treatment

29. Use
 When a physician cannot identify disease.

30. Use
 For researchers
 Its helps on drug discovery
 Its directed or may allow researcher to narrow down the region of
interest on particular gene
 Neurofibromatosis 1 (NF1 gene),
 Neurofibromatosis 2 (NF2 gene),
 Turcots (APC gene),
 Gorlins (PTCH gene),
 Li-Fraumeni syndrome (TP53 gene).

31. Limitation of ontology model
 Assertion errors
 Relevance errors
 Encoding errors

32. Conclusions and future
work
 A computer-base brain tumour ontology support the works of
researcher in gathering information on brain tumour research
and allows user across the world to intelligently access new
scientific information much more quickly.
 Shared knowledge improves research efficiency and
effectiveness, as it helps to avoid unnecessary redundancy in
doing the same experiments.

33. Reference
 National Brain Tumor Society (http://www.braintumor.org)-USA
 American Brain Tumor Association (http://www.abta.org/)
 Brain Tumor foundation of Canada (http://www.braintumour.ca/)
 Brain Tumor Association of Western Australia
(http://braintumourwa.com)
 Snomed-CT ( http://www.ihtsdo.org/snomed-ct/)
 Medical Subject Headings
(http://www.nlm.nih.gov/pubs/factsheets/mesh.html)
 Hadzic, Maja and Chang, Elizabeth (2005): Ontology-based support
for human disease study. IEEE, 2005, pp.1-7.