Ontology-based Support for

Brain Tumour Study

Subhashis Das

MSLIS 2011-13

DRTC, Indian Statistical Institute

Presentation Structure

Introduction and background

Problems of current informaton retrieval systems

Why I chose Ontology

Ontology building method

Use

Conclusion

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

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

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

Problems in IR

Current information retrieval systems mostly Keyword search, Low precision. Junk retrieval

So what is the solution?

Ontology based information system

What an ontology is and is not?

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

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

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

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

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.

Other benefits

Eliminating redundant effort

Significant head-start

Interoperability with other ontologies

Community acceptance

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

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

Analysis and synthesis

The formal terms collected during the previous phase are analyzed per genus.

With the synthesis, formal terms are arrange into facets

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 MEDLINEMore than 1,77,000 entry

Sources: http://www.ncbi.nlm.nih.gov/mesh and http://viw2.vetmed.vt.edu/sct/menu.cfm

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

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

 

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

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.

Environmental causes

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.

Demo

Now I show you how I build ontology using Protégé 4.1 ontology editor

Use

For physician If a medical practitioner queries the system, she/he will mainly be

interested in

Symptoms

Possible treatment

Use

When a physician cannot identify disease.

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).

Limitation of ontology model

Assertion errors

Relevance errors

Encoding errors

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.

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.