SlideShare a Scribd company logo
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
Ontology based support for brain tumour study
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 MEDLINE
More 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
Ontology based support for brain tumour study
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.
Ontology based support for brain tumour study
Ontology based support for brain tumour study

More Related Content

PDF
Tumor Detection Based On Symmetry Information
PDF
Treatment of Brain Metastases Using the Current Predictive Models: Is the Pro...
PDF
Aggressive Cancer
PPTX
Pathomics Based Biomarkers and Precision Medicine
PPTX
Digital pathology in developing country
PDF
EuroBioForum2014_speaker_Balling
DOCX
Physics
DOCX
Physics
Tumor Detection Based On Symmetry Information
Treatment of Brain Metastases Using the Current Predictive Models: Is the Pro...
Aggressive Cancer
Pathomics Based Biomarkers and Precision Medicine
Digital pathology in developing country
EuroBioForum2014_speaker_Balling
Physics
Physics

What's hot (13)

PDF
Ecc2012 13 4
PPTX
Artificial Intelligence in pathology
PDF
Trampleasure VR [P1]
PPTX
Approach to the patients with brain metastases
PDF
Segmentation of Diffusion Tensor Brain Tumor Images using Fuzzy C-Means Clust...
PDF
Twenty Years of Whole Slide Imaging - the Coming Phase Change
PDF
Description of Different Phases of Brain Tumor Classification
PPTX
It Is Time to Reevaluate the Management of Patients With Brain Metastases
PDF
Frontiers in Neuroscience Brochure 2015
PDF
Atlas of Regional ANATOMY of the Brain Using MRI
PDF
How computers can help to share understanding with patients
PDF
Vph2012 20 sept12_shublaq_final
PPTX
Brain tumour a brief study - medical information
Ecc2012 13 4
Artificial Intelligence in pathology
Trampleasure VR [P1]
Approach to the patients with brain metastases
Segmentation of Diffusion Tensor Brain Tumor Images using Fuzzy C-Means Clust...
Twenty Years of Whole Slide Imaging - the Coming Phase Change
Description of Different Phases of Brain Tumor Classification
It Is Time to Reevaluate the Management of Patients With Brain Metastases
Frontiers in Neuroscience Brochure 2015
Atlas of Regional ANATOMY of the Brain Using MRI
How computers can help to share understanding with patients
Vph2012 20 sept12_shublaq_final
Brain tumour a brief study - medical information
Ad

Viewers also liked (10)

PPT
42925901 brain-tumor
PPT
Tumour detection
PDF
Jashapara RKM-2016 - Competency model in knowledge management
PPTX
Cuckoo Optimization ppt
PPT
Lecture 2 Analysis KM
PPTX
Neural Network Based Brain Tumor Detection using MR Images
PPTX
Imaging in pediatric brain tumors
ODP
Brain Tumor And Its Types
PPTX
Tumores Cerebrais / Sistema Nervoso Central
PPTX
MRI Procedure of Brain
42925901 brain-tumor
Tumour detection
Jashapara RKM-2016 - Competency model in knowledge management
Cuckoo Optimization ppt
Lecture 2 Analysis KM
Neural Network Based Brain Tumor Detection using MR Images
Imaging in pediatric brain tumors
Brain Tumor And Its Types
Tumores Cerebrais / Sistema Nervoso Central
MRI Procedure of Brain
Ad

Similar to Ontology based support for brain tumour study (20)

PPT
Bio ontology drtc-seminar_anwesha
PPTX
Brain Cancer ISU
PPTX
GA4GH Phenotype Ontologies Task team update
PPTX
The Translational Medicine Ontology: Driving personalized medicine by br...
PPTX
Imageprocessinginbraintumordetection 190316110830
PDF
Detection of location-specific intra-cranial brain tumors
PDF
Tumor Detection and Classification of MRI Brain Images using SVM and DNN
PDF
Hybrid Deep Convolutional Neural Network
PPTX
Haystack 2019 - Ontology and Oncology: NLP for Precision Medicine - Sean Mullane
PDF
Automatic detection and severity analysis of brain tumors using gui in matlab
PDF
Automatic detection and severity analysis of brain
PDF
BRAIN TUMOUR DETECTION AND CLASSIFICATION
PDF
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
PDF
IRJET- Brain Tumor Detection using Convolutional Neural Network
PPT
Simplifying semantics for biomedical applications
PDF
Enhanced_Watershed_Segmentation_Algorithm-Based_Mo.pdf
PDF
Automated Intracranial Neoplasm Detection Using Convolutional Neural Networks
PDF
12 a survey on mri brain image segmentation technique
PDF
Semantic decomposition of ontologies for creation of flexible biomedical conc...
PDF
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...
Bio ontology drtc-seminar_anwesha
Brain Cancer ISU
GA4GH Phenotype Ontologies Task team update
The Translational Medicine Ontology: Driving personalized medicine by br...
Imageprocessinginbraintumordetection 190316110830
Detection of location-specific intra-cranial brain tumors
Tumor Detection and Classification of MRI Brain Images using SVM and DNN
Hybrid Deep Convolutional Neural Network
Haystack 2019 - Ontology and Oncology: NLP for Precision Medicine - Sean Mullane
Automatic detection and severity analysis of brain tumors using gui in matlab
Automatic detection and severity analysis of brain
BRAIN TUMOUR DETECTION AND CLASSIFICATION
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
IRJET- Brain Tumor Detection using Convolutional Neural Network
Simplifying semantics for biomedical applications
Enhanced_Watershed_Segmentation_Algorithm-Based_Mo.pdf
Automated Intracranial Neoplasm Detection Using Convolutional Neural Networks
12 a survey on mri brain image segmentation technique
Semantic decomposition of ontologies for creation of flexible biomedical conc...
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...

Recently uploaded (20)

PPTX
Fundamentals of human energy transfer .pptx
PPTX
Acid Base Disorders educational power point.pptx
PPTX
POLYCYSTIC OVARIAN SYNDROME.pptx by Dr( med) Charles Amoateng
PPT
OPIOID ANALGESICS AND THEIR IMPLICATIONS
PPT
genitourinary-cancers_1.ppt Nursing care of clients with GU cancer
PPTX
Important Obstetric Emergency that must be recognised
PDF
Intl J Gynecology Obste - 2021 - Melamed - FIGO International Federation o...
PPTX
History and examination of abdomen, & pelvis .pptx
DOCX
NEET PG 2025 | Pharmacology Recall: 20 High-Yield Questions Simplified
PPTX
Pathophysiology And Clinical Features Of Peripheral Nervous System .pptx
PPTX
Neuropathic pain.ppt treatment managment
PPTX
neonatal infection(7392992y282939y5.pptx
PPT
Copy-Histopathology Practical by CMDA ESUTH CHAPTER(0) - Copy.ppt
PPT
ASRH Presentation for students and teachers 2770633.ppt
PPTX
Electromyography (EMG) in Physiotherapy: Principles, Procedure & Clinical App...
PPTX
JUVENILE NASOPHARYNGEAL ANGIOFIBROMA.pptx
PPTX
Slider: TOC sampling methods for cleaning validation
PPT
1b - INTRODUCTION TO EPIDEMIOLOGY (comm med).ppt
PPTX
Uterus anatomy embryology, and clinical aspects
PPTX
Gastroschisis- Clinical Overview 18112311
Fundamentals of human energy transfer .pptx
Acid Base Disorders educational power point.pptx
POLYCYSTIC OVARIAN SYNDROME.pptx by Dr( med) Charles Amoateng
OPIOID ANALGESICS AND THEIR IMPLICATIONS
genitourinary-cancers_1.ppt Nursing care of clients with GU cancer
Important Obstetric Emergency that must be recognised
Intl J Gynecology Obste - 2021 - Melamed - FIGO International Federation o...
History and examination of abdomen, & pelvis .pptx
NEET PG 2025 | Pharmacology Recall: 20 High-Yield Questions Simplified
Pathophysiology And Clinical Features Of Peripheral Nervous System .pptx
Neuropathic pain.ppt treatment managment
neonatal infection(7392992y282939y5.pptx
Copy-Histopathology Practical by CMDA ESUTH CHAPTER(0) - Copy.ppt
ASRH Presentation for students and teachers 2770633.ppt
Electromyography (EMG) in Physiotherapy: Principles, Procedure & Clinical App...
JUVENILE NASOPHARYNGEAL ANGIOFIBROMA.pptx
Slider: TOC sampling methods for cleaning validation
1b - INTRODUCTION TO EPIDEMIOLOGY (comm med).ppt
Uterus anatomy embryology, and clinical aspects
Gastroschisis- Clinical Overview 18112311

Ontology based support for brain tumour study

  • 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.
  • 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.