Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 472  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 


RESEARCH ARTICLE
Year : 2012  |  Volume : 3  |  Issue : 1  |  Page : 1

A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method


1 Department of Pathology and Anatomical Sciences; MU Informatics Institute, University of Missouri, USA
2 Department of Pathology and Anatomical Sciences; Ellis Fischel Cancer Center; MU Informatics Institute, University of Missouri, USA
3 Health Management and Informatics; MU Informatics Institute, University of Missouri, USA
4 MU Informatics Institute, University of Missouri, USA
5 Department of Pathology and Anatomical Sciences, University of Missouri, USA
6 MU Informatics Institute; Computer Science, University of Missouri, USA

Correspondence Address:
Dmitriy Shin
Department of Pathology and Anatomical Sciences; MU Informatics Institute, University of Missouri
USA
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.93393

Rights and Permissions

Background: Immunohistochemistry (IHC) is an important tool to identify and quantify expression of certain proteins (antigens) to gain insights into the molecular processes in a diseased tissue. However, it is a challenge for pathologists to remember the discriminative characteristics of the growing number of such antigens across multiple diseases. The complexity of their expression patterns, fueled by continuous discoveries in molecular pathology, gives rise to a combinatorial explosion that places an unprecedented burden on a practicing pathologist and therefore increases cost and variability of IHC studies. Materials and Methods: To tackle these issues, we have developed antibody test optimized selection method, a novel informatics tool to help pathologists in improving the IHC antibody selection process. The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree. Results: A comparative analysis of our method with the expert and World Health Organization classification guidelines showed that the proposed method brings threefold reduction in number of antibody tests required to reach a diagnostic conclusion. Conclusion: The developed method can significantly streamline the antibody test selection process, decrease associated costs and reduce inter- and intrapathologist variability in IHC decision-making.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed4910    
    Printed89    
    Emailed0    
    PDF Downloaded508    
    Comments [Add]    
    Cited by others 1    

Recommend this journal