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

Shin D, Arthur G, Caldwell C, Popescu M, Petruc M, Diaz-Arias A, Shyu CR - J Pathol Inform (2012)

Bottom Line: 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.The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology and Anatomical Sciences, University of Missouri.

ABSTRACT

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.

No MeSH data available.


Interpathologist variability (two experiments)
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Figure 10: Interpathologist variability (two experiments)

Mentions: To measure an interpathology variability, we calculated an average difference between numbers of antibody tests required to diagnose the same disease by different pathologists participated in our research. We then constructed histograms that show the frequency for each such difference [Figure 10]. For example, from the top panel of Figure 10 we can see that there were three diseases for which pathologists used the same number of tests, five diseases when the number of tests differed on average by one, one disease when the number of tests differed by 3, six diseases when the number of tests differed by four tests, and so on. The bars appearing on the left side of the histogram correspond to lower variability, while the bars on the right side to the higher variability. We repeated the experiment and plotted another histogram, which is shown in the bottom panel of Figure 10. It can be seen from the chart that the difference in the number of tests in some cases can reach 10 tests.


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

Shin D, Arthur G, Caldwell C, Popescu M, Petruc M, Diaz-Arias A, Shyu CR - J Pathol Inform (2012)

Interpathologist variability (two experiments)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3307231&req=5

Figure 10: Interpathologist variability (two experiments)
Mentions: To measure an interpathology variability, we calculated an average difference between numbers of antibody tests required to diagnose the same disease by different pathologists participated in our research. We then constructed histograms that show the frequency for each such difference [Figure 10]. For example, from the top panel of Figure 10 we can see that there were three diseases for which pathologists used the same number of tests, five diseases when the number of tests differed on average by one, one disease when the number of tests differed by 3, six diseases when the number of tests differed by four tests, and so on. The bars appearing on the left side of the histogram correspond to lower variability, while the bars on the right side to the higher variability. We repeated the experiment and plotted another histogram, which is shown in the bottom panel of Figure 10. It can be seen from the chart that the difference in the number of tests in some cases can reach 10 tests.

Bottom Line: 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.The method uses extensions of Shannon's information entropies and Bayesian probabilities to dynamically build an efficient diagnostic tree.

View Article: PubMed Central - PubMed

Affiliation: Department of Pathology and Anatomical Sciences, University of Missouri.

ABSTRACT

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.

No MeSH data available.