J Pathol Transl Med.  2020 Nov;54(6):462-470. 10.4132/jptm.2020.07.11.

A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database

• Chong Y
• Lee JY
• Kim Y
• Choi J
• Yu H
• Park G
• Cho MY
• Thakur N

• KMID: 2509499
• DOI: http://doi.org/10.4132/jptm.2020.07.11

Abstract

Background
Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.
Methods
A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.
Results
IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.
Conclusions
Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Keyword

Database; Expert-supporting system; Machine learning; Immunohistochemistry; Probabilistic decision tree

Figure

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  Fig. 1. A probabilistic decision tree for a machine-learning algorithm in diagnostic tests and disease.

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  Fig. 2. Prior and post probability based on Bayes’ theorem.

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  Fig. 3. A screenshot of the mobile application “ImmunoGenius.”

Reference

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