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Open AccessArticle
Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System
by
Anca Iulia Neagu
Anca Iulia Neagu 1,2
,
Diana Gina Poalelungi
Diana Gina Poalelungi 1,3,*
,
Ana Fulga
Ana Fulga 1,3,
Marius Neagu
Marius Neagu 1,3,*
,
Iuliu Fulga
Iuliu Fulga 1,3 and
Aurel Nechita
Aurel Nechita 1,2
1
Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza St., 800010 Galati, Romania
2
Saint John Clinical Emergency Hospital for Children, 800487 Galati, Romania
3
Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei St., 800578 Galati, Romania
*
Authors to whom correspondence should be addressed.
Diagnostics 2024, 14(17), 1853; https://doi.org/10.3390/diagnostics14171853 (registering DOI)
Submission received: 13 June 2024
/
Revised: 26 July 2024
/
Accepted: 22 August 2024
/
Published: 24 August 2024
Abstract
Background: In recent decades, machine-learning (ML) technologies have advanced the management of high-dimensional and complex cancer data by developing reliable and user-friendly automated diagnostic tools for clinical applications. Immunohistochemistry (IHC) is an essential staining method that enables the identification of cellular origins by analyzing the expression of specific antigens within tissue samples. The aim of this study was to identify a model that could predict histopathological diagnoses based on specific immunohistochemical markers. Methods: The XGBoost learning model was applied, where the input variable (target variable) was the histopathological diagnosis and the predictors (independent variables influencing the target variable) were the immunohistochemical markers. Results: Our study demonstrated a precision rate of 85.97% within the dataset, indicating a high level of performance and suggesting that the model is generally reliable in producing accurate predictions. Conclusions: This study demonstrated the feasibility and clinical efficacy of utilizing the probabilistic decision tree algorithm to differentiate tumor diagnoses according to immunohistochemistry profiles.
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MDPI and ACS Style
Neagu, A.I.; Poalelungi, D.G.; Fulga, A.; Neagu, M.; Fulga, I.; Nechita, A.
Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System. Diagnostics 2024, 14, 1853.
https://doi.org/10.3390/diagnostics14171853
AMA Style
Neagu AI, Poalelungi DG, Fulga A, Neagu M, Fulga I, Nechita A.
Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System. Diagnostics. 2024; 14(17):1853.
https://doi.org/10.3390/diagnostics14171853
Chicago/Turabian Style
Neagu, Anca Iulia, Diana Gina Poalelungi, Ana Fulga, Marius Neagu, Iuliu Fulga, and Aurel Nechita.
2024. "Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System" Diagnostics 14, no. 17: 1853.
https://doi.org/10.3390/diagnostics14171853
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