Abstract
Abstract
Background
Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.
Methods
We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.
Results
We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.
Conclusion
The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.
Funder
National Research Foundation of Korea (NRF) funded by the Ministry of Education
partly funded by a research grant from the institute of Clinical Medicine Research, Catholic University of Korea, Yeouido St. Mary’s Hospital.
Po-Ca Networking Groups funded by the Postech-Catholic Biomedical Engineering Institute (PCBMI).
Publisher
Springer Science and Business Media LLC
Subject
General Medicine,Histology,Pathology and Forensic Medicine
Reference31 articles.
1. Elias JM. Immunohistochemistry : a brief historical perspective : commentary. Natick: Eaton Pub.; 2000.
2. Buchwalow IB, Böcker W. Immunohistochemistry : basics and methods. Heidelberg: Springer; 2010.
3. Matos LL, Trufelli DC, de Matos MG, da Silva Pinhal MA. Immunohistochemistry as an important tool in biomarkers detection and clinical practice. Biomark Insights. 2010;5:9–20.
4. Chu PG, Weiss LM. In: 2nd edn, editor. Modern immunohistochemistry. Cambridge: Cambridge University Press; 2014.
5. Dabbs DJ. Diagnostic immunohistochemistry : theranostic and genomic applications. 4th ed. Philadelphia: Elsevier/Saunders; 2014.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献