Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

• Published in: Diagnostic pathology Vol. 16; no. 1; p. 19
• Main Authors: Chong, Yosep, Thakur, Nishant, Lee, Ji Young, Hwang, Gyoyeon, Choi, Myungjin, Kim, Yejin, Yu, Hwanjo, Cho, Mee Yon
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11-03-2021; BioMed Central; BMC
• Subjects: Algorithms; Antibodies; Applications programs; Bayesian analysis; Data mining; Diagnosis; Differential diagnosis; Disease; Expert system; Expert systems; Immunohistochemistry; Learning algorithms; Machine learning; Medical research; Metastasis; Mobile communications networks; Mobile computing; Mobile operating systems; Neoplasms; Probabilistic decision tree; Probability; Random variables; Software; Textbooks; Thyroid gland; Tumors; Viral antibodies; Immunohistochemistry; Database; Probabilistic decision tree; Expert system; Machine learning
• ISSN: 1746-1596; 1746-1596
• DOI: 10.1186/s13000-021-01081-8

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.
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.
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.
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.