Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-03-01
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| Series: | Diagnostic Pathology |
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| Online Access: | https://doi.org/10.1186/s13000-021-01081-8 |
| _version_ | 1818676863067750400 |
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| author | Yosep Chong Nishant Thakur Ji Young Lee Gyoyeon Hwang Myungjin Choi Yejin Kim Hwanjo Yu Mee Yon Cho |
| author_facet | Yosep Chong Nishant Thakur Ji Young Lee Gyoyeon Hwang Myungjin Choi Yejin Kim Hwanjo Yu Mee Yon Cho |
| author_sort | Yosep Chong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T08:50:14Z |
| format | Article |
| id | doaj.art-06f295b76bd145aa9e27ab1429640c3b |
| institution | Directory Open Access Journal |
| issn | 1746-1596 |
| language | English |
| last_indexed | 2024-12-17T08:50:14Z |
| publishDate | 2021-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic Pathology |
| spelling | doaj.art-06f295b76bd145aa9e27ab1429640c3b2022-12-21T21:56:06ZengBMCDiagnostic Pathology1746-15962021-03-011611910.1186/s13000-021-01081-8Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretationYosep Chong0Nishant Thakur1Ji Young Lee2Gyoyeon Hwang3Myungjin Choi4Yejin Kim5Hwanjo Yu6Mee Yon Cho7Department of Hospital Pathology, College of Medicine, The Catholic University of KoreaDepartment of Hospital Pathology, College of Medicine, The Catholic University of KoreaDepartment of Hospital Pathology, College of Medicine, The Catholic University of KoreaDepartment of Hospital Pathology, College of Medicine, The Catholic University of KoreaDasom X, Inc.Department of Creative Information Technology, POSTECHComputer Science and Engineering, POSTECHDepartment of Pathology, Yonsei University, Wonju College of MedicineAbstract 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.https://doi.org/10.1186/s13000-021-01081-8DatabaseExpert systemMachine learningImmunohistochemistryProbabilistic decision tree |
| spellingShingle | Yosep Chong Nishant Thakur Ji Young Lee Gyoyeon Hwang Myungjin Choi Yejin Kim Hwanjo Yu Mee Yon Cho Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation Diagnostic Pathology Database Expert system Machine learning Immunohistochemistry Probabilistic decision tree |
| title | Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation |
| title_full | Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation |
| title_fullStr | Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation |
| title_full_unstemmed | Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation |
| title_short | Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation |
| title_sort | diagnosis prediction of tumours of unknown origin using immunogenius a machine learning based expert system for immunohistochemistry profile interpretation |
| topic | Database Expert system Machine learning Immunohistochemistry Probabilistic decision tree |
| url | https://doi.org/10.1186/s13000-021-01081-8 |
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