Diagnosis Prediction of Tumours of Unknown Origin Using ImmunoGenius, A Machine Learning-based Expert System for Immunohistochemistry Profile Interpretation
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.
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This is a list of supplementary files associated with this preprint. Click to download.
Fig S1: The example of the IHC database
Fig S2: The example of the patients IHC profile dataset for training and validation of the diagnosis presumption algorithm.
Posted 15 Jan, 2021
Received 22 Jan, 2021
On 22 Jan, 2021
On 16 Jan, 2021
Received 12 Jan, 2021
On 09 Jan, 2021
Received 09 Jan, 2021
On 09 Jan, 2021
Received 09 Jan, 2021
On 06 Jan, 2021
Invitations sent on 05 Jan, 2021
On 04 Jan, 2021
On 04 Jan, 2021
On 04 Jan, 2021
On 04 Nov, 2020
Received 02 Nov, 2020
Received 02 Nov, 2020
Received 30 Oct, 2020
Received 28 Oct, 2020
Received 26 Oct, 2020
On 24 Oct, 2020
On 19 Oct, 2020
On 19 Oct, 2020
On 19 Oct, 2020
On 14 Oct, 2020
Invitations sent on 12 Oct, 2020
On 12 Aug, 2020
On 12 Aug, 2020
On 11 Aug, 2020
On 11 Aug, 2020
Diagnosis Prediction of Tumours of Unknown Origin Using ImmunoGenius, A Machine Learning-based Expert System for Immunohistochemistry Profile Interpretation
Posted 15 Jan, 2021
Received 22 Jan, 2021
On 22 Jan, 2021
On 16 Jan, 2021
Received 12 Jan, 2021
On 09 Jan, 2021
Received 09 Jan, 2021
On 09 Jan, 2021
Received 09 Jan, 2021
On 06 Jan, 2021
Invitations sent on 05 Jan, 2021
On 04 Jan, 2021
On 04 Jan, 2021
On 04 Jan, 2021
On 04 Nov, 2020
Received 02 Nov, 2020
Received 02 Nov, 2020
Received 30 Oct, 2020
Received 28 Oct, 2020
Received 26 Oct, 2020
On 24 Oct, 2020
On 19 Oct, 2020
On 19 Oct, 2020
On 19 Oct, 2020
On 14 Oct, 2020
Invitations sent on 12 Oct, 2020
On 12 Aug, 2020
On 12 Aug, 2020
On 11 Aug, 2020
On 11 Aug, 2020
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.
Figure 1
Figure 2
Figure 3
Figure 4