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A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database

Journal of Pathology and Translational Medicine 2020년 54권 6호 p.462 ~ 470
정요셉, 이지영, 김예진, 최진균, 유환조, 박경신, 조미원, Thakur Nishant,
소속 상세정보
정요셉 ( Chong Yo-Sep ) - Catholic University College of Medicine Department of Hospital Pathology
이지영 ( Lee Ji-Young ) - Catholic University College of Medicine Department of Hospital Pathology
김예진 ( Kim Ye-Jin ) - POSTECH Department of Creative Information Technology
최진균 ( Choi Jin-Gyun ) - POSTECH Computer Science and Engineering
유환조 ( Yu Hwan-Jo ) - POSTECH Computer Science and Engineering
박경신 ( Park Gyeong-Sin ) - Catholic University College of Medicine Department of Hospital Pathology
조미원 ( Cho Mee-Yon ) - Yonsei University Wonju College of Medicine Department of Pathology
 ( Thakur Nishant ) - Catholic University College of Medicine Department of Hospital Pathology

Abstract


Background: Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.

Methods: A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.

Results: IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.

Conclusions: Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease-specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

키워드

Database; Expert-supporting system; Machine learning; Immunohistochemistry; Probabilistic decision tree

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