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Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms

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Author(s)
Jamshid Abdul-GhafarKyung Jin SeoHye-Ra JungGyeongsin ParkSeung-Sook LeeYosep Chong
Keimyung Author(s)
Jung, Hye Ra
Department
Dept. of Pathology (병리학)
Journal Title
Diagnostics (Basel)
Issued Date
2023
Volume
13
Issue
7
Keyword
databaseexpert supporting systemimmunohistochemistrymachine learningprobabilistic decision tree
Abstract
Background:
Differential diagnosis using immunohistochemistry (IHC) panels is a crucial step in the pathological diagnosis of hematolymphoid neoplasms. In this study, we evaluated the prediction accuracy of the ImmunoGenius software using nationwide data to validate its clinical utility.

Methods:
We collected pathologically confirmed lymphoid neoplasms and their corresponding IHC results from 25 major university hospitals in Korea between 2015 and 2016. We tested ImmunoGenius using these real IHC panel data and compared the precision hit rate with previously reported diagnoses.

Results:
We enrolled 3052 cases of lymphoid neoplasms with an average of 8.3 IHC results. The precision hit rate was 84.5% for these cases, whereas it was 95.0% for 984 in-house cases.

Discussion:
ImmunoGenius showed excellent results in most B-cell lymphomas and generally showed equivalent performance in T-cell lymphomas. The primary reasons for inaccurate precision were atypical IHC profiles of certain cases, lack of disease-specific markers, and overlapping IHC profiles of similar diseases. We verified that the machine-learning algorithm could be applied for diagnosis precision with a generally acceptable hit rate in a nationwide dataset. Clinical and histological features should also be taken into account for the proper use of this system in the decision-making process.
Keimyung Author(s)(Kor)
정혜라
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
2075-4418
Source
https://www.mdpi.com/2075-4418/13/7/1308
DOI
10.3390/diagnostics13071308
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44972
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Pathology (병리학)
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