Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases

Abstract Background Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and proge...

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Bibliographic Details
Main Authors: Minsun Jung, Seung Geun Song, Soo Ick Cho, Sangwon Shin, Taebum Lee, Wonkyung Jung, Hajin Lee, Jiyoung Park, Sanghoon Song, Gahee Park, Heon Song, Seonwook Park, Jinhee Lee, Mingu Kang, Jongchan Park, Sergio Pereira, Donggeun Yoo, Keunhyung Chung, Siraj M. Ali, So-Woon Kim
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Breast Cancer Research
Subjects:
Artificial intelligence (AI)
Breast cancer
Concordance
Digital pathology
Estrogen receptor (ER)
Human epidermal growth factor receptor 2 (HER2)
Online Access:https://doi.org/10.1186/s13058-024-01784-y

Internet

https://doi.org/10.1186/s13058-024-01784-y

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