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Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study

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Titel: Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study
Autoren: Aoling Huang, Yizhi Zhao, Feng Guan, Hongfeng Zhang, Bin Luo, Ting Xie, Shuaijun Chen, Xinyue Chen, Shuying Ai, Xianli Ju, Honglin Yan, Lin Yang, Jingping Yuan
Quelle: Comput Struct Biotechnol J
Computational and Structural Biotechnology Journal, Vol 26, Iss, Pp 40-50 (2024)
Verlagsinformationen: Elsevier BV, 2024.
Publikationsjahr: 2024
Schlagwörter: 0301 basic medicine, Artificial intelligence, 03 medical and health sciences, 0302 clinical medicine, Bladder cancer, HER2-low, Heterogeneity, TP248.13-248.65, Biotechnology, Research Article
Beschreibung: This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443-0.526 vs kappa=0.87; 95 % CI, 0.852-0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.
Publikationsart: Article
Other literature type
Sprache: English
ISSN: 2001-0370
DOI: 10.1016/j.csbj.2024.10.007
Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39469445
https://doaj.org/article/af9ec4b1370342b5a6af974f714fbbb7
Rights: CC BY NC ND
Dokumentencode: edsair.doi.dedup.....3a4726c52fb78374c78e5a7556104b1c
Datenbank: OpenAIRE
Beschreibung
Abstract:This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443-0.526 vs kappa=0.87; 95 % CI, 0.852-0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.
ISSN:20010370
DOI:10.1016/j.csbj.2024.10.007