Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.
The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserver variability in the evaluation of HER2 staining b...
| Publicado en: | Virchows Archiv: European Journal of Pathology Vol. 479; no. 3; pp. 443 - 450 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152504085&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152504085 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09456317 O1Z jtl: Virchows Archiv: European Journal of Pathology issn: 09456317 maglogo: N pubinfo: dt: Sep2021 vid: 479 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 152504085 151465166 152504085 NLM34279719 152504085 10.1007/s00428-021-03154-x NLM34279719 152504085 ppf: 443 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study. aug: au: Yue, Meng Zhang, Jun Wang, Xinran Yan, Kezhou Cai, Lijing Tian, Kuan Niu, Shuyao Han, Xiao Yu, Yongqiang Huang, Junzhou Han, Dandan Yao, Jianhua Liu, Yueping affil: Department of Pathology, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Road, 050011, Shijiazhuang, Hebei, China sug: subj: Artificial Intelligence Receptors, Cell Surface Analysis Image Interpretation, Computer Assisted Microscopy Equipment and Supplies Immunohistochemistry Carcinoma, Ductal, Breast Breast Neoplasms In Situ Hybridization, Fluorescence Female Reproducibility of Results China Receptors, Cell Surface Carcinoma, Ductal, Breast Pathology Retrospective Design Human Observer Bias Automation, Laboratory Predictive Value of Tests Breast Neoplasms Pathology Comparative Studies Multicenter Studies Evaluation Research Validation Studies Female ab: The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserver variability in the evaluation of HER2 staining by visual examination. In this study, we aim to propose an artificial intelligence (AI)-assisted microscope to improve the HER2 assessment accuracy and reliability. Our AI-assisted microscope was equipped with a conventional microscope with a cell-level classification-based HER2 scoring algorithm and an augmented reality module to enable pathologists to obtain AI results in real time. We organized a three-round ring study of 50 infiltrating duct carcinoma not otherwise specified (NOS) cases without neoadjuvant treatment, and recruited 33 pathologists from 6 hospitals. In the first ring study (RS1), the pathologists read 50 HER2 whole-slide images (WSIs) through an online system. After a 2-week washout period, they read the HER2 slides using a conventional microscope in RS2. After another 2-week washout period, the pathologists used our AI microscope for assisted interpretation in RS3. The consistency and accuracy of HER2 assessment by the AI-assisted microscope were significantly improved (p < 0.001) over those obtained using a conventional microscope and online WSI. Specifically, our AI-assisted microscope improved the precision of immunohistochemistry (IHC) 3 + and 2 + scoring while ensuring the recall of fluorescent in situ hybridization (FISH)-positive results in IHC 2 + . Also, the average acceptance rate of AI for all pathologists was 0.90, demonstrating that the pathologists agreed with most AI scoring results. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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