Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

Journal: Virchows Archiv : an international journal of pathology
PMID: 34279719

Abstract

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.

Authors

  • Meng Yue
    Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Xinran Wang
    Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Kezhou Yan
    AI Healthcare, Technology and Engineering Group, Tencent Inc, Tencent Building, Kejizhongyi Avenue, Hi-tech Park, Shenzhen, PR China.
  • Lijing Cai
    Heilongjiang Bayi Agricultural University, College of Information and Electrical Engineering, Daqing, Heilongjiang 163319, China.
  • Kuan Tian
    AI Healthcare, Technology and Engineering Group, Tencent Inc, Tencent Building, Kejizhongyi Avenue, Hi-tech Park, Shenzhen, PR China.
  • Shuyao Niu
    Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Xiao Han
    College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
  • Yongqiang Yu
    College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
  • Junzhou Huang
  • Dandan Han
    School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
  • Jianhua Yao
  • Yueping Liu
    Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.

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