PMID- 37328516
OWN - NLM
STAT- MEDLINE
DCOM- 20230619
LR  - 20231122
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Jun 16
TI  - Deep learning to automatically evaluate HER2 gene amplification status from 
      fluorescence in situ hybridization images.
PG  - 9746
LID - 10.1038/s41598-023-36811-z [doi]
LID - 9746
AB  - Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify 
      breast cancer patients who may respond to targeted anti-HER2 therapy. This study 
      aims to develop an automated method for quantifying HER2 fluorescence in situ 
      hybridization (FISH) signals and improve the working efficiency of pathologists. 
      An Aitrox artificial intelligence (AI) model based on deep learning was 
      constructed, and a comparison between the AI model and traditional manual 
      counting was performed. In total, 918 FISH images from 320 consecutive invasive 
      breast cancers were analysed and automatically classified into 5 groups according 
      to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% 
      (157/184) with a mean average precision of 0.735. In Group 5, the most common 
      group, the consistency was as high as 95.90% (117/122), while the consistency was 
      low in the other groups due to the limited number of cases. The causes of this 
      inconsistency, including clustered HER2 signals, coarse CEP17 signals and some 
      section quality problems, were analysed. The developed AI model is a reliable 
      tool for evaluating HER2 amplification statuses, especially for breast cancer in 
      Group 5; additional cases from multiple centres could further improve the 
      accuracy achieved for other groups.
CI  - (c) 2023. The Author(s).
FAU - Xue, Tian
AU  - Xue T
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Chang, Heng
AU  - Chang H
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Ren, Min
AU  - Ren M
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Wang, Haochen
AU  - Wang H
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Yang, Yu
AU  - Yang Y
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Wang, Boyang
AU  - Wang B
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - Lv, Lei
AU  - Lv L
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - Tang, Licheng
AU  - Tang L
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - Fu, Chicheng
AU  - Fu C
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - Fang, Qu
AU  - Fang Q
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - He, Chuan
AU  - He C
AD  - Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
FAU - Zhu, Xiaoli
AU  - Zhu X
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China.
FAU - Zhou, Xiaoyan
AU  - Zhou X
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China. xyzhou100@163.com.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China. xyzhou100@163.com.
FAU - Bai, Qianming
AU  - Bai Q
AD  - Department of Pathology, Fudan University Shanghai Cancer Centre, 270 Dong'an 
      Road, Shanghai, 200032, China. baiqianming@163.com.
AD  - Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 
      China. baiqianming@163.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230616
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - EC 2.7.10.1 (Receptor, ErbB-2)
RN  - 0 (Biomarkers, Tumor)
SB  - IM
MH  - Humans
MH  - Female
MH  - In Situ Hybridization, Fluorescence/methods
MH  - Gene Amplification
MH  - Artificial Intelligence
MH  - *Deep Learning
MH  - Receptor, ErbB-2/genetics/metabolism
MH  - *Breast Neoplasms/genetics/metabolism
MH  - Biomarkers, Tumor/genetics
PMC - PMC10275857
COIS- The authors declare no competing interests.
EDAT- 2023/06/17 05:11
MHDA- 2023/06/19 16:16
PMCR- 2023/06/16
CRDT- 2023/06/16 23:19
PHST- 2022/09/27 00:00 [received]
PHST- 2023/06/10 00:00 [accepted]
PHST- 2023/06/19 16:16 [medline]
PHST- 2023/06/17 05:11 [pubmed]
PHST- 2023/06/16 23:19 [entrez]
PHST- 2023/06/16 00:00 [pmc-release]
AID - 10.1038/s41598-023-36811-z [pii]
AID - 36811 [pii]
AID - 10.1038/s41598-023-36811-z [doi]
PST - epublish
SO  - Sci Rep. 2023 Jun 16;13(1):9746. doi: 10.1038/s41598-023-36811-z.