PMID- 35502367
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220506
IS  - 2223-4292 (Print)
IS  - 2223-4306 (Electronic)
IS  - 2223-4306 (Linking)
VI  - 12
IP  - 5
DP  - 2022 May
TI  - An efficient fluorescence in situ hybridization (FISH)-based circulating 
      genetically abnormal cells (CACs) identification method based on Multi-scale 
      MobileNet-YOLO-V4.
PG  - 2961-2976
LID - 10.21037/qims-21-909 [doi]
AB  - BACKGROUND: Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer 
      are cells that have been shed from the primary tumor, which cause the development 
      of a secondary tumor in a distant organ site, leading to cancer metastasis. 
      Recent research suggests that CTCs with abnormalities in gene copy numbers in 
      mononuclear cell-enriched peripheral blood samples, namely circulating 
      genetically abnormal cells (CACs), could be used as a non-invasive decision tool 
      to detect patients with benign pulmonary nodules. Such cells are identified by 
      counting the fluorescence signals of fluorescence in situ hybridization (FISH). 
      However, owing to the rarity of CACs in the blood, identification of CACs using 
      this technique is time-consuming and is a drawback of this method. METHODS: This 
      study has proposed an efficient and automatic FISH-based CACs identification 
      approach which is based on a combination of the high accuracy of You Only Look 
      Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of 
      YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and 
      prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a 
      new feature map with a larger scale to enable the enhanced detection ability for 
      small targets. RESULTS: We trained and tested the proposed model using a dataset 
      containing more than 7,000 cells based on five-fold cross-validation. All the 
      images in the dataset were 2,448x2,048 (pixels) in size. The number of cells in 
      each image was >70. The accuracy of four-color fluorescence signals detection for 
      our proposed model were all approximately 98%, and the mean average precision 
      (mAP) were close to 100%. The final outcome of the developed method was the type 
      of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method 
      had a CACs identification accuracy of 93.86% (similar to an expert pathologist), 
      and a detection speed that was about 500 times greater than that of a 
      pathologist. CONCLUSIONS: The developed method could greatly increase the review 
      accuracy, enhance the efficiency of reviewers, and reduce the review turnaround 
      time during CACs identification.
CI  - 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
FAU - Xu, Chao
AU  - Xu C
AD  - China Telecommunication Technology Labs, China Academy of Information and 
      Communications Technology, Beijing, China.
FAU - Zhang, Yi
AU  - Zhang Y
AD  - China Telecommunication Technology Labs, China Academy of Information and 
      Communications Technology, Beijing, China.
FAU - Fan, Xianjun
AU  - Fan X
AD  - Department of Product Development, Zhuhai Sanmed Biotech Ltd., Zhuhai, China.
FAU - Lan, Xingjie
AU  - Lan X
AD  - Department of Data Operation, Zhuhai Sanmed Biotech Ltd., Zhuhai, China.
FAU - Ye, Xin
AU  - Ye X
AD  - Department of Product Development, Zhuhai Sanmed Biotech Ltd., Zhuhai, China.
FAU - Wu, Tongning
AU  - Wu T
AD  - China Telecommunication Technology Labs, China Academy of Information and 
      Communications Technology, Beijing, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Quant Imaging Med Surg
JT  - Quantitative imaging in medicine and surgery
JID - 101577942
PMC - PMC9014158
OTO - NOTNLM
OT  - Circulating genetically abnormal cell (CAC)
OT  - MobileNet-V3
OT  - You Only Look Once-V4 (YOLO-V4)
OT  - fluorescence in situ hybridization (FISH)
OT  - multi-scale
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://qims.amegroups.com/article/view/10.21037/qims-21-909/coif). XF, XL and XY 
      are employees of Zhuhai Sanmed Biotech Ltd. The other authors have no conflicts 
      of interest to declare.
EDAT- 2022/05/04 06:00
MHDA- 2022/05/04 06:01
PMCR- 2022/05/01
CRDT- 2022/05/03 02:10
PHST- 2021/09/11 00:00 [received]
PHST- 2022/02/11 00:00 [accepted]
PHST- 2022/05/03 02:10 [entrez]
PHST- 2022/05/04 06:00 [pubmed]
PHST- 2022/05/04 06:01 [medline]
PHST- 2022/05/01 00:00 [pmc-release]
AID - qims-12-05-2961 [pii]
AID - 10.21037/qims-21-909 [doi]
PST - ppublish
SO  - Quant Imaging Med Surg. 2022 May;12(5):2961-2976. doi: 10.21037/qims-21-909.