PMID- 35071491
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220828
IS  - 2305-5839 (Print)
IS  - 2305-5847 (Electronic)
IS  - 2305-5839 (Linking)
VI  - 9
IP  - 24
DP  - 2021 Dec
TI  - A deep convolutional neural network-based method for laryngeal squamous cell 
      carcinoma diagnosis.
PG  - 1797
LID - 10.21037/atm-21-6458 [doi]
LID - 1797
AB  - BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is one of the most common 
      tumors of the respiratory tract. Currently, the diagnosis of LSCC is mainly based 
      on a laryngoscopy analysis and pathological findings. Deep-learning algorithms 
      have been shown to provide accurate clinical diagnoses. METHODS: We developed a 
      deep convolutional neural network (CNN) model, and evaluated its application to 
      narrow-band imaging (NBI) endoscopy and pathological diagnoses of LSCC at several 
      hospitals. A total of 4,591 patients' laryngeal NBI scans (1,927 benign and 2,664 
      LSCC) were used to test and validate the model. Additionally, 3,458 pathological 
      images (752 benign and 2,706 LSCC) of 1,228 patients' hematoxylin and eosin 
      staining slides (318 benign and 910 LSCC) were used for the pathological 
      diagnosis training and validation. The images were randomly divided into 
      training, validation and testing images at the ratio of 70:15:15. An independent 
      test cohort of LSCC NBI scans and pathological images from other institutions 
      were also used. RESULTS: In the NBI group, the areas under the curve of the 
      validation, test, and independent test data sets were 0.966, 0.964, and 0.873, 
      respectively, and those of the pathology group were 0.994, 0.981, and 0.982, 
      respectively. Our method was highly accurate at diagnosing LSCC. CONCLUSIONS: In 
      this study, the CNN model performed well in the NBI and pathological diagnosis of 
      LSCC. More accurate and faster diagnoses could be achieved with the assistance of 
      this algorithm.
CI  - 2021 Annals of Translational Medicine. All rights reserved.
FAU - He, Yurong
AU  - He Y
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - Cheng, Yingduan
AU  - Cheng Y
AD  - Department of Urology, Shenzhen People's Hospital, The Second Clinical Medical 
      College of Jinan University, The First Affiliated Hospital of Southern University 
      of Science and Technology, Shenzhen, China.
FAU - Huang, Zhigang
AU  - Huang Z
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - Xu, Wen
AU  - Xu W
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - Hu, Rong
AU  - Hu R
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - Cheng, Liyu
AU  - Cheng L
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - He, Shizhi
AU  - He S
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
FAU - Yue, Changli
AU  - Yue C
AD  - Department of Pathology, Beijing Tongren Hospital, Capital Medical University, 
      Beijing, China.
FAU - Qin, Gang
AU  - Qin G
AD  - Department of Otolaryngology Head and Neck Surgery, the Affiliated Hospital of 
      Southwest Medical University, Luzhou, China.
FAU - Wang, Yan
AU  - Wang Y
AD  - Department of Otolaryngology Head and Neck Surgery, The First Hospital of China 
      Medical University, Shenyang, China.
FAU - Zhong, Qi
AU  - Zhong Q
AD  - Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, 
      Capital Medical University; Key Laboratory of Otolaryngology Head and Neck 
      Surgery (Capital Medical University), Ministry of Education, Beijing, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Ann Transl Med
JT  - Annals of translational medicine
JID - 101617978
PMC - PMC8756237
OTO - NOTNLM
OT  - Laryngeal squamous cell carcinoma (LSCC)
OT  - convolutional neural network (CNN)
OT  - narrow-band imaging (NBI)
OT  - pathology
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at https://dx.doi.org/10.21037/atm-21-6458). The authors have no 
      conflicts of interest to declare.
EDAT- 2022/01/25 06:00
MHDA- 2022/01/25 06:01
PMCR- 2021/12/01
CRDT- 2022/01/24 08:55
PHST- 2021/10/22 00:00 [received]
PHST- 2021/12/17 00:00 [accepted]
PHST- 2022/01/24 08:55 [entrez]
PHST- 2022/01/25 06:00 [pubmed]
PHST- 2022/01/25 06:01 [medline]
PHST- 2021/12/01 00:00 [pmc-release]
AID - atm-09-24-1797 [pii]
AID - 10.21037/atm-21-6458 [doi]
PST - ppublish
SO  - Ann Transl Med. 2021 Dec;9(24):1797. doi: 10.21037/atm-21-6458.