Evidence map›Paper›PMID 31332619›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2020

Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging.

Lan Li, Yishu Chen, Zhe Shen, Xuequn Zhang, Jianzhong Sang, Yong Ding, Xiaoyun Yang, Jun Li, Ming Chen, Chaohui Jin and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

86 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Artificial intelligence in gastric cancer: a systematic review.Journal of cancer research and clinical oncology · 2020
    Pooled it
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

26 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Lan LiDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Yishu ChenDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Zhe ShenDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Xuequn ZhangDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China.
Jianzhong SangDepartment of Gastroenterology, Yuyao People's Hospital, Yuyao, China.
Yong DingDepartment of Gastroenterology, The Affiliated Hospital of School of Medicine of Ningbo University, Ningbo, China.
Xiaoyun YangDepartment of Gastroenterology, Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Jun LiDepartment of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Ming ChenHithink RoyalFlush Information Network Co., Ltd, Hangzhou, China.
Chaohui JinHithink RoyalFlush Information Network Co., Ltd, Hangzhou, China.
Chunlei ChenState Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
Chaohui YuDepartment of Gastroenterology, The First Affiliated Hospital, College of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310003, China. zyyyych@zju.edu.cn.ORCID http://orcid.org/0000-0003-4842-3646

Funding

National Key R&D Program of China 2017YFC0908900National Key R&D Program of China 2018YFA0507502Natural Science Foundation of Zhejiang Province LY15H030012
6 · The paper itself

Abstract

backgroundMagnifying endoscopy with narrow band imaging (M-NBI) has been applied to examine early gastric cancer by observing microvascular architecture and microsurface structure of gastric mucosal lesions. However, the diagnostic efficacy of non-experts in differentiating early gastric cancer from non-cancerous lesions by M-NBI remained far from satisfactory. In this study, we developed a new system based on convolutional neural network (CNN) to analyze gastric mucosal lesions observed by M-NBI.

methodsA total of 386 images of non-cancerous lesions and 1702 images of early gastric cancer were collected to train and establish a CNN model (Inception-v3). Then a total of 341 endoscopic images (171 non-cancerous lesions and 170 early gastric cancer) were selected to evaluate the diagnostic capabilities of CNN and endoscopists. Primary outcome measures included diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

resultsThe sensitivity, specificity, and accuracy of CNN system in the diagnosis of early gastric cancer were 91.18%, 90.64%, and 90.91%, respectively. No significant difference was spotted in the specificity and accuracy of diagnosis between CNN and experts. However, the diagnostic sensitivity of CNN was significantly higher than that of the experts. Furthermore, the diagnostic sensitivity, specificity and accuracy of CNN were significantly higher than those of the non-experts.

conclusionsOur CNN system showed high accuracy, sensitivity and specificity in the diagnosis of early gastric cancer. It is anticipated that more progress will be made in optimization of the CNN diagnostic system and further development of artificial intelligence in the medical field.

Indexed as

Early Detection of CancerGastric MucosaHumansImage Processing, Computer-AssistedNarrow Band ImagingNeural Networks, ComputerSensitivity and SpecificityStomach NeoplasmsConvolutional neural networkEarly gastric cancerMagnifying endoscopyNarrow band imaging

Identifiers

PMID31332619
PMCPMC6942561

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.