Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.
Background: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancer...
| Publicado en: | Lancet Oncology Vol. 20; no. 12; pp. 1645 - 1655 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140095167&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140095167 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14702045 KH2 jtl: Lancet Oncology issn: 14702045 maglogo: N pubinfo: dt: Dec2019 vid: 20 iid: 12 ed: 1077-4114 (Print) pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 140095167 140095167 NLM31591062 140095167 10.1016/S1470-2045(19)30637-0 NLM31591062 140095167 ppf: 1645 ppct: 10 formats: tig: atl: Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. aug: au: Luo, Huiyan Xu, Guoliang Li, Chaofeng He, Longjun Luo, Linna Wang, Zixian Jing, Bingzhong Deng, Yishu Jin, Ying Li, Yin Li, Bin Tan, Wencheng He, Caisheng Seeruttun, Sharvesh Raj Wu, Qiubao Huang, Jun Huang, De-wang Chen, Bin Lin, Shao-bin Chen, Qin-ming affil: Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China sug: subj: Artificial Intelligence Endoscopy Methods Algorithms Gastrointestinal Neoplasms Diagnosis Image Processing, Computer Assisted Methods Aged Human Prospective Studies Case Control Studies Retrospective Design Prognosis ROC Curve Child Adolescence Young Adult Aged, 80 and Over Middle Age Adult Female Male Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged: 65+ years Child: 6-12 years Adolescent: 13-18 years Aged, 80 & over Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Background: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.Methods: This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method.Findings: 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952-0·957) in the internal validation set, 0·927 (0·925-0·929) in the prospective set, and ranged from 0·915 (0·913-0·917) to 0·977 (0·977-0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924-0·957] vs 0·945 [0·927-0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832-0·880], p<0·0001) and trainee (0·722 [0·691-0·752], p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788-0·838) for GRAIDS, 0·932 (0·913-0·948) for the expert endoscopist, 0·974 (0·960-0·984) for the competent endoscopist, and 0·824 (0·795-0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971-0·984) for GRAIDS, 0·980 (0·974-0·985) for the expert endoscopist, 0·951 (0·942-0·959) for the competent endoscopist, and 0·904 (0·893-0·916) for the trainee endoscopist.Interpretation: GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses.Funding: The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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