Yi Lu, Lu Chen, Jiachuan Wu, Limian Er, Huihui Shi, Weihui Cheng, Ke Chen, Yuan Liu, Bingfeng Qiu, Qiancheng Xu, Yue Feng, Nan Tang, Fuchuan Wan, Jiachen Sun, Min Zhi
{"title":"内窥镜超声造影中的人工智能：胃肠道间质瘤的风险分层。","authors":"Yi Lu, Lu Chen, Jiachuan Wu, Limian Er, Huihui Shi, Weihui Cheng, Ke Chen, Yuan Liu, Bingfeng Qiu, Qiancheng Xu, Yue Feng, Nan Tang, Fuchuan Wan, Jiachen Sun, Min Zhi","doi":"10.1177/17562848231177156","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine.</p><p><strong>Objectives: </strong>We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs.</p><p><strong>Design: </strong>This was a retrospective study with external validation.</p><p><strong>Methods: </strong>We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts.</p><p><strong>Results: </strong>A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively.</p><p><strong>Conclusion: </strong>We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs.</p><p><strong>Registration: </strong>The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).</p>","PeriodicalId":23022,"journal":{"name":"Therapeutic Advances in Gastroenterology","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c3/59/10.1177_17562848231177156.PMC10233610.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors.\",\"authors\":\"Yi Lu, Lu Chen, Jiachuan Wu, Limian Er, Huihui Shi, Weihui Cheng, Ke Chen, Yuan Liu, Bingfeng Qiu, Qiancheng Xu, Yue Feng, Nan Tang, Fuchuan Wan, Jiachen Sun, Min Zhi\",\"doi\":\"10.1177/17562848231177156\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine.</p><p><strong>Objectives: </strong>We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs.</p><p><strong>Design: </strong>This was a retrospective study with external validation.</p><p><strong>Methods: </strong>We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts.</p><p><strong>Results: </strong>A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively.</p><p><strong>Conclusion: </strong>We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs.</p><p><strong>Registration: </strong>The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).</p>\",\"PeriodicalId\":23022,\"journal\":{\"name\":\"Therapeutic Advances in Gastroenterology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2023-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c3/59/10.1177_17562848231177156.PMC10233610.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Therapeutic Advances in Gastroenterology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/17562848231177156\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Therapeutic Advances in Gastroenterology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/17562848231177156","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors.
Background: Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine.
Objectives: We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs.
Design: This was a retrospective study with external validation.
Methods: We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts.
Results: A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively.
Conclusion: We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs.
Registration: The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).
期刊介绍：
Therapeutic Advances in Gastroenterology is an open access journal which delivers the highest quality peer-reviewed original research articles, reviews, and scholarly comment on pioneering efforts and innovative studies in the medical treatment of gastrointestinal and hepatic disorders. The journal has a strong clinical and pharmacological focus and is aimed at an international audience of clinicians and researchers in gastroenterology and related disciplines, providing an online forum for rapid dissemination of recent research and perspectives in this area.
The editors welcome original research articles across all areas of gastroenterology and hepatology.
The journal publishes original research articles and review articles primarily. Original research manuscripts may include laboratory, animal or human/clinical studies – all phases. Letters to the Editor and Case Reports will also be considered.
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