Use of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors

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dc.authorid0000-0001-8863-8348
dc.contributor.authorSeven, Gülseren
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorKoçhan, Koray
dc.contributor.authorTüzün İnce, Ali
dc.contributor.authorArıcı, Dilek Sema
dc.contributor.authorŞentürk, Hakan
dc.date.accessioned2022-02-02T08:34:12Z
dc.date.available2022-02-02T08:34:12Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractBackground and Aims This study aimed to investigate whether AI via a deep learning algorithm using endoscopic ultrasonography (EUS) images could predict the malignant potential of gastric gastrointestinal stromal tumors (GISTs). Methods A series of patients who underwent EUS before surgical resection for gastric GISTs were included. A total of 685 images of GISTs from 55 retrospectively included patients were used as the training data set for the AI system. Convolutional neural networks were constructed to build a deep learning model. After applying the synthetic minority oversampling technique, 70% of the generated images were used for AI training and 30% were used to test AI diagnoses. Next, validation was performed using 153 EUS images of 15 patients with GISTs. In addition, conventional EUS features of 55 patients in the training cohort were evaluated to predict the malignant potential of GISTs and mitotic index. Results The overall sensitivity, specificity, and accuracy of the AI system for predicting malignancy risk were 83%, 94%, and 82% in the training dataset, and 75%, 73%, and 66% in the validation cohort, respectively. When patients were divided into low-risk and high-risk groups, sensitivity, specificity, and accuracy increased to 99% in the training dataset and 99.7%, 99.7%, and 99.6%, respectively, in the validation cohort. No conventional EUS features were found to be associated with either malignant potential or mitotic index (P > 0.05). Conclusions AI via a deep learning algorithm using EUS images could predict the malignant potential of gastric GISTs with high accuracy. Graphic
dc.identifier.citationSeven, G., Silahtaroğlu, G., Koçhan, K., Tüzün İnce, A., Arıcı, D. S. ve Şentürk, H. (2022). Use of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors. Digestive Diseases and Sciences, 67(1), 273-281. https://dx.doi.org/10.1007/s10620-021-06830-9
dc.identifier.doi10.1007/s10620-021-06830-9
dc.identifier.endpage281
dc.identifier.issn0163-2116
dc.identifier.issn1573-2568
dc.identifier.issue1
dc.identifier.pmid33547537
dc.identifier.scopusqualityQ2
dc.identifier.startpage273
dc.identifier.urihttps://dx.doi.org/10.1007/s10620-021-06830-9
dc.identifier.urihttps://hdl.handle.net/20.500.12511/8950
dc.identifier.volume67
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofDigestive Diseases and Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectGastric Gastrointestinal Stromal Tumors
dc.subjectMitotic Index
dc.subjectRisk Classification
dc.titleUse of artificial intelligence in the prediction of malignant potential of gastric gastrointestinal stromal tumors
dc.typeArticle

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