I D Musatov, V O Osipov, A N Verbovsky, G R Setdikova, A V Semenkov
{"title":"[Potential applications of artificial intelligence technology in assessing mitotic activity of gastrointestinal stromal tumors of the stomach].","authors":"I D Musatov, V O Osipov, A N Verbovsky, G R Setdikova, A V Semenkov","doi":"10.17116/patol20268802167","DOIUrl":null,"url":null,"abstract":"<p><p>Gastrointestinal stromal tumors (GISTs) are the most common subtype of soft tissue sarcomas, with diagnosis based on a combination of clinical presentation, anatomical localization of the tumor, histological and immunohistochemical (IHC) studies. This paper explores the potential of artificial intelligence (AI) technologies for the automated detection of mitotic figures in histological images of GISTs. A literature review on the application of AI in morphological diagnostics of various tumors is presented. A test dataset of 220 digitized histological slides annotated in CVAT was created as part of the study. A convolutional neural network, YOLOv11, trained over 300 epochs, was used to analyze mitotic activity. Model performance evaluation showed that its accuracy was limited due to the small size of the training dataset. Future work will focus on expanding the dataset and improving the accuracy of pathological mitosis detection.</p>","PeriodicalId":8548,"journal":{"name":"Arkhiv patologii","volume":"88 2","pages":"67-71"},"PeriodicalIF":0.0000,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arkhiv patologii","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17116/patol20268802167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Gastrointestinal stromal tumors (GISTs) are the most common subtype of soft tissue sarcomas, with diagnosis based on a combination of clinical presentation, anatomical localization of the tumor, histological and immunohistochemical (IHC) studies. This paper explores the potential of artificial intelligence (AI) technologies for the automated detection of mitotic figures in histological images of GISTs. A literature review on the application of AI in morphological diagnostics of various tumors is presented. A test dataset of 220 digitized histological slides annotated in CVAT was created as part of the study. A convolutional neural network, YOLOv11, trained over 300 epochs, was used to analyze mitotic activity. Model performance evaluation showed that its accuracy was limited due to the small size of the training dataset. Future work will focus on expanding the dataset and improving the accuracy of pathological mitosis detection.
期刊介绍：
The journal deals with original investigations on pressing problems of general pathology and pathologic anatomy, newest research methods, major issues of the theory and practice as well as problems of experimental, comparative and geographic pathology. To inform readers latest achievements of Russian and foreign medicine the journal regularly publishes editorial and survey articles, reviews of the most interesting Russian and foreign books on pathologic anatomy, new data on modern methods of investigation (histochemistry, electron microscopy, autoradiography, etc.), about problems of teaching, articles on the history of pathological anatomy development both in Russia and abroad.
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