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Musatov I.D.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Osipov V.O.

Sberbank — SberData, Moscow, Russia

Verbovsky A.N.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Setdikova G.R.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Semenkov A.V.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Potential applications of artificial intelligence technology in assessing mitotic activity of gastrointestinal stromal tumors of the stomach

Authors:

Musatov I.D., Osipov V.O., Verbovsky A.N., Setdikova G.R., Semenkov A.V.

More about the authors

Journal: Russian Journal of Archive of Pathology. 2026;88(2): 67‑71

DOI: 10.17116/patol20268802167

Read: 401 times

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Table of contents

POTENTIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN ASSESSING MITOTIC ACTIVITY OF GASTROINTESTINAL STROMAL TUMORS OF THE STOMACH
POTENTIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN ASSESSING MITOTIC ACTIVITY OF GASTROINTESTINAL STROMAL TUMORS OF THE STOMACH

To cite this article:

Musatov ID, Osipov VO, Verbovsky AN, Setdikova GR, Semenkov AV. Potential applications of artificial intelligence technology in assessing mitotic activity of gastrointestinal stromal tumors of the stomach. Russian Journal of Archive of Pathology. 2026;88(2):67‑71. (In Russ.)
https://doi.org/10.17116/patol20268802167

Подписка 2026 Архив патологии (Russian Journal of Archive of Pathology)
МСФ на ЯМ
Использование инфографики авторами научных работ
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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.

Keywords:

GIST
artificial intelligence
mitotic activity

Authors:

Musatov I.D.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Osipov V.O.

Sberbank — SberData, Moscow, Russia

Verbovsky A.N.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Setdikova G.R.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Semenkov A.V.

Moscow Regional Research and Clinical Institute, Moscow, Russia

Received:

12.07.2025

Accepted:

22.12.2025

Close metadata

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