ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1486140
Digital pathology-based artificial intelligence model to predict microsatellite instability in gastroesophageal junction adenocarcinomas
Provisionally accepted
- 1Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- 2Mudanjiang Medical University, Mudanjiang, China
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Purpose: Microsatellite instability (MSI) plays a crucial role in determining the therapeutic outcomes of gastroesophageal junction (GEJ) adenocarcinoma. This study aimed to develop a deep learning model based on H&E-stained pathological specimens to accurately identify MSI-H in GEJ adenocarcinomas patients. Methods: A total of 416 H&E-stained slides of 212 GEJ adenocarcinoma patients were collected to establish an artificial intelligence (AI) model using digital pathology (DP) for of MSI-H prediction. Simple Vit and ResNet18 Neural networks were trained and tested on models developed from patchlevel images. A whole-slide image (WSI)-level AI model was constructed by integrating deep learning-generated pathological features with six machine learning algorithms. Results: The MLP model showed demonstrated the highest performance in predicting MSI-H in the test cohort, achieving an AUC of 93.3%, a sensitivity of 0.841, and a specificity of 0.952. Similarly, Decision Curve Analysis (DCA) revealed that WSI-level H&E-stained slides offered significant clinical MSI-H prediction in GEJ adenocarcinoma patients. Conclusion: The AI model based on digital pathology exhibits great potential for predicting MSI-H in GEJ adenocarcinoma, suggesting promising clinical applications.
Keywords: artificial intelligence, Deep machine learning, Gastroesophageal junction adenocarcinomas, digital pathology, Microsatellite Instability, Microsatellite instability high
Received: 25 Aug 2024; Accepted: 09 Jul 2025.
Copyright: © 2025 Li, Chen, Sun, Li and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
ming Dao Li, Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Kuisheng Chen, Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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