ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology
Provisionally accepted
- 1The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
- 2BYD Company Limited, Shenzhen, China
- 3College of Clinical Medicine, Henan University of Science and Technology, Luoyang, China
- 4University of Science and Technology Beijing, Beijing, China
- 5Department of Pathology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
- 6Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital of The First Affiliated Hospital (College of Clinical Medicine), Henan University of Science and Technology, Luoyang, China
- 7Department of Gynecologic Oncology, The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, China
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Background: Rapid advances in artificial intelligence (AI) have enabled automated tumor identification. To overcome challenges in traditional pathology, including complex sampling and limited physician resources, accessible tools for automated diagnosis are urgently needed. Methods: We developed a deep learning system based on an improved ResNet-18 to automatically identify endometrioid endometrial carcinoma (EEC) from H&E-stained endometrial hyperplastic lesions and normal tissues. Results: Frontiers The model demonstrated strong performance in detecting endometrioid endometrial carcinoma. The positive predictive value (PPV), defined as the proportion of true disease cases among all positive diagnostic results, reached 95.13%, and the F1-score, defined as the harmonic mean of precision and recall, reached 0.95. The model achieved a PPV of 87.15% and an F1-score of 0.87 for typical hyperplasia, as well as a PPV of 79.88% and an F1-score of 0.74 for atypical hyperplasia, both meeting clinically acceptable thresholds. For normal endometrial physiological states, the PPVs were 91.75% (proliferative phase), 80.94% (secretory phase), and 80.88% (menopausal phase). Conclusion: This multi-task deep learning system provides stable and efficient support for automated EEC identification and effectively classifies endometrial pathological and physiological states, demonstrating strong potential for clinical translation.
Keywords: artificial intelligence, Convolutional Neural Network, deep learning, Endometrioid endometrial carcinoma, Endometrium, Pathology
Received: 08 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Li, Zou, Ma, Liu, Wang, Huang, Gao and Yang. 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:
Shegan Gao
Xueying Yang
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