Loading
Life sciences · Journal article
Eurasian Journal of Oncology and Radiology · October 6, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Relevance: Endometrial cancer (EC) is one of the most common malignant neoplasms of the female reproductive system in developed countries. Despite a relatively favorable prognosis when detected early, diagnosis at preclinical stages remains challenging, especially in patients with obesity, metabolic disorders, and concomitant gynecological diseases. Traditional diagnostic methods, including transvaginal ultrasound (TVUS), hysteroscopy, biopsy, and magnetic resonance imaging (MRI), have limitations in sensitivity and specificity. Recently, artificial intelligence (AI) technologies have been actively integrated into medical imaging and pathological diagnostics. AI improves EC diagnostic accuracy and reduces the impact of subjective factors.The study aimed to review the current state of AI applications in the diagnosis, preoperative staging, and risk stratification of endometrial cancer, including analyses of transvaginal ultrasound (TVUS), magnetic resonance imaging (MRI), histopathological images, and radiomic data.Materials and Methods: This systematized analysis covered scientific publications indexed in the PubMed and Cochrane Library databases over the past 10 years. The final review included 22 publications addressing the application of AI in the diagnosis, staging, and risk stratification of endometrial cancer.Results: AI model performance varied by diagnostic modality and validation approach. In individual studies, AI algorithms achieved accuracies of up to 90–95% in differentiating benign from malignant endometrial lesions using MRI data and digital histopathological images. MRI-based radiomic models assessed tumor grade, depth of myometrial invasion, lymphovascular space invasion, and lymph node metastasis. TVUS-based models achieved AUC values ranging from 0.80 to 0.90. In several studies involving hysteroscopy and histopathological analysis, model performance exceeded 90%. The integration of clinical, laboratory, and imaging data substantially improved the diagnostic and prognostic value of the models.Conclusion: Applying AI to TVUS, MRI, hysteroscopic, and digital histopathological data, as well as to preoperative assessment of endometrial cancer risk factors, may improve interpretation accuracy, reduce subjectivity, and facilitate the development of personalized therapeutic approaches. Nevertheless, most models remain at the research prototype stage. Implementing them in clinical practice requires multicenter prospective studies, standardized analytical methods, and integration of multimodal AI models into routine clinical workflows.