Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries, with diagnostic accuracy and early detection being critical to patient outcomes. Recent advances in artificial intelligence (AI) offer new opportunities to enhance diagnostic precision and clinical decision-making. Objectives: This literature review aims to explore recent developments in AI-augmented diagnostic approaches for EC, with a focus on applications in histopathology, imaging, and multi-omics, and to assess their clinical impact and future potential. Methods: A non-systematic literature review was conducted to examine recent advances in artificial intelligence applications for the diagnosis of EC. Relevant studies were identified through searches on PubMed and Google Scholar, focusing on the integration of AI techniques in histopathology, imaging, and multi-omics data. Conclusions: AI-driven diagnostic tools have shown high performance in detecting and characterizing EC across multiple modalities, often matching or exceeding expert-level accuracy. These technologies hold promise for earlier detection, better risk assessment, and more personalized treatment planning. However, further research and validation are needed to address current limitations and support their broader integration into clinical workflows.
背景：子宫内膜癌是发达国家最常见的妇科恶性肿瘤，其诊断准确性与早期发现对患者预后至关重要。人工智能技术的最新进展为提高诊断精度和临床决策提供了新机遇。目的：本文献综述旨在探讨人工智能增强子宫内膜癌诊断方法的最新进展，重点关注其在组织病理学、影像学及多组学领域的应用，并评估其临床影响与未来潜力。方法：通过非系统性文献综述，检索PubMed和Google Scholar数据库中关于人工智能应用于子宫内膜癌诊断的最新研究，重点关注人工智能技术在组织病理学、影像学及多组学数据整合方面的应用。结论：人工智能驱动的诊断工具在多种检测模式中展现出卓越的子宫内膜癌检测与表征能力，其准确度常达到或超越专家水平。这些技术有望实现更早的疾病检测、更精准的风险评估及更个性化的治疗方案制定。然而，当前仍需进一步的研究与验证以突破现有局限，推动其在临床工作流程中的广泛应用。
AI-Augmented Advances in the Diagnostic Approaches to Endometrial Cancer
肿瘤(癌症)患者之家