Background: Ovarian cancer is a leading gynecological malignancy with high global mortality. Early and accurate diagnosis is critical for optimal management; however, a considerable portion of ovarian masses remain indeterminate after initial evaluation. Although transvaginal ultrasound is the first-line imaging tool, up to 30% of cases yield inconclusive findings, complicating treatment decisions.Methods: This review summarizes current diagnostic strategies for ovarian masses, with an emphasis on advanced imaging and emerging technologies. Topics include the diagnostic utility of contrast-enhanced MRI, the application of the O-RADS MRI scoring system, and the integration of Artificial Intelligence (AI) into imaging workflows.Results: Contrast-enhanced MRI offers high diagnostic accuracy (83–93%) for characterizing indeterminate ovarian masses. The O-RADS MRI Score offers a reported sensitivity of 93% and specificity of 91% for malignancy risk assessment. Additionally, new classification systems have been proposed to further improve diagnostic performance. AI-based approaches, particularly machine learning and deep learning applied to imaging data, show potential in improving diagnostic precision; however, most techniques require further clinical validation before widespread adoption.Conclusions: Advanced imaging techniques and AI-driven methods are reshaping the diagnostic landscape of ovarian cancer. While current tools like MRI and O-RADS enhance accuracy, ongoing research into novel models and AI applications suggests further improvements are possible. Clinical validation and expert oversight remain essential for their integration into routine practice.
背景：卵巢癌是全球死亡率较高的主要妇科恶性肿瘤。早期准确诊断对优化治疗至关重要，然而相当一部分卵巢肿块在初步评估后仍无法明确性质。尽管经阴道超声是首选影像学检查手段，但高达30%的病例无法获得确定性结论，使治疗决策复杂化。 方法：本综述总结了当前卵巢肿块的诊断策略，重点关注先进影像学技术和新兴技术。涵盖主题包括增强磁共振成像的诊断效用、O-RADS MRI评分系统的应用，以及人工智能在影像工作流程中的整合。 结果：增强磁共振成像对性质未明卵巢肿块的诊断准确率较高（83-93%）。O-RADS MRI评分系统在恶性肿瘤风险评估中报告的敏感度为93%，特异度为91%。此外，新提出的分类系统有望进一步提升诊断效能。基于人工智能的方法，特别是应用于影像数据的机器学习和深度学习技术，显示出提高诊断精度的潜力；但多数技术在大规模应用前仍需进一步的临床验证。 结论：先进影像学技术与人工智能驱动的方法正在重塑卵巢癌的诊断格局。虽然当前如磁共振成像和O-RADS等工具提高了诊断准确性，但对新型模型和人工智能应用的持续研究预示着进一步改进的可能性。临床验证和专家监督对于这些技术融入常规实践仍然至关重要。
Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions
肿瘤(癌症)患者之家