Department of Radiology, St. James's Hospital, Dublin, Ireland.
School of Medicine, Trinity College Dublin, Dublin, Ireland.
Abdom Radiol (NY). 2024 Oct;49(10):3540-3547. doi: 10.1007/s00261-024-04330-8. Epub 2024 May 15.
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
卵巢癌与高癌症相关死亡率相关，其原因在于晚期诊断、有限的治疗选择以及频繁的疾病复发。因此，仔细选择患者尤为重要，特别是在进行根治性手术时。放射组学是医学影像学中的一个新兴领域，它可能有助于提供重要的预后评估，并有助于为根治性治疗策略选择患者。本系统评价旨在评估放射组学作为预测卵巢癌疾病复发的指标的作用。在 Medline、EMBASE 和 Web of Science 数据库中进行了系统搜索。我们纳入了定性分析中使用放射组学来预测卵巢癌术后复发的研究。使用 QUADAS-2 和放射组学质量评分工具评估研究质量。符合纳入标准的六项回顾性研究共纳入 952 名参与者。放射组学特征在预测疾病复发方面表现出一致的性能，其受试者工作特征曲线下面积（AUC 范围 0.77-0.89）值令人满意。放射组学特征似乎是卵巢癌疾病复发的良好预后指标，AUC 估计值为 0.77-0.89。综述研究一致报告了放射组学特征在增强这一复杂患者群体的风险分层和个性化治疗决策方面的潜力。需要进一步的研究来解决与特征可靠性、工作流程异质性以及前瞻性验证研究需求相关的限制。
