本综述的目的是调查放射组学特征，以检测接受前列腺切除术的前列腺癌 (PCa) 患者的磁共振成像 (MRI) 病理性囊外扩展 (pECE)。使用科学文献数据库检索 2007 年 1 月至 2023 年 10 月发表的研究。所有与 PCa MRI 分期和使用放射组学特征检测前列腺切除术后 pECE 相关的研究均包含在内。根据系统审查和荟萃分析的首选报告项目（PRISMA）进行系统审查。使用 QUADAS-2 和放射组学质量评分评估证据的偏倚风险和确定性。从筛选的 1247 篇文章标题中，评估了 16 份报告的资格，并纳入了 11 项研究。所有研究均采用回顾性研究设计，其中大多数使用 3 T MRI。只有两项研究是在多个机构进行的。对于测试验证，仅使用放射组学特征的模型的最高 AUC 为 0.85。训练组和验证组的最佳模型性能（与临床/语义特征相关的放射组学）的 AUC 分别为 0.72-0.92 和 0.69-0.89。在检测 ECE 方面，组合模型比单独的放射组学特征表现更好。大多数研究显示出低至中等的偏倚风险。经过彻底分析，我们发现没有强有力的证据支持临床使用放射组学特征来识别术前 PCa 患者的囊外扩展 (ECE)。未来的研究应采用前瞻性多中心方法，使用大型公共数据集和组合模型来检测 ECE。使用放射组学算法与临床和人工智能集成来预测囊外扩张，可能会导致更准确的预测模型的开发，这可能有助于改善手术规划并为前列腺癌患者带来更好的结果。PROSPERO CRD42021272088。发布：https://doi.org/10.1136/bmjopen-2021-052342 .Radiomics 可以从 MRI 中提取诊断特征，以提高前列腺癌的诊断性能。在检测囊外扩展方面，组合模型比单独的放射组学特征表现更好。放射组学对于 PCa 患者的囊外检测尚不可靠。© 2024。作者。

The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.The use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.PROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Radiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.© 2024. The Author(s).