通过MRI放射线签名预测前列腺癌的囊外延伸：系统评价

这篇综述的目的是调查用于检测病理外延伸（PECE）对磁共振成像（MRI）的病理延伸（PCA）（PCA）（PCA）（PCA）的标志。科学文献数据库用于搜索从2007年1月至2023年10月发表的搜索研究。所有与PCA MRI分期有关的研究以及使用放射线学特征来检测前列腺切除术后彼得的研究。根据系统审查和荟萃分析（PRISMA）的首选报告项目进行系统审查。使用Quadas-2和放射线质量评分评估了偏见和证据确定性的风险。从筛选的1247个文章标题中，评估了16个报告资格，并在该系统审查中包括了11项研究。所有人都使用了回顾性研究设计，其中大多数使用了3吨MRI。在多个机构中只进行了两项研究。对于测试验证，仅使用放射线学特征的模型中最高的AUC为0.85。最佳模型性能的AUC（与临床/语义特征相关的放射组学）分别为0.72-0.92和0.69-0.89，分别为培训和验证组。组合模型的性能比仅用于检测ECE的放射线学特征更好。大多数研究表明，偏见的风险低到中等。经过彻底的分析，我们发现没有有力的证据支持放射线学特征的临床使用，以鉴定囊外PCA患者中的囊外扩展（ECE）。 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.已发布：https：//doi.org/10.1136/bmjopen-2021-052342。非二元组可以从MRI中提取诊断功能，以增强前列腺癌诊断性能。组合模型的性能要比单独的放射线学特征表现更好，以检测囊外延伸。 PCA患者的放射素学尚不可靠可靠。

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.