利用MRI放射组学签名预测前列腺癌的包膜外扩展：一项系统综述

本综述的目的是调查用于检测前列腺癌（PCa）患者术后病理性包膜外扩展（pECE）的MRI放射组学签名。通过科学文献数据库检索自2007年1月至2023年10月发表的相关研究。纳入所有关于PCaMRI分期及使用放射组学签名检测pECE的研究。按照PRISMA标准进行系统综述。采用QUADAS-2和放射组学质量评分评估偏倚风险及证据的可靠性。在筛选的1247篇文章标题中，评估符合条件的有16篇，最终纳入11项研究。所有研究采用回顾性设计，大部分使用3 T MRI。仅有两项研究在多家机构进行。仅依靠放射组学特征的模型最高AUC为0.85（验证集）。结合临床/语义特征的最佳模型AUC在训练组和验证组分别为0.72-0.92和0.69-0.89。联合模型优于单一放射组学签名检测ECE。大部分研究偏倚风险低至中等。在深入分析后，尚无强有力证据支持放射组学签名在术前PCa患者识别包膜外扩展（ECE）中的临床应用。未来研究应采用前瞻性多中心方法，利用大型公共数据库和联合模型以更准确检测ECE。放射组学结合临床与AI的算法，有望开发出更精确的预测模型，帮助改善手术规划并提升前列腺癌患者的预后。PROSPERO编号：CRD42021272088。发表链接：https://doi.org/10.1136/bmjopen-2021-052342 。放射组学可以从MRI中提取诊断特征，提升前列腺癌诊断能力。联合模型在检测ECE方面优于单一放射组学签名。目前，放射组学在PCa患者中检测包膜外扩展的可靠性仍待提高。

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.