使用临床列线图预测前列腺癌 (PCa) 的前列腺外扩展 (EPE)。尽管灵敏度和标准化较差代表了尚未解决的问题，但纳入 MRI 可能代表着一个飞跃。 MRI 放射组学已被提议用于 EPE 预测。本研究的目的是系统回顾文献，并对基于 MRI 的 EPE 预测放射组学方法进行荟萃分析。系统检索了多个数据库，以查找截至 2022 年 6 月的 EPE 检测放射组学研究。方法学质量根据质量进行评估诊断准确性研究评估 2 (QUADAS-2) 工具和放射组学质量评分 (RQS)。合并受试者工作特征曲线下面积 (AUC) 以估计预测准确性。随机效应模型估计总体效应大小。用 I2 值评估统计异质性。用漏斗图评估发表偏倚。进行亚组分析以探讨异质性。纳入了 13 项研究，显示研究设计和方法学质量的局限性（中位 RQS 10/36），且统计异质性较高。 EPE 鉴定的汇总 AUC 为 0.80。在亚组分析中，基于测试集和交叉验证的研究汇总的 AUC 分别为 0.85 和 0.89。基于深度学习 (DL) 的汇总 AUC 为 0.72，手工放射组学研究的汇总 AUC 为 0.82，使用多个和单个扫描仪数据的研究分别为 0.79 和 0.83。最后，使用放射组学特征获得的具有最佳预测性能的模型显示汇总 AUC 为 0.82，而那些包含 0.76.MRI 放射组学驱动模型的临床数据来识别 PCa 中 EPE 的模型总体上显示出有希望的预测性能。然而，仍然需要方法论上可靠的、临床驱动的研究来评估其诊断和治疗影响。放射组学可能会改善前列腺癌患者的管理，增加 MRI 在评估前列腺外扩散方面的价值。然而，未来的研究必须优先考虑确认研究和更强的临床方向，以巩固这些进展。• MRI 放射组学作为克服 MRI 在前列腺癌局部分期中局限性的工具，值得关注。 • 13 项纳入研究的汇总 AUC 为 0.80，异质性较高（84.7%，p< .001）、方法学问题和临床导向较差。 • 方法学上稳健的放射组学研究需要重点关注提高 MRI 敏感性并为患者层面的临床列线图带来附加值。© 2023。作者。

Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction.Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity.Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76.MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed.Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements.• MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.© 2023. The Author(s).