本系统综述和荟萃分析的目的是评估基于 MRI 的放射组学预测乳腺癌中 Ki-67 表达的质量和诊断准确性。进行了系统文献检索以查找不同数据库（包括 PubMed）中发表的相关研究、Web of Science 和 Embase 截止日期为 2023 年 3 月 10 日。所有论文均由两名审稿人独立评估其资格。符合研究问题并为定量合成提供足够数据的研究分别纳入系统评价和荟萃分析。使用诊断准确性研究质量评估 2 (QUADAS-2) 和放射组学质量评分 (RQS) 工具评估文章的质量。使用汇总敏感性 (SEN)、特异性和曲线下面积 (AUC) 评估基于 MRI 的放射组学对乳腺癌患者 Ki-67 抗原的预测价值。进行荟萃回归来探讨异质性的原因。采用不同协变量进行亚组分析。系统评价纳入31项研究；其中，21 个报告了足够的数据进行荟萃分析。 20 个训练队列和 5 个验证队列分别合并。基于 MRI 的放射组学用于预测训练队列中 Ki-67 表达的汇总敏感性、特异性和 AUC 分别为 0.80 [95% CI, 0.73-0.86]、0.82 [95% CI, 0.78-0.86] 和 0.88 [95% CI，0.85-0.91]，分别。验证队列的相应值分别为 0.81 [95% CI, 0.72-0.87]、0.73 [95% CI, 0.62-0.82] 和 0.84 [95% CI, 0.80-0.87]。基于 QUADAS-2，检测到参考标准以及流量和时间域的一些偏差风险。然而，所收录文章的质量是可以接受的。纳入文章的平均 RQS 分数接近 6，相当于最大可能分数的 16.6%。在训练队列的汇总敏感性和特异性中观察到显着的异质性（I2 > 75%）。我们发现，使用深度学习放射组学方法、磁场强度（3 T 与 1.5 T）、扫描仪制造商、感兴趣区域结构（2D 与 3D）、组织采样路径、Ki-67 截止值、逻辑回归根据我们的联合模型分析，模型构建、用于特征缩减的 LASSO 以及用于特征提取的 PyRadiomics 软件对异质性有很大影响。使用基于深度学习的放射组学和多个 MRI 序列（例如 DWI DCE）的研究的诊断性能略高。此外，从 DWI 序列得出的放射组学特征在特异性和敏感性方面优于对比增强序列。根据 Deeks 的漏斗图，未发现发表偏倚。敏感性分析表明，逐一剔除每项研究并不影响整体结果。这项荟萃分析表明，基于 MRI 的放射组学在区分 Ki-67 高表达和低表达组的乳腺癌患者方面具有良好的诊断准确性。然而，这些方法的敏感性和特异性仍不超过90%，限制了它们用作当前病理评估（例如活检或手术）的补充以准确预测Ki-67表达。版权所有©2023 The Association of University放射科医生。由爱思唯尔公司出版。保留所有权利。

The purpose of this systematic review and meta-analysis was to assess the quality and diagnostic accuracy of MRI-based radiomics for predicting Ki-67 expression in breast cancer.A systematic literature search was performed to find relevant studies published in different databases, including PubMed, Web of Science, and Embase up until March 10, 2023. All papers were independently evaluated for eligibility by two reviewers. Studies that matched research questions and provided sufficient data for quantitative synthesis were included in the systematic review and meta-analysis, respectively. The quality of the articles was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. The predictive value of MRI-based radiomics for Ki-67 antigen in patients with breast cancer was assessed using pooled sensitivity (SEN), specificity, and area under the curve (AUC). Meta-regression was performed to explore the cause of heterogeneity. Different covariates were used for subgroup analysis.31 studies were included in the systematic review; among them, 21 reported sufficient data for meta-analysis. 20 training cohorts and five validation cohorts were pooled separately. The pooled sensitivity, specificity, and AUC of MRI-based radiomics for predicting Ki-67 expression in training cohorts were 0.80 [95% CI, 0.73-0.86], 0.82 [95% CI, 0.78-0.86], and 0.88 [95%CI, 0.85-0.91], respectively. The corresponding values for validation cohorts were 0.81 [95% CI, 0.72-0.87], 0.73 [95% CI, 0.62-0.82], and 0.84 [95%CI, 0.80-0.87], respectively. Based on QUADAS-2, some risks of bias were detected for reference standard and flow and timing domains. However, the quality of the included article was acceptable. The mean RQS score of the included articles was close to 6, corresponding to 16.6% of the maximum possible score. Significant heterogeneity was observed in pooled sensitivity and specificity of training cohorts (I2 > 75%). We found that using deep learning radiomic methods, magnetic field strength (3 T vs. 1.5 T), scanner manufacturer, region of interest structure (2D vs. 3D), route of tissue sampling, Ki-67 cut-off, logistic regression for model construction, and LASSO for feature reduction as well as PyRadiomics software for feature extraction had a great impact on heterogeneity according to our joint model analysis. Diagnostic performance in studies that used deep learning-based radiomics and multiple MRI sequences (e.g., DWI+DCE) was slightly higher. In addition, radiomic features derived from DWI sequences performed better than contrast-enhanced sequences in terms of specificity and sensitivity. No publication bias was found based on Deeks' funnel plot. Sensitivity analysis showed that eliminating every study one by one does not impact overall results.This meta-analysis showed that MRI-based radiomics has a good diagnostic accuracy in differentiating breast cancer patients with high Ki-67 expression from low-expressing groups. However, the sensitivity and specificity of these methods still do not surpass 90%, restricting them from being used as a supplement to current pathological assessments (e.g., biopsy or surgery) to predict Ki-67 expression accurately.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.