目的:本系统综述和荟萃分析的目的是评估基于MRI的影像组学预测乳腺癌中Ki-67表达的质量和诊断准确性。
方法:进行了系统的文献检索,以查找发表在不同数据库中的相关研究,包括PubMed,WebofScience,和Embase直到2023年3月10日。所有论文均由两名审阅者独立评估合格性。匹配研究问题并为定量综合提供足够数据的研究包括在系统评价和荟萃分析中,分别。使用诊断准确性研究质量评估2(QUADAS-2)和影像组学质量评分(RQS)工具评估文章的质量。使用合并敏感性(SEN)评估基于MRI的影像组学对乳腺癌患者Ki-67抗原的预测价值。特异性,和曲线下面积(AUC)。进行Meta回归以探讨异质性的原因。不同的协变量用于亚组分析。
结果:31项研究纳入系统评价;其中,21报告了足够的荟萃分析数据。分别汇集20个训练队列和5个验证队列。汇集的敏感性,特异性,基于MRI的影像组学预测训练队列中Ki-67表达的AUC为0.80[95%CI,0.73-0.86],0.82[95%CI,0.78-0.86],和0.88[95CI,0.85-0.91],分别。验证队列的相应值为0.81[95%CI,0.72-0.87],0.73[95%CI,0.62-0.82],和0.84[95CI,0.80-0.87],分别。基于QUADAS-2,在参考标准以及流量和时间域中检测到一些偏倚风险。然而,所含物品的质量可以接受。纳入的文章的平均RQS得分接近6,相当于最大可能得分的16.6%。在训练队列的合并敏感性和特异性中观察到显著的异质性(I2>75%)。我们发现使用深度学习放射学方法,磁场强度(3Tvs.1.5T),扫描仪制造商,感兴趣区域结构(2D与3D),组织取样路线,Ki-67截止,用于模型构建的逻辑回归,根据我们的联合模型分析,用于特征减少的LASSO以及用于特征提取的PyRadiomics软件对异质性有很大影响。在使用基于深度学习的影像组学和多个MRI序列的研究中的诊断性能(例如,DWI+DCE)略高。此外,来自DWI序列的影像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漏斗图没有发现发表偏倚。敏感性分析显示,逐一排除每项研究并不影响总体结果。
结论:这项荟萃分析显示,基于MRI的影像组学在区分Ki-67高表达乳腺癌患者和低表达乳腺癌患者方面具有良好的诊断准确性。然而,这些方法的灵敏度和特异性仍未超过90%,限制它们用作当前病理评估的补充(例如,活检或手术)以准确预测Ki-67表达。
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.