Li Longchao, Zhang Jing, Zhe Xia, Tang Min, Zhang Xiaoling, Lei Xiaoyan, Zhang Li
Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi 710000, China.
Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi 710000, China.
Eur J Radiol. 2022 Jun;151:110243. doi: 10.1016/j.ejrad.2022.110243. Epub 2022 Mar 9.
To evaluate the ability of preoperative MRI-based radiomic features in predicting lymph node metastasis (LNM) in patients with cervical cancer.
PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases were searched to identify relevant studies published up until October 22, 2021. Two reviewers screened all papers independently for eligibility. We included diagnostic accuracy studies that used radiomics-MRI for LNM in patients with cervical cancer, using histopathology as the reference standard.Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Overall diagnostic odds ratio (DOR), sensitivity, specificity and area under the curve (AUC) were calculated to assess the prediction efficacy of MRI-based radiomic features in patients with cervical cancer. Spearman's correlation coefficient was calculated and subgroup analysis performed to investigate causes of heterogeneity.
Twelve studies comprising 793 female patients were included. The pooled DOR, sensitivity, specificity, and AUC of radiomics in detecting LNM were 12.08 [confidence interval (CI) 8.18, 17.85], 80% (72%, 87%), 76% (72%, 80%), and 0.83 (0.76, 0.89), respectively. The meta-analysis showed significant heterogeneity among the included studies. No threshold effect was detected. Subgroup analysis showed that multiple sequences, and radiomics combined with clinical factors, radiomics approach [DOR:15.49 (6.06, 39.62), 18.93 (8.46, 42.38), and 10.63 (6.23, 18.12), respectively] could slightly improve diagnostic performance compared with apparent diffusion coefficient-based radiomic features, T2 + dynamic contrast-enhanced MRI-based radiomic features, T2 images-based radiomic features, single radiomics, and human reading [DOR: 4.9 (1.91, 12.74), 7.63 (3.78, 15.38), 8.31 (3.05, 22.61), 16.10 (9.10, 28.47), and 6.46 (3.08, 13.56), respectively].
Our meta-analysis showed that preoperative MRI-based radiomic features performs well in predicting LNM in patients with cervical cancer. This noninvasive and convenient tool may be used to facilitate preoperative identification of LNM.
评估基于术前MRI的放射组学特征预测宫颈癌患者淋巴结转移（LNM）的能力。
检索PubMed、Embase、Web of Science、Cochrane图书馆数据库以及四个中文数据库，以识别截至2021年10月22日发表的相关研究。两名评审员独立筛选所有论文的合格性。我们纳入了以组织病理学为参考标准，使用放射组学-MRI评估宫颈癌患者LNM的诊断准确性研究。使用诊断准确性研究质量评估2和放射组学质量评分评估质量。计算总体诊断比值比（DOR）、敏感性、特异性和曲线下面积（AUC），以评估基于MRI的放射组学特征对宫颈癌患者的预测效能。计算Spearman相关系数并进行亚组分析，以研究异质性的原因。
纳入了12项研究，共793例女性患者。放射组学检测LNM的合并DOR、敏感性、特异性和AUC分别为12.08 [置信区间（CI）8.18, 17.85]、80%（72%，87%）、76%（72%，80%）和0.83（0.76, 0.89）。荟萃分析显示纳入的研究之间存在显著异质性。未检测到阈值效应。亚组分析表明，与基于表观扩散系数的放射组学特征、基于T2 + 动态对比增强MRI的放射组学特征、基于T2图像的放射组学特征、单一放射组学和人工判读相比，多序列以及放射组学与临床因素相结合、放射组学方法[DOR分别为：15.49（6.06, 39.62）、18.93（8.46, 42.38）和10.63（6.23, 18.12）]可略微提高诊断性能[DOR分别为：4.9（1.91, 12.74）、7.63（3.78, 15.38）、8.31（3.05, 22.61）、16.10（9.10, 28.47）和6.46（3.08, 13.56）]。
我们的荟萃分析表明，基于术前MRI的放射组学特征在预测宫颈癌患者LNM方面表现良好。这种无创且便捷的工具可用于促进术前LNM的识别。
