Department of Radiology, Jiaxing Maternity and Child Health Care Hospital, Jiaxing, Zhejiang 314000, China.
Department of Radiology, Wenzhou Central Hospital, Wenzhou, Zhejiang 325000, China.
Clin Radiol. 2024 Nov;79(11):e1372-e1382. doi: 10.1016/j.crad.2024.07.018. Epub 2024 Aug 7.
Aimed to evaluate the diagnostic performance of preoperative MRI-based radiomic models for noninvasive prediction of lymphovascular space invasion (LVSI) in patients with cervical cancer (CC).
A systematic search of the PubMed, Embase, Web of Science, and Cochrane databases was conducted up to December 21, 2023. The quality of the studies was assessed utilizing the Radiomics Quality Score (RQS) system and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled estimates of sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) were computed. The clinical utility was evaluated using the Fagan nomogram. Heterogeneity was investigated and subgroup analyses were conducted.
Eleven studies were included, with nine studies reporting independent validation sets. In the training sets, the pooled sensitivity, specificity, DOR, and AUC of SROC were 0.81 (95% CI: 0.75-0.85), 0.78 (95% CI: 0.73-0.83), 15 (95% CI: 11-20), and 0.86 (95% CI: 0.79-0.92), respectively. For the validation sets, the overall sensitivity, specificity, DOR, and AUC of SROC were 0.79 (95% CI: 0.73-0.84), 0.73 (95% CI: 0.67-0.78), 10 (95% CI: 7-15), and 0.83 (95% CI: 0.71-0.91), respectively. The Fagan nomogram indicated good clinical utility. Subgroup analysis revealed that multi-sequence MRI-based models exhibited superior diagnostic performance compared to single-sequence MRI-based models in validation sets.
This meta-analysis highlights the potential diagnostic efficacy of MRI-based radiomic models for predicting LVSI in CC. Nevertheless, large-sample, multicenter studies are still warranted, and improvements in the standardization of radiomics methodology are necessary.
旨在评估术前基于 MRI 的放射组学模型在预测宫颈癌(CC)患者淋巴血管空间侵犯(LVSI)中的非侵入性诊断性能。
系统检索了 PubMed、Embase、Web of Science 和 Cochrane 数据库,检索截至 2023 年 12 月 21 日。使用放射组学质量评分(RQS)系统和诊断准确性研究的质量评估(QUADAS-2)工具评估研究质量。计算汇总受试者工作特征曲线(SROC)的敏感性、特异性、诊断优势比(DOR)和曲线下面积(AUC)的合并估计值。使用 Fagan 列线图评估临床实用性。调查了异质性并进行了亚组分析。
纳入了 11 项研究,其中 9 项研究报告了独立的验证集。在训练集中,SROC 的合并敏感性、特异性、DOR 和 AUC 分别为 0.81(95%CI:0.75-0.85)、0.78(95%CI:0.73-0.83)、15(95%CI:11-20)和 0.86(95%CI:0.79-0.92)。对于验证集,SROC 的总体敏感性、特异性、DOR 和 AUC 分别为 0.79(95%CI:0.73-0.84)、0.73(95%CI:0.67-0.78)、10(95%CI:7-15)和 0.83(95%CI:0.71-0.91)。Fagan 列线图表明具有良好的临床实用性。亚组分析显示,多序列 MRI 基模型在验证集中的诊断性能优于单序列 MRI 基模型。
本荟萃分析强调了基于 MRI 的放射组学模型在预测 CC 中 LVSI 方面的潜在诊断效能。然而,仍需要大样本、多中心研究,并需要改进放射组学方法的标准化。