Chongshuang Yang ¹ Min Wu ^1* Jiancheng Zhang ^1* Hongwei Qian ^1* Xiangyang Fu ^1,2* Jing Yang ^1* Yingbin Luo ^1* Zhihong Qin ^1* Tianliang Shi ^1*

• ¹ Department of Radiology, Tongren People's Hospital,, Tongren, China
• ² Department of Radiology, Wanshan District People's Hospital,, Tongren, China

The objective of this meta-analysis is to assess the efficacy of radiomics techniques utilizing Magnetic Resonance Imaging (MRI) for predicting Lymphovascular Space Invasion (LVSI) in patients with Cervical Cancer (CC).Methods: A comprehensive literature search was conducted in databases including PubMed, Embase, Cochrane Library, Medline, Scopus, CNKI, and Wanfang, with studies published up to April 8, 2024, being considered for inclusion. The meta-analysis was performed using Stata 15 and Review Manager 5.4. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score tools. The analysis encompassed the pooled sensitivity, specificity, Positive Likelihood Ratio (PLR), Negative Likelihood Ratio (NLR), and Diagnostic Odds Ratio (DOR). Summary ROC curves were constructed, and the AUC was calculated. Heterogeneity was investigated using meta-regression. Statistical significance was set at P ≤ 0.05. Results: Thirteen studies involving a total of 2,245 patients were included in the meta-analysis. The overall sensitivity and specificity of the MRI-based model in the Training set were 83% (95%CI: 77%-87%) and 72% (95%CI: 74%-88%), respectively. The AUC, DOR, PLR, and NLR of the MRI-based model in the Training set were 0.89 (95%CI: 0.86-0.91), 22 (95%CI: 12-40), 4.6 (95%CI: 3.1-7.0), and 0.21 (95%CI: 0.16-0.29), respectively. Subgroup analysis revealed that the AUC of the model combining radiomics with clinical factors [0.90 (95%CI: 0.87-0.93)] was superior to models based on T2-weighted Imaging (T2WI) sequence [0.78 (95%CI: 0.74-0.81)], contrast-enhanced T1-weighted Imaging (T1WI-CE) sequence [0.85 (95%CI: 0.82-0.88)], and multiple sequences [0.86 (95%CI: 0.82-0.89)] in the Training set. The pooled sensitivity and specificity of the model integrating radiomics with clinical factors [83% (95%CI: 73%-89%) and 86% (95%CI: 73%-93%)] surpassed those of models based on T2WI sequence [79% (95%CI:71%-85%) and 72% (95%CI: 67%-76%)], T1WI-CE sequence [78% (95%CI: 67%-86%) and 78% (95%CI: 68%-86%)], and multiple sequences [78% (95%CI: 67%-87%) and 79% (95%CI: 2 70%-87%)], respectively. Funnel plot analysis indicated an absence of publication bias (P > 0.05).LVSI in CC patients. The diagnostic performance of models combing radiomics and clinical factors is superior to that of models utilizing radiomics alone.