The role of radiomics for predicting of lymph-vascular space invasion in cervical cancer patients based on artificial intelligence: a systematic review and meta-analysis
- Author:
Mengli ZHAO
1
;
Zhen LI
;
Xiaowei GU
;
Xiaojing YANG
;
Zhongrong GAO
;
Shanshan WANG
;
Jie FU
Author Information
- Publication Type:Review Article
- From:Journal of Gynecologic Oncology 2025;36(2):e26-
- CountryRepublic of Korea
- Language:English
- Abstract: The primary aim of this study was to conduct a methodical examination and assessment of the prognostic efficacy exhibited by magnetic resonance imaging (MRI)-derived radiomic models concerning the preoperative prediction of lymph-vascular space infiltration (LVSI) in cervical cancer cases. A comprehensive and thorough exploration of pertinent academic literature was undertaken by two investigators, employing the resources of the Embase, PubMed, Web of Science, and Cochrane Library databases. The scope of this research was bounded by a publication cutoff date of May 15, 2023. The inclusion criteria encompassed studies that utilized radiomic models based on MRI to prognosticate the accuracy of preoperative LVSI estimation in instances of cervical cancer. The Diagnostic Accuracy Studies-2 framework and the Radiomic Quality Score metric were employed. This investigation included nine distinct research studies, enrolling a total of 1,406 patients. The diagnostic performance metrics of MRI-based radiomic models in the prediction of preoperative LVSI among cervical cancer patients were determined as follows: sensitivity of 83% (95% confidence interval [CI]=77%–87%), specificity of 74% (95% CI=69%–79%), and a corresponding AUC of summary receiver operating characteristic measuring 0.86 (95% CI=0.82–0.88). The results of the synthesized meta-analysis did not reveal substantial heterogeneity. This meta-analysis suggests the robust diagnostic proficiency of the MRI-based radiomic model in the prognostication of preoperative LVSI within the cohort of cervical cancer patients. In the future, radiomics holds the potential to emerge as a widely applicable noninvasive modality for the early detection of LVSI in the context of cervical cancer.
