Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China.
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China. [email protected].
- Department of Radiology, Obstetrics & Gynecology Hospital of Fudan University, Yangtze River Delta Integration Demonstration Zone (QingPu), Shanghai, China. [email protected].
Abstract
Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can increase the degree of clinical benefit. The present study aimed to investigate the diagnostic performance of magnetic resonance imaging (MRI)-derived artificial intelligence for accurate preoperative assessment of the invasive risk. The PubMed, Embase, Cochrane Library and Web of Science databases were searched, and pertinent English-language papers were collected. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR, respectively) of all the papers were calculated using Stata software. The results were plotted on a summary receiver operating characteristic (SROC) curve, publication bias and threshold effects were evaluated, and meta-regression and subgroup analyses were conducted to explore the possible causes of intratumoral heterogeneity. MRI-based radiomics revealed pooled sensitivity (SEN) and specificity (SPE) values of 0.85 and 0.82 for the prediction of high-grade EC; 0.80 and 0.85 for deep myometrial invasion (DMI); 0.85 and 0.73 for lymphovascular space invasion (LVSI); 0.79 and 0.85 for microsatellite instability (MSI); and 0.90 and 0.72 for lymph node metastasis (LNM), respectively. For LVSI prediction and high-grade histological analysis, meta-regression revealed that the image segmentation and MRI-based radiomics modeling contributed to heterogeneity (p = 0.003 and 0.04). Through a systematic review and meta-analysis of the reported literature, preoperative MRI-derived ML could help clinicians accurately evaluate EC risk factors, potentially guiding individual treatment thereafter.