A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Issue 151 (June 2022) - Li, Longchao; Zhang, Jing; Zhe, Xia; Tang, Min; Zhang, Xiaoling; Lei, Xiaoyan; Zhang, Li
Highlights: Using MRI-based radiomic features can help improve the accuracy of predicting lymph node metastasis in cervical cancer patients. Radiomics combined with clinical factors exhibited a high diagnostic odds ratio (DOR) of 18.93. Of four types of sequence(T2 WI + DWI + DCE, T2 WI + DCE, T2 WI, ADC), multiple sequences (T2 WI + DWI + DCE) performed best (DOR = 15.49 vs. 7.63, 8.31, and 4.93) in discriminating LNM and non-LNM in cervical cancer. Despite the good predictive power of radiomics, the overall quality of the selected studies needs to be improved. Abstract: Purpose: To evaluate the ability of preoperative MRI-based radiomic features in predicting lymph node metastasis (LNM) in patients with cervical cancer. Methods: 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. Results: 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]. Conclusion: 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.