MRI radiomics for the preoperative evaluation of lymphovascular invasion in breast cancer: A meta-analysis

医学 淋巴血管侵犯 置信区间 诊断优势比 荟萃分析 无线电技术 乳腺癌 子群分析 科克伦图书馆 乳房磁振造影 优势比 放射科 内科学 肿瘤科 癌症 转移 乳腺摄影术
作者
Qinqin Ma,Zhifan Li,Wenjing Li,Qitian Chen,Xinran Liu,Wen Feng,Junqiang Lei
出处
期刊:European Journal of Radiology [Elsevier BV]
卷期号:168: 111127-111127 被引量:8
标识
DOI:10.1016/j.ejrad.2023.111127
摘要

Purpose To evaluate the ability of preoperative MRI-based radiomic features in predicting lymphovascular invasion (LVI) in patients with breast cancer. Methods PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases were searched to identify relevant studies published up until June 15, 2023. Two reviewers screened all papers independently for eligibility. We included diagnostic accuracy studies that used radiomics-MRI for LVI in patients with breast 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 breast cancer. Spearman’s correlation coefficient was calculated and subgroup analysis performed to investigate causes of heterogeneity. Results Eight studies comprising 1685 female patients were included. The pooled DOR, sensitivity, specificity, and AUC of radiomics in detecting LVI were 23 [confidence interval (CI) 16,32], 0.89(0.86,0.92), 0.82 (0.78,0.86), and 0.83(0.78,0.87), respectively. The meta-analysis showed significant heterogeneity among the included studies. No threshold effect was detected. Subgroup analysis showed that more than 200 participants, radiomics with clinical factors, semiautomatic segmentation method and peritumoral or intra- and peritumoral model [DOR: 28(18,42), 26(19,37), 34(16,70), 40(10,156), respectively] could improve diagnostic performance compared with less than 200 participants, only radiomics, manual segmentation method, and tumor model [DOR: 16(7,37), 21(6,73), 20(12,32), 21(13,32), respectively], but 3.0 T MR and multiple sequences approach [DOR: 27(15,49),17(8,35)] couldn’t improve diagnostic performance compared with 1.5 T and DCE radiomic features [DOR:27(7,99),25(17,37)]. Conclusion Our meta-analysis showed that preoperative MRI-based radiomic features performs well in predicting LVI in patients with breast cancer. This noninvasive and convenient tool may be used to facilitate preoperative identification of LVI in breast cancer.
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