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

Qinqin Ma; Zhifan Li; Wenjing Li; Qitian Chen; Xinran Liu; Wen Feng; Junqiang Lei

doi:10.1016/j.ejrad.2023.111127

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

Eur J Radiol. 2023 Nov:168:111127. doi: 10.1016/j.ejrad.2023.111127. Epub 2023 Sep 29.

Authors

Qinqin Ma¹, Zhifan Li², Wenjing Li³, Qitian Chen⁴, Xinran Liu⁵, Wen Feng⁶, Junqiang Lei⁷

Affiliations

¹ Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: 1689880146@qq.com.
² Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: lizhf16@163.com.
³ Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: l15236636689@163.com.
⁴ No.2 Hospital of Baiyin City, Baiyin 730900, China. Electronic address: 14556687084@qq.com.
⁵ Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: xrliu17@lzu.edu.cn.
⁶ Lanzhou University, Lanzhou 730000, China; Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: 503608053@qq.com.
⁷ Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China; Intelligent Imaging Medical Engineering Research Center of Gansu Province, Lanzhou 730000, China; Accurate Image Collaborative Innovation International Science and Technology Cooperation Base of Gansu Province, Lanzhou 730000, China. Electronic address: leijq2011@126.com.

PMID: 37801997
DOI: 10.1016/j.ejrad.2023.111127

Abstract

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.

Keywords: Brest cancer; Lymphovascular invasion; Magnetic resonance imaging; Meta-analysis; Radiomic.

Publication types

Meta-Analysis

MeSH terms

Area Under Curve
Breast Neoplasms* / diagnostic imaging
Breast Neoplasms* / pathology
Breast Neoplasms* / surgery
Contrast Media
Female
Humans
Magnetic Resonance Imaging / methods
Preoperative Care / methods
Retrospective Studies

Substances

Contrast Media