Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis

Li, Zhenkai; Ye, Juan; Du, Hongdi; Cao, Ying; Wang, Ying; Liu, Desen; Zhu, Feng; Shen, Hailin

doi:10.3389/fonc.2022.837257

Cited by 5 publications

(1 citation statement)

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“…In addition, we found significant heterogeneity in the pooled sensitivity and specificity. This observation is similar to that in published meta-analyses of diagnostic accuracy using radiomics (49)(50)(51)(52). Although radiomic models help predict the diagnosis of LNM in breast cancer, they involve numerous analysis methods.…”

Section: Discussionsupporting

confidence: 85%

Diagnostic performance of radiomics in predicting axillary lymph node metastasis in breast cancer: A systematic review and meta-analysis

et al. 2022

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BackgroundThis study aimed to perform a meta‐analysis to evaluate the diagnostic performance of radiomics in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis (SLNM) in breast cancer.Materials and methodsMultiple electronic databases were systematically searched to identify relevant studies published before April 29, 2022: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, and Wanfang Data. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The overall diagnostic odds ratio (DOR), sensitivity, specificity, and area under the curve (AUC) were calculated to evaluate the diagnostic performance of radiomic features for lymph node metastasis (LNM) in patients with breast cancer. Spearman’s correlation coefficient was determined to assess the threshold effect, and meta-regression and subgroup analyses were performed to explore the possible causes of heterogeneity.ResultsA total of 30 studies with 5611 patients were included in the meta-analysis. Pooled estimates suggesting overall diagnostic accuracy of radiomics in detecting LNM were determined: DOR, 23 (95% CI, 16-33); sensitivity, 0.86 (95% CI, 0.82-0.88); specificity, 0.79 (95% CI, 0.73-0.84); and AUC, 0.90 (95% CI, 0.87-0.92). The meta-analysis showed significant heterogeneity between sensitivity and specificity across the included studies, with no evidence for a threshold effect. Meta-regression and subgroup analyses showed that combined clinical factors, modeling method, region, and imaging modality (magnetic resonance imaging [MRI], ultrasound, computed tomography [CT], and X-ray mammography [MMG]) contributed to the heterogeneity in the sensitivity analysis (P < 0.05). Furthermore, modeling methods, MRI, and MMG contributed to the heterogeneity in the specificity analysis (P < 0.05).ConclusionOur results show that radiomics has good diagnostic performance in predicting ALNM and SLNM in breast cancer. Thus, we propose this approach as a clinical method for the preoperative identification of LNM.

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Section: Discussionsupporting

confidence: 85%