AUTHOR=Zhang Jing , Li Longchao , Zhe Xia , Tang Min , Zhang Xiaoling , Lei Xiaoyan , Zhang Li 

TITLE=The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.799209

DOI=10.3389/fonc.2022.799209

ISSN=2234-943X

ABSTRACT=Background: In recent years, radiomics and machine learning (ML) applications have become increasingly popular for predicting axillary lymph node metastasis (ALNM) in breast cancer, and research results have also varied from study to study. Thus, it is necessary to perform a meta‐analysis to evaluate the diagnostic performance of ML-based radiomics of DCE-MRI in predicting ALNM in breast cancer.
Methods: English and Chinese databases were searched for original studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) were used to assess the methodological quality of the included studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were used to summarize the diagnostic accuracy. Spearman's correlation coefficient and subgroup analysis were performed to investigate the cause of the heterogeneity.
Results: Thirteen studies (1618 participants) were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.82 (0.75, 0.87), 0.83 (0.74, 0.89), 21.56 (10.60, 43.85), and 0.89 (0.86, 0.91), respectively. The meta-analysis showed significant heterogeneity among the included studies. There was no threshold effect in the test. The result of subgroup analysis showed that ML, 3.0 T, area of interest comprising the ALN, being manually drawn, using biopsy as gold standard, and only including ALN could slightly improve diagnostic performance compared to deep learning, 1.5 T, area of interest comprising the breast tumor, semiautomatic scanning, pathology, and the sentinel lymph node, respectively.
Conclusions: ML-based radiomics of DCE-MRI has the potential to predict ALNM accurately. The heterogeneity of the ALNM diagnoses included between the studies is a major limitation.