Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging

[摘要] Background: The purpose of this study was to develop a deep learning-based system with a cascade feature pyramid network for the detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: 2 cm) and small (≤2 cm) breast lesions was further evaluated. Average precision (AP), mean AP, F1-score, sensitivity, and false positives were used to evaluate different systems. Cohen’s kappa scores were calculated to assess agreement between different systems, and Student’s t-test and the Holm-Bonferroni method were used to compare multiple groups. Results: The cascade feature pyramid network system outperformed the other systems with a mean AP and highest sensitivity of 0.826±0.051 and 0.970±0.014 (at 0.375 false positives), respectively. The F1-score of the cascade feature pyramid network in real detection was also superior to that of the other systems at both the slice and patient levels. The mean AP values of the cascade feature pyramid network reached 0.779±0.152 and 0.790±0.080 in detecting large and small lesions, respectively. Especially for small lesions, the cascade feature pyramid network achieved the best performance in detecting benign and malignant breast lesions at both the slice and patient levels. Conclusions: The deep learning-based system with the developed cascade feature pyramid network has the potential to detect and classify breast lesions on DCE-MRI, especially small lesions.

[学科分类] 外科医学

[关键词] Breast lesions;deep learning;dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI);detection;classification