Automated breast ultrasound features associated with diagnostic performance of a multiview convolutional neural network according to the level of experience of radiologists

Abstract

Purpose To investigate automated breast ultrasound (ABUS) features affecting the use of a multiview convolutional neural network (CNN) for breast lesions according to the level of experience of radiologists. Materials and Methods A total of 656 breast lesions (152 malignant and 504 benign lesions) were included and reviewed by 6 radiologists for background echotexture, glandular tissue component (GTC), and lesion type and size without as well as with a multiview CNN. The sensitivity, specificity, and the area under the receiver operating curve (AUC) for ABUS features were compared between 2 sessions according to the level of the radiologists&apos; experience. Results Radiology residents showed significant AUC improvement with the multiview CNN for mass (0.81-0.91, P =0.003) and non-mass lesions (0.56-0.90, P =0.007), all background echotextures (homogeneous-fat: 0.84-0.94, P =0.04; homogeneous-fibroglandular: 0.85-0.93, P =0.01; heterogeneous: 0.68-0.88, P =0.002), all GTC levels (minimal: 0.86-0.93, P =0.001; mild: 0.82-0.94, P =0.003; moderate: 0.75-0.88, P =0.01; marked: 0.68-0.89, P <0.001), and lesions <= 10mm (<= 5mm: 0.69-0.86, P <0.001; 6-10mm: 0.83-0.92, P <0.001). Breast specialists showed significant AUC improvement with the multiview CNN in heterogeneous echotexture (0.90-0.95, P =0.03), marked GTC (0.88-0.95, P <0.001), and lesions <= 10mm (<= 5mm: 0.89-0.93, P =0.02; 6-10mm: 0.95-0.98, P =0.01). Conclusion With the multiview CNN, ABUS performance among radiology residents was improved regardless of lesion type, background echotexture, or GTC. For breast lesions smaller than 10mm, both radiology residents and breast specialists achieved better ABUS performance.