To explore the application value of a deep learning-based model for generating strain elastography images using breast grayscale ultrasound images.

A total of 1336 sets of ultrasound images of patients who underwent breast ultrasound examination at Xijing Hospital from May 2019 to June 2022 were retrospectively collected. A deep learning-based model for generating strain elastography images was constructed on the basis of generative adversarial network (GAN) in neural network. Then, 882 sets of images from the training set and 354 sets of images from the validation set were used to train and adjust the model, and 100 sets of images from the test set were additionally used to generate strain elastography images. The similarity was compared between the real elastography images and the generated elastography images. Four physicians of different seniority (2 senior and 2 junior) were selected to compare the differences between the two kinds of elastography images. Normalized cross-correlation (NCC) values were used to evaluate the similarity of the two kinds of elastography images and the authenticity score of reading physicians was obtained. Based on the Tsukuba's 5-point elastography scoring scale, Kappa test was applied to test the consistency of elastography scores of the four physicians. Receiver operating characteristic (ROC) curves were plotted to evaluate the performance of breast imaging reporting and data system (BI-RADS) classification in the diagnosis of benign and malignant breast lesions by different physicians based on the two kinds of elastography images.

The average NCC value for the similarity of the two kinds of elastography images in the test set was (0.70±0.08), with a median of 0.70 (range: 0.50 to 0.86). The average authenticity score of all the physicians was 0.49, with an average score of 0.45 for junior doctors and 0.53 for senior doctors, all of which were close to 0.50. The Kappa value of elastography scores of the four physicians was higher in the generated elastography image group than in the real elastography image group (Kappa values: 0.61 vs 0.57). There was no statistically significant difference in the area under ROC curve for each physician applying BI-RADS classification based on the two kinds of elastography images (P>0.05). Except for the specificity (P=0.0196) and positive predictive value (P=0.021) of one senior physician, there were no statistically significant differences in sensitivity, specificity, positive predictive value, or negative predictive value among other physicians (P>0.05).

The constructed deep learning-based model for generating strain elastography images using breast grayscale ultrasound images can generate elastography images similar to real elastography images, and the generated elastography images is comparable to real elastography in terms of diagnostic assistance.