Deep learning model for the diagnosis of breast cancers smaller than 1 cm with ultrasonography: integration of ultrasonography and clinical factors

BACKGROUND: The aim of this study was to evaluate the diagnostic performance of a deep learning (DL) algorithm for breast masses smaller than 1 cm on ultrasonography (US). We also evaluated a hybrid model that combines the predictions of the DL algorithm from US images and a patient’s clinical factors including age, family history of breast cancer, BRCA mutation, and mammographic breast density. METHODS: A total of 1,041 US images (including 633 benign and 408 malignant masses) were obtained from 1,041 patients who underwent US between January 2014 and June 2021. All US images were randomly divided into training (513 benign and 288 malignant lesions), validation (60 benign and 60 malignant lesions), and test (60 benign and 60 malignant lesions) data sets. A mask region-based convolutional neural network (R-CNN) was used to generate a feature map of the input image with a CNN and a pre-trained ResNet101 structure. For the clinical model, the multilayer perceptron (MLP) structure was used to calculate the likelihood that the tumor was benign or malignant from the clinical risk factors. We compared the diagnostic performance of an image-based DL algorithm, a combined model with regression, and a combined model with the decision tree method. RESULTS: Using the US images, the area under the receiver operating characteristics curve (AUROC) of the DL algorithm was 0.85 [95% confidence interval (CI), 0.78–0.92]. With the combined model using a regression model, the sensitivity was 78.3% (95% CI, 67.9–88.8%) and the specificity was 85% (95% CI, 76–94%). The sensitivity of the combined model using a regression model was significantly higher than that of the imaging model (P=0.003). The specificity values of the two models were not significantly different (P=0.083). The sensitivity and specificity of the combined model using a decision tree model were 75% (95% CI, 62.1–85.3%) and 91.7% (95% CI, 81.6–97.2%), respectively. The sensitivity of the combined model using the decision tree model was higher than that of the image model but the difference was not statistically significant (P=0.081). The specificity values of the two models were not significantly different (P=0.748). CONCLUSIONS: The DL model could feasibly be used to predict breast cancers smaller than 1 cm. The combined model using clinical factors outperformed the standalone US-based DL model.