Abstract
This study aims to develop a machine learning model applied on digital mammograms to reduce unnecessary invasive biopsies for suspicious calcifications classified as BI-RADS category 4. This study retrospectively analyzed data from 372 female patients with pathologically confirmed BI-RADS category 4 mammographic calcifications. Patients from the First Affiliated Hospital of Bengbu Medical College (n = 275) were divided chronologically into a training and internal validation set. An external validation set (n = 97) was recruited from Tongde Hospital of Zhejiang Province. We first segmented calcifications using nnUnet, and then built a radiomics model and deep learning model, respectively. Finally, we used an information fusion method to combine the results of the two models to obtain the final prediction. The different models, including the radiomics model, the deep learning model, and the fusion model, were evaluated on the validation set from two hospitals. In the external validation set, the radiomics model yielded an AUC of 0.883 (95% CI, 0.802-0.939), a sensitivity of 0.921, and a specificity of 0.735, and the deep learning model yielded an AUC of 0.873 (95% CI, 0.789-0.932), a sensitivity of 0.905, and a specificity of 0.853. The fusion model achieved an AUC of 0.947 (95% CI, 0.882-0.982), sensitivity of 0.825, and specificity of 0.941 in the external validation set. The fusion model has the potential to reduce the need for invasive biopsies of benign mammographic calcifications classified as BI-RADS category 4, without sacrificing the diagnostic accuracy for malignant cases.