Deep Learning-Based DCE-MRI Automatic Segmentation in Predicting Lesion Nature in BI-RADS Category 4.

ABSTRACT: To investigate whether automatic segmentation based on DCE-MRI with a deep learning (DL) algorithm enabled advantages over manual segmentation in differentiating BI-RADS 4 breast lesions. A total of 197 patients with suspicious breast lesions from two medical centers were enrolled in this study. Patients treated at the First Hospital of Qinhuangdao between January 2018 and April 2024 were included as the training set (n = 138). Patients treated at Lanzhou University Second Hospital were assigned to an external validation set (n = 59). Areas of suspicious lesions were delineated based on DL automatic segmentation and manual segmentation, and evaluated consistency through the Dice correlation coefficient. Radiomics models were constructed based on DL and manual segmentations to predict the n

SUBMITTER: Liu T 

PROVIDER: S-EPMC12344054 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature