MRI Images Based on Semiautomatic Segmentation Algorithm for Prediction of Histological Grade of Breast Tumor

Abstract

This study aimed to explore the effects of delayed enhanced magnetic resonance imaging (MRI) (DE-MRI) image features based on the semiautomatic circle-dependent (SCD)-based multiphase level set (MLS) (SCD-MLS) algorithm in predicting the histological grade of breast tumors. We explore the clinical effect of DE-MRI on the prediction of the histological grade of breast tumors (BTs). In this study, 264 breast tumor (BT) patients from The Second Affiliated Hospital of Soochow University were selected as the research objects, and DE-MRI examinations were performed. The SCD-MLS algorithm was compared with the Live-Wire algorithm for the segmentation effect and was applied to the DE-MRI image for prediction of the histological grade of BT. The results proved that the SCD-MLS algorithm in this study showed a more accurate segmentation effect with good stability. BT tissue showed a patchy low signal on T1WI and a high signal on T2WI. The number of patients with grade II was the highest (P < 0.05); the lesion diameters of grade II and grade III were concentrated at 1.5–2.5 cm and 2.5–3.5 cm, respectively (P < 0.05); the fractional anisotropy (FA) of patients with grade I was the largest (P < 0.05); and the apparent diffusion coefficient (ADC) of the patients with grade III was the highest (P < 0.05). In short, DE-MRI showed a good performance in the BT diagnosis, and the grading of BT tissue was related to lesion size, number of lesions, ADC, and FA.