Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

Shuang Liu,Feng Wei,Min Tang,Jiaxi Lu,Shuqin Ruan,Gustavo Ramirez

doi:10.1155/2021/5289128

Abstract

This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P < 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P < 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P < 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

Highlights

As the most common malignant tumor in Chinese women, the incidence of breast cancer is increasing year by year
Clinical diagnosis of breast cancer mainly involves imaging examinations, including ultrasound imaging, mammography (MAM), and breast magnetic resonance imaging (MRI) [6]. e principle of ultrasonic imaging diagnosis mainly uses the characteristics of ultrasonic wave, such as rapid-fire, reflection and refraction, and ultrasonic attenuation. is imaging is effective for the detection of substantive and cystic masses in the breast and can reflect the situation of axillary lymph nodes and surrounding tissues
One-way analysis of variance was used for pairwise comparison. e difference was statistically considerable with P < 0.05

Summary

Introduction

As the most common malignant tumor in Chinese women, the incidence of breast cancer is increasing year by year. The medical community does not have the same general idea about the pathogenesis of breast cancer due to the uncertainty of its pathogenic factors and the unclear dominance of early clinical features. Erefore, most of the diagnosed patients have reached the middle and advanced stage, missing the best treatment opportunity for the disease [2, 3]. Clinical diagnosis of breast cancer mainly involves imaging examinations, including ultrasound imaging, mammography (MAM), and breast magnetic resonance imaging (MRI) [6]. E principle of ultrasonic imaging diagnosis mainly uses the characteristics of ultrasonic wave, such as rapid-fire, reflection and refraction, and ultrasonic attenuation. Is imaging is effective for the detection of substantive and cystic masses in the breast and can reflect the situation of axillary lymph nodes and surrounding tissues.

Methods

Results

Discussion

Conclusion