Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer.

Musu Ala,Jianlin Wu

doi:10.1155/2022/6557494

Abstract

The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was DSC (Dice similarity coefficient) = 0.9626, MPA (mean pixel accuracy) = 0.9676, and IOU (intersection over union) = 0.9155. When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, P > 0.05. After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics (P < 0.05). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.

Highlights

Mammary gland disease includes mammary gland inflammatory disease, benign pathological changes of mammary gland, and malignant tumor
When classifying whether patients had breast cancer (BC) lesions, compared with the classification of radiologists, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%
There are many types of molecular markers used in clinical diagnosis of BC, among which the main tumor markers are carbohydrate antigen 153, carbohydrate antigen 125, carcinoembryonic antigen, and HER2, whose expression plays a key role in treating BC [14–16]

Summary

Introduction

Mammary gland disease includes mammary gland inflammatory disease, benign pathological changes of mammary gland, and malignant tumor. Fat, clinically associated with breast tissue is caused by diseases such as bubonic collectively known as mammary gland disease. This kind of disease is divided into three categories, such as mammary gland inflammatory disease, benign pathological changes of mammary gland, and malignant tumor of mammary gland. These breast diseases will cause great harm to the physical and mental health of female patients. Late BC due to lesion invasion of nerve may appear breast pain

Results

Discussion

Conclusion