# Magnetic Resonance Imaging Features in Diagnosis of Breast Cancer and Evaluation of Effect of Epidermal Growth Factor Receptor-Targeted Therapy

Authors: Hao Hao Yang; Fang Fang Zhang
Journal: BioMed Research International
 (2022)

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## Abstract

Breast cancer diagnosis and treatment are important healthcare issues in the Industrialized World. In this study, magnetic resonance imaging (MRI) images are used to diagnose breast cancer (BC) and analyze the application effect of epidermal factor receptor-targeted therapy in the treatment of BC. A total of 858 patients diagnosed with BC in Jincheng People’s Hospital from 2019 to 2021 are included and randomly divided into an experimental group and a control group. The experimental group is treated with epidermal growth factor receptor- (EGFR-) targeted therapy, and the control group is treated with conventional chemotherapy according to subsequent treatment modalities. Experimental results show that compared with manual segmentation, machine automatic segmentation includes the local and edge information of the image with higher accuracy, compared with benign and malignant tumors. There are significant differences in the changes of Tpeak, SSmax, El,E2, and E5, P<0.05, with SSmax and El having the greatest changes. After chemotherapy, the recorded maximum diameter of cancer foci in the control and the experimental groups are 26.4±11.6 mm and 20.3±13.5 mm, respectively, and the difference is statistically meaningful (P<0.05). The ADC value (12.74±2.08) in the experimental group is higher than that (9.7±1.88) in the control group (P<0.05). There is a significant difference in SSmax between the control group and experimental group (P<0.05), the SImax, pH values of the control group are significantly higher than those of the experimental group (P<0.05), and the SSmaxR value (7.82±6.24) in the experimental group is lower than that in the control group (10.08±6.25), but the difference is not significant, P>0.05. The proposed MRI method has high sensitivity and accuracy in the diagnosis of BC, improving the detection rate of lesions, compared with conventional chemotherapy. In addition, epidermal factor receptor-targeted therapy has a better therapeutic effect, with significant changes in cancer foci, which has the value of clinical promotion.


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## Body

## 1. Introduction

Breast cancer (BC) is usually an abnormal status of breast epithelial tissues. It is a kind of malignant tumor that women often suffer from [1]. In recent years, the number of BC patients has been on the rise. The incidence of BC in the world is about 1.2 million every year. About half of them will lose their lives. With the improvement of living standards, the prevalence of BC in China has been increasing. At this stage, it has become the leading disease in women’s malignant tumors. Every year, the number of people who die as a result of BC rises and shows a trend of gradually getting younger, posing a severe threat to the health and safety of patients, particularly women [2]. Early detection of BC can significantly improve the quality of life of patients. Once the distant metastasis occurs, the 5-year survival rate of patients will be significantly reduced. Especially in patients with advanced BC, the treatment effect is very poor. Therefore, timely detection, diagnosis, and treatment will play an important role in the prevention and treatment of BC in the clinic [3].At present, BC is mainly diagnosed by pathological examination or imaging examination [4]. A biopsy is a golden standard for the diagnosis of BC, but some in patients with tiny lesions, it is difficult to obtain biopsy tissue, and biopsy can also lead to paralytic fibrosis or even sclerotic mass of breast tissue [5]. Imaging studies are generally molybdenum target mammography, ultrasound imaging, and magnetic resonance imaging (MRI) [6–8]. Due to their different imaging principles, each examination method has its shortcomings. Molybdenum target mammography has the drawbacks of poor display of multicentric lesions, low sensitivity for the examination of dense breasts, possible masking lesions, difficulty in detecting lesions when they are located in higher and deeper sites, and insufficient estimation of the size, number, and range of masses. Therefore, it is mainly used for BC screening and is suitable for the screening of postmenopausal women and women with low dense breasts. Moreover, there is certain radiation [9]. Ultrasound examination is difficult to find microcalcification, and it is easy to miss diagnosis if there is no obvious mass. The detection rate has a certain correlation with the machine resolution, the technical level, and the knowledge background of operators. There are great differences in the judgment of diagnostic results, and there is a lack of unified objective standards [10]. With the advancement of technology, MRI has increasingly grown in importance as a technique of investigation [11]. The advantages of MRI include high soft-tissue resolution and the absence of radiation. It has several advantages when it comes to detecting breast cancer. It is especially suitable for patients who have difficulty in detecting or diagnosing lesions by breast X-ray and ultrasound and young patients who want breast-conserving surgery [12]. In addition to the diagnosis of breast diseases, MRI can also be applied to preoperative staging, prognosis, and evaluation of the efficacy of neoadjuvant chemotherapy in BC [13].Epidermal growth factor (EGF) is a single chain of 53 amino acids that phosphorylates the receptor and related effector proteins by specifically binding to the epidermal growth factor receptor (EGFR) on the cell membrane. This reaction will trigger accelerated DNA synthesis and gene transcription and produce an important effect on malignant transformation and proliferation of cells, vascular invasion, or neoplastic angiogenesis [14]. EGFR and its mutants have attracted people’s attention due to the characteristics of uncontrolled cell growth and salinization using signal transduction. Moradi-Kalbolandi et al. [15] have shown that 90% of tumors will have high-density EGFR expression on the surface, of which 54% of malignant tumors can bind to exogenous ligand EGF. Gonzalez-Conchas et al. [16] suggested that most BCs have increased and overexpressed the EGFR gene, which is positively correlated with histological grade and lymph node metastasis, and negatively correlated with tumor differentiation and estrogen receptor. High EGFR expression often indicates many invasive pathological features, with the tendency of early bone marrow micrometastasis and blood circulation metastasis, which has important prognostic value for BC. High-density EGFR expressed on the surface of BC cells can be a potential target for tumor radionuclide imaging or therapy [17].The breast was one of the first areas of the body to be imaged with magnetic resonance imaging (MRI) [8]. By the mid-1980s, however, the majority of researchers had determined that MRI had limited clinical utility in detecting or diagnosing breast cancer. Heywang and colleagues published [18] the first paper on the use of contrast agents in breast imaging. This research changed the concept and discovered that breast cancers, in comparison with normal breast tissue, were increased significantly with standard gadolinium contrast agents. Heywang’s findings were shortly followed by Kaiser and Zeitler [19], who used a different technique to find contrast-enhanced magnetic resonance images beneficial in breast cancer diagnosis.In this study, MRI imaging analysis is used to diagnose BC patients and compare them with the results of the pathological examination to explore its application value in the diagnosis of BC; some BC patients are treated with EGFR targeted therapy and compared with patients treated with conventional chemotherapy, and their therapeutic effects are detected by MRI to provide data and theoretical support for the diagnosis and treatment of BC in the future clinical practice.The rest of the manuscript is organized as follows: Section2 is about material and methods where the proposed method is illustrated in detail. In Section 3, the results are explained. Section 4 is about the discussion, and Section 5 concludes the manuscript.


## 2. Research Methods

### 2.1. Study Subjects

In this study, for data collection, a total of 1,186 female patients with or suspected breast tumor who received clinical palpation, molybdenum target examination, or color Doppler ultrasonography in Jincheng People’s Hospital from 2019 to 2021 were selected. All patients received preoperative MRI examination and needle biopsy after MRI examination. After screening, 858 patients were included (ages: 31~72 years old), with a mean age of51.42±10.22 years. The patients were divided into an experimental group and a control group. The experimental group was treated with EGFR-targeted therapy, and the control group was given conventional chemotherapy according to subsequent treatment modalities. All patients with a single mass of unilateral breast found by molybdenum target, ultrasound, and clinical examination; patients without any treatment; patients undergoing MRI after admission; and patients with pathological confirmation after breast MRI were included in the study, whereas patients without pathological confirmation; patients who underwent chemotherapy, surgery, and other related treatment; pregnant women; and lactating patients were excluded. All patients signed the informed consent form, and the trial process was approved by the ethics committee of Jincheng People’s Hospital.


### 2.2. MRI Scanning Method

For MRI scanning, the Siemens 3.0T superconducting magnetic resonance scanner was adopted. The patient was placed in a prone position with both breasts naturally drooped. The breast and axillary regions were scanned. After conventional triplane localization, transaxial T1WI, T2WI, T2WI-FS, and dynamic contrast-enhanced scans were performed. Field of view was 340 mm, slice thickness was 4 mm, and spacing was 0.8 mm; matrix:320×320. Gadopentetate meglumine (GD-DTPA), a contrast agent, was rapidly injected through the cubital vein at 0.2 mmol/kg at an injection rate of 3.0 mL/s, and 15 mL of normal saline was intravenously injected at the same rate to flush out the residual contrast agent. A phase was scanned before injection of the contrast agent, after which the body position was maintained, and other phases were scanned in the 20 s after injection of the contrast agent, with five consecutive phases acquired without interval [20, 21].


### 2.3. Images Postprocessing Method

The relevant software was applied on a Siemens image postprocessing workstation, while finding the region of interest (ROI) the necrosis, vascular, or cystic areas were avoided. When selecting the ROI, the area should be as large as possible. The time-signal intensity curve (TIC) of the lesion in the ROI was measured. Maximum signal intensity projection (MP) and multiplanar reformation were performed on the images to observe the shape of the lesion and its relationship with the surrounding vessels. The image quality, lesion characteristics, and lesion detection rate of each sequence were compared. The following parameters were measured by the TIC:(i)
Steepest slope (SSmax): the SSmax was computed as(1)SSmax%/s=SIend−SIprior×100%ISI0×Tend−Tprior,where SIend is the signal intensity at the highest point of the rapid rise segment of the curve, SIprior is the signal intensity value at the start point of the rapid rise segment, Tend and Tprior represent the time points corresponding to SIend and SIprior, respectively, and SI0 indicate the signal intensity value before enhancement. Moreover, the time to peak height (pH) (Tpeak) was also measured
(ii)
The maximum enhancement (Emax): it was computed as(2)Emax=SSmax−SLpreSLprc×100%(iii)
The rate of early intension (REI): the REI was computed(3)REI=SSmax1~3min−SIpreSIpre×100%(iv)
The signal enhancement rate at 1 ~ 5 minutes after enhancement(EN): it was calculated as(4)EN=SIn−SIpreSIpre×100%,where EN is the enhancement rate and SIpre and SIn are the signal intensities of the lesions before and after enhancement (n=l-5)
(v)
Measurement of tumor size: according to RECIST criteria [19], the cancer focus was determined on the early arterial images, its maximum diameter line (D) was measured 3 times on the layer with the largest extent of cancer focus, and the average value was calculated; for multi-focal cancer, the maximum diameter line of each cancer focus was measured, and its sum was calculated as the maximum diameter line of cancer focus. The reduction rate after chemotherapy was calculated as(5)ReductionRate:△D%=D1−D2D1×100%,whereD1 and D2 indicate the maximum diameter of cancer foci before and after chemotherapy, respectivelyEach ROI was approximately 20-30 mm2, and its ADC value was measured. The maximum signal intensity (SImax); the signal intensity value of the curve at the starting point (SI0); the pH, SSmax, and SSmax ratio (SSmaxR) were calculated. The equation was pH=SImax−SI0; SSmaxR=SIpostf−SIpref/SIpostn−SIpren. SIpost was the highest signal intensity, and Slpre was the lowest signal intensity during the scan period with the greatest signal rise, f was the lesion, and n was the normal tissue; SImax was the maximum value within the curve.
(vi)
Calculation of DWI diagnostic indicators: ADC value change rate was computed as(6)△ADC%=ADC1−ADC2ADC1×100%,where ADC1 and ADC2 indicate ADC values before and after chemotherapy, respectively.(vii)
Calculation of semiquantitative parameter diagnostic indicators [22]: the change rate of SImax and pH: △SImax%=Smax1−Smax2/SImax1×100%, Smax1, and SImax2 indicated the maximum signal intensity of cancer foci before and after chemotherapy; △pH%=pH1−PH2/PH1×100%, PH1, and PH2 indicated the PH value of cancer foci before and after chemotherapy


### 2.4. Automatic Segmentation Algorithm of MRI Image

In clinical practice, people are only interested in certain sections of the image received from imaging equipment, referred to as the target region. To analyze these target regions qualitatively or quantitatively in the process of diagnosis and visualization, it is necessary to separate them from the image by special techniques. The purpose of image segmentation is to achieve such a process [23]. The target detection algorithm based on machine learning is mainly used, while the traditional target detection algorithm depends on manual processing as shown in Figure 1.Figure 1
Flow chart of the traditional target detection method.Region-based convolutional neural network (RCNN) applied the deep learning method to the research of target detection for the first time, but the problems of long training time and slow image detection of RCNN still exist. Afterward, fast RCNN, as an improved algorithm of RCNN appeared, which greatly improved the training and detection speed of RCNN [24]. The process of the fast RCNN algorithm is shown in Figure 2.Figure 2
Flow chart of fast RCNN algorithm.The RCNN algorithm relies on massive high-dimensional features to generate classifiers and regressors, which requires a lot of computer memory. To solve this problem, fast RCNN creatively adopts a convolutional neural network (CNN) to realize the classification and location regression of candidate regions, avoiding the computational cost caused by training many linear support vector machines and linear regression models. To enable the CNN to deal with classification and regression multitask problems at the same time and share convolution parameters, fast RCNN designs a multitask loss function as given in(7)Le,r,hr,d=Lclsp,r+γr≥1Llochr,d,wherer and d are the category label and location label of the candidate region. Only when the target is the background, r is equal to 0; otherwise, it is greater than or equal to 1. e is the category of prediction, and hr is the location of the bounding box of prediction. r≥1 means when the candidate region is not the background, γ=1.Fast RCNN greatly speeds up the model training and image detection by optimizing the feature extraction of candidate regions. It also uses CNN to design the detector, which reduces the consumption of computing resources of the model and improves the detection accuracy.


### 2.5. Evaluation of Medical Image Segmentation

In the field of medical image processing, segmentation evaluation methods have also received a lot of attention, and some organizations or institutions have also put forward their segmentation evaluation system. Volume overlap error (VOE), relative volume difference (AVD), average symmetric surface distance (ASSD), the root mean square symmetric surface distance (RMS), and the maximum symmetric surface distance (MSSD) were evaluated [25].
(8)VOEM,N=Volseg∩VolrefVolseg∪Volref×100%,AVDM,N=Volseg−VolrefVolref×100%,ASSDM,N=∑m∈Mminn∈Ndistm,n+∑n∈Nminm∈Mdistm,nPM+PN,RMSM,N=∑m∈Mminn∈Ndistm,n2+∑n∈Nminm∈Mdistm,n2PM+PN,MSSDM,N=maxmaxm∈Mminn∈Ndistm,n,minm∈Mdistm,n,whereVolseg is the number of volumes in the segmentation result, Volref shows the number of volumes in the gold standard, distm,n indicates the distance between volumes m and n, M and N represent the segmentation results and the gold standard, and PM and PN are the segmentation results and the number of boundary volumes in the gold standard. Given various factors, the above five methods are selected for the evaluation index of the segmentation algorithm.


### 2.6. Statistical Methods

SPSS 22.0 statistical software was applied for data analysis. All data were expressed asmean±standarddeviation. Two samples were compared by t-test, and multiple groups were compared by χ2 test. P<0.05 indicated that the test was statistically significant.


## 2.1. Study Subjects

In this study, for data collection, a total of 1,186 female patients with or suspected breast tumor who received clinical palpation, molybdenum target examination, or color Doppler ultrasonography in Jincheng People’s Hospital from 2019 to 2021 were selected. All patients received preoperative MRI examination and needle biopsy after MRI examination. After screening, 858 patients were included (ages: 31~72 years old), with a mean age of51.42±10.22 years. The patients were divided into an experimental group and a control group. The experimental group was treated with EGFR-targeted therapy, and the control group was given conventional chemotherapy according to subsequent treatment modalities. All patients with a single mass of unilateral breast found by molybdenum target, ultrasound, and clinical examination; patients without any treatment; patients undergoing MRI after admission; and patients with pathological confirmation after breast MRI were included in the study, whereas patients without pathological confirmation; patients who underwent chemotherapy, surgery, and other related treatment; pregnant women; and lactating patients were excluded. All patients signed the informed consent form, and the trial process was approved by the ethics committee of Jincheng People’s Hospital.


## 2.2. MRI Scanning Method

For MRI scanning, the Siemens 3.0T superconducting magnetic resonance scanner was adopted. The patient was placed in a prone position with both breasts naturally drooped. The breast and axillary regions were scanned. After conventional triplane localization, transaxial T1WI, T2WI, T2WI-FS, and dynamic contrast-enhanced scans were performed. Field of view was 340 mm, slice thickness was 4 mm, and spacing was 0.8 mm; matrix:320×320. Gadopentetate meglumine (GD-DTPA), a contrast agent, was rapidly injected through the cubital vein at 0.2 mmol/kg at an injection rate of 3.0 mL/s, and 15 mL of normal saline was intravenously injected at the same rate to flush out the residual contrast agent. A phase was scanned before injection of the contrast agent, after which the body position was maintained, and other phases were scanned in the 20 s after injection of the contrast agent, with five consecutive phases acquired without interval [20, 21].


## 2.3. Images Postprocessing Method

The relevant software was applied on a Siemens image postprocessing workstation, while finding the region of interest (ROI) the necrosis, vascular, or cystic areas were avoided. When selecting the ROI, the area should be as large as possible. The time-signal intensity curve (TIC) of the lesion in the ROI was measured. Maximum signal intensity projection (MP) and multiplanar reformation were performed on the images to observe the shape of the lesion and its relationship with the surrounding vessels. The image quality, lesion characteristics, and lesion detection rate of each sequence were compared. The following parameters were measured by the TIC:(i)
Steepest slope (SSmax): the SSmax was computed as(1)SSmax%/s=SIend−SIprior×100%ISI0×Tend−Tprior,where SIend is the signal intensity at the highest point of the rapid rise segment of the curve, SIprior is the signal intensity value at the start point of the rapid rise segment, Tend and Tprior represent the time points corresponding to SIend and SIprior, respectively, and SI0 indicate the signal intensity value before enhancement. Moreover, the time to peak height (pH) (Tpeak) was also measured
(ii)
The maximum enhancement (Emax): it was computed as(2)Emax=SSmax−SLpreSLprc×100%(iii)
The rate of early intension (REI): the REI was computed(3)REI=SSmax1~3min−SIpreSIpre×100%(iv)
The signal enhancement rate at 1 ~ 5 minutes after enhancement(EN): it was calculated as(4)EN=SIn−SIpreSIpre×100%,where EN is the enhancement rate and SIpre and SIn are the signal intensities of the lesions before and after enhancement (n=l-5)
(v)
Measurement of tumor size: according to RECIST criteria [19], the cancer focus was determined on the early arterial images, its maximum diameter line (D) was measured 3 times on the layer with the largest extent of cancer focus, and the average value was calculated; for multi-focal cancer, the maximum diameter line of each cancer focus was measured, and its sum was calculated as the maximum diameter line of cancer focus. The reduction rate after chemotherapy was calculated as(5)ReductionRate:△D%=D1−D2D1×100%,whereD1 and D2 indicate the maximum diameter of cancer foci before and after chemotherapy, respectivelyEach ROI was approximately 20-30 mm2, and its ADC value was measured. The maximum signal intensity (SImax); the signal intensity value of the curve at the starting point (SI0); the pH, SSmax, and SSmax ratio (SSmaxR) were calculated. The equation was pH=SImax−SI0; SSmaxR=SIpostf−SIpref/SIpostn−SIpren. SIpost was the highest signal intensity, and Slpre was the lowest signal intensity during the scan period with the greatest signal rise, f was the lesion, and n was the normal tissue; SImax was the maximum value within the curve.
(vi)
Calculation of DWI diagnostic indicators: ADC value change rate was computed as(6)△ADC%=ADC1−ADC2ADC1×100%,where ADC1 and ADC2 indicate ADC values before and after chemotherapy, respectively.(vii)
Calculation of semiquantitative parameter diagnostic indicators [22]: the change rate of SImax and pH: △SImax%=Smax1−Smax2/SImax1×100%, Smax1, and SImax2 indicated the maximum signal intensity of cancer foci before and after chemotherapy; △pH%=pH1−PH2/PH1×100%, PH1, and PH2 indicated the PH value of cancer foci before and after chemotherapy


## 2.4. Automatic Segmentation Algorithm of MRI Image

In clinical practice, people are only interested in certain sections of the image received from imaging equipment, referred to as the target region. To analyze these target regions qualitatively or quantitatively in the process of diagnosis and visualization, it is necessary to separate them from the image by special techniques. The purpose of image segmentation is to achieve such a process [23]. The target detection algorithm based on machine learning is mainly used, while the traditional target detection algorithm depends on manual processing as shown in Figure 1.Figure 1
Flow chart of the traditional target detection method.Region-based convolutional neural network (RCNN) applied the deep learning method to the research of target detection for the first time, but the problems of long training time and slow image detection of RCNN still exist. Afterward, fast RCNN, as an improved algorithm of RCNN appeared, which greatly improved the training and detection speed of RCNN [24]. The process of the fast RCNN algorithm is shown in Figure 2.Figure 2
Flow chart of fast RCNN algorithm.The RCNN algorithm relies on massive high-dimensional features to generate classifiers and regressors, which requires a lot of computer memory. To solve this problem, fast RCNN creatively adopts a convolutional neural network (CNN) to realize the classification and location regression of candidate regions, avoiding the computational cost caused by training many linear support vector machines and linear regression models. To enable the CNN to deal with classification and regression multitask problems at the same time and share convolution parameters, fast RCNN designs a multitask loss function as given in(7)Le,r,hr,d=Lclsp,r+γr≥1Llochr,d,wherer and d are the category label and location label of the candidate region. Only when the target is the background, r is equal to 0; otherwise, it is greater than or equal to 1. e is the category of prediction, and hr is the location of the bounding box of prediction. r≥1 means when the candidate region is not the background, γ=1.Fast RCNN greatly speeds up the model training and image detection by optimizing the feature extraction of candidate regions. It also uses CNN to design the detector, which reduces the consumption of computing resources of the model and improves the detection accuracy.


## 2.5. Evaluation of Medical Image Segmentation

In the field of medical image processing, segmentation evaluation methods have also received a lot of attention, and some organizations or institutions have also put forward their segmentation evaluation system. Volume overlap error (VOE), relative volume difference (AVD), average symmetric surface distance (ASSD), the root mean square symmetric surface distance (RMS), and the maximum symmetric surface distance (MSSD) were evaluated [25].
(8)VOEM,N=Volseg∩VolrefVolseg∪Volref×100%,AVDM,N=Volseg−VolrefVolref×100%,ASSDM,N=∑m∈Mminn∈Ndistm,n+∑n∈Nminm∈Mdistm,nPM+PN,RMSM,N=∑m∈Mminn∈Ndistm,n2+∑n∈Nminm∈Mdistm,n2PM+PN,MSSDM,N=maxmaxm∈Mminn∈Ndistm,n,minm∈Mdistm,n,whereVolseg is the number of volumes in the segmentation result, Volref shows the number of volumes in the gold standard, distm,n indicates the distance between volumes m and n, M and N represent the segmentation results and the gold standard, and PM and PN are the segmentation results and the number of boundary volumes in the gold standard. Given various factors, the above five methods are selected for the evaluation index of the segmentation algorithm.


## 2.6. Statistical Methods

SPSS 22.0 statistical software was applied for data analysis. All data were expressed asmean±standarddeviation. Two samples were compared by t-test, and multiple groups were compared by χ2 test. P<0.05 indicated that the test was statistically significant.


## 3. Results

### 3.1. Analysis of Image Segmentation Results

The results of the manual segmentation evaluation showed that the RVD, VOE, ASSD, RMS, and MSSD were 4.63%, 6.36%, 0.98 mm, 1.81 mm, and 1.85 mm, respectively. The results of machine automatic segmentation showed that RVD, VOE, ASSD, RMS, and MSSD were 6.82%, 10.27%, 1.47 mm, 1.28 mm, and 3.88 mm, respectively. The difference between the two groups was statistically significant as shown in Figure3. Compared with manual segmentation, machine automatic segmentation not only considers the local information of the image but also considers the edge information of the image, with high precision.Figure 3
The score of the image segmentation effect (∗ indicates that the difference is statistically significant compared with manual segmentation, P<0.05).


### 3.2. MRI Results

Of the total 858 patients, 264 were benign and 594 were malignant. MRI detection was carried out. The detection rate of T1WI, T2WI, and T2WI-FS plain scan was low. MRI dynamic enhanced scan can not only detect all lesions but also clearly show the size, morphological characteristics, edge structure, anatomical location, and hemodynamic characteristics of the lesion [26]. The results are shown in Figures 4 and 5, respectively. In Figure 5(a), the long arrow is highly suspected of cancer, because it is significantly enhanced compared with the reference substance, and the short arrow refers to a second small cancer, which is not displayed and suspected during molybdenum target examination. Figure 5(b) shows the same image under the cover of MRI-assisted detection. When the breast shows an obvious enhancement than the baseline, the frequency color is used to emphasize, which can further point out the special types of enhancement, providing more accurate information for the identification of benign and malignant breast tumors.Figure 4
Comparison of detection results of lesions in different sequences (∗ indicates that the difference is statistically significant relative to pathological detection results, P<0.05).Figure 5
Breast cancer images.
(a)(b)The mean values of Tpeak, SSmax, El,E2, E3, E4, and E5 in benign breast tumors were 284±26 s, 1.6±0.7×102, 272±78%, 134±38%, 203±67%, 251±74%, 258±75%, and 270±51%, respectively. The mean values of Tpeak, SSmax, El, E2, E3, E4, and E5 in malignant breast tumors were 159±64 s, 2.8±1.2×102, 268±71%, 188±64%, 254±70%, 247±68%, 241±62%, and 236±53%, respectively. There were obvious differences (P<0.05), in the changes of Tpeak, SSmax, El, E2, and E5 between benign and malignant tumors and the changes of SSmax and El were the largest (Figure 6).Figure 6
Parameters detected by MRI (∗ indicates that the difference is statistically significant versus benign lesions, P<0.05).


### 3.3. Comparison of Tumors between Two Groups before and after Chemotherapy

Before chemotherapy, the maximum diameter of tumors in the control group and the experimental group were38.2±12.3 mm and 41.5±11.8 mm, respectively (P>0.05). After chemotherapy, the maximum diameter of the tumor in the control and the experimental groups were 26.4±11.6 mm and 20.3±13.5 mm, respectively, and the difference was statistically significant (P<0.05) as shown in Figure 7. The △D % of the experimental group was 53±26, which was significantly higher than the 22±21 of the control group (P<0.05). The ROC curve was performed with △D % as the evaluation parameter and pathological reactivity grouping as the gold standard. The area under the curve was 0.91, P<0.05, so △D % was considered to be of diagnostic value. The cut-off point was 33.6%, the sensitivity was 100%, and the specificity was 73.3% as shown in Figure 8.Figure 7
Comparison of the maximum diameter of tumor between the two groups after chemotherapy (∗ indicates that the difference is statistically significant in contrast with the control group, P<0.05).Figure 8
ROC curve of maximum tumor diameter detection.


### 3.4. Comparison of ADC Values before and after Chemotherapy

Before chemotherapy, there was no apparent distinction in ADC value between the experimental group and the control group (P>0.05). After chemotherapy, the ADC value of the experimental group was 12.74±2.08, which was superior to that of the control group (9.7±1.88) (P<0.05) as shown in Figure 9. The △ADC % in the experimental group was 33±18, which was significantly higher than 12±7 in the control group (P<0.05). The ROC curve was performed with △ADC % as the evaluation parameter and the pathological reactivity grouping as the gold standard. The area under the curve was 0.82 (P<0.05), and the cut-off point was 25.4%. The sensitivity of the evaluation was 64.8%, and the specificity was 79.2% as illustrated in Figure 10.Figure 9
Comparison of ADC values between the two groups before and after chemotherapy (∗ indicates that the difference is statistically significant versus the control group, P<0.05).Figure 10
ROC curve of ADC value detection.


### 3.5. Comparison of Semiquantitative Parameters of Time-Signal Curve before and after Chemotherapy

Before chemotherapy, there was no significant difference in SSmax, SImax, pH, and SSmaxR between the two groups (P>0.05). After chemotherapy, SSmax was 2.63±0.88×102 and 3.08±0.63×102 in the control group and the experimental group, respectively (P<0.05); after chemotherapy, SImax and pH were 2,322.7±208.4 and 1,592.1±462.4 in the control group and 1,983.5±166.3 and 1,227.6±322.5 in the experimental group, respectively (P<0.05). The SSmaxR value was 7.82±6.24 in the experimental group was significantly lower than 10.08±6.25 in the control group, but there was no significant difference (P>0.05) as shown in Figure 11.Figure 11
Comparison of semiquantitative parameters of time-signal curve between the two groups before and after chemotherapy (∗ indicates that the distinction is statistically meaningful versus the control group, P<0.05).In the experimental group, △SImax % and △pH % were20.8±17.6 and 2.9±2.3. Similarly in the control group, △SImax % and △pH % were 4.2±2.3 and 0.6±0.4; the data of the experimental group were significantly higher than those of the control group (P<0.05). The results are shown in Figure 12. The ROC curve was performed with △SImax % and △pH % as the evaluation parameters, and the pathological reactivity grouping as the gold standard. The area under the curve was 0.77 and 0.79 (P<0.05). Therefore, it was considered that △SImax % and △pH % had diagnostic values. The △SImax % of the cut-off point was 17.4%, and the sensitivity and specificity were 73.6% and 79.8%, respectively. The △pH % of the cut-off point was 24.72%, and the sensitivity and specificity were 82.7% and 81.3% (Figure 13).Figure 12
Comparison of SImax and pH change rate between the two groups before and after chemotherapy (∗ indicates that the difference is statistically significant compared with the control group, P<0.05).Figure 13
ROC curve of the SImax and pH change rate.


## 3.1. Analysis of Image Segmentation Results

The results of the manual segmentation evaluation showed that the RVD, VOE, ASSD, RMS, and MSSD were 4.63%, 6.36%, 0.98 mm, 1.81 mm, and 1.85 mm, respectively. The results of machine automatic segmentation showed that RVD, VOE, ASSD, RMS, and MSSD were 6.82%, 10.27%, 1.47 mm, 1.28 mm, and 3.88 mm, respectively. The difference between the two groups was statistically significant as shown in Figure3. Compared with manual segmentation, machine automatic segmentation not only considers the local information of the image but also considers the edge information of the image, with high precision.Figure 3
The score of the image segmentation effect (∗ indicates that the difference is statistically significant compared with manual segmentation, P<0.05).


## 3.2. MRI Results

Of the total 858 patients, 264 were benign and 594 were malignant. MRI detection was carried out. The detection rate of T1WI, T2WI, and T2WI-FS plain scan was low. MRI dynamic enhanced scan can not only detect all lesions but also clearly show the size, morphological characteristics, edge structure, anatomical location, and hemodynamic characteristics of the lesion [26]. The results are shown in Figures 4 and 5, respectively. In Figure 5(a), the long arrow is highly suspected of cancer, because it is significantly enhanced compared with the reference substance, and the short arrow refers to a second small cancer, which is not displayed and suspected during molybdenum target examination. Figure 5(b) shows the same image under the cover of MRI-assisted detection. When the breast shows an obvious enhancement than the baseline, the frequency color is used to emphasize, which can further point out the special types of enhancement, providing more accurate information for the identification of benign and malignant breast tumors.Figure 4
Comparison of detection results of lesions in different sequences (∗ indicates that the difference is statistically significant relative to pathological detection results, P<0.05).Figure 5
Breast cancer images.
(a)(b)The mean values of Tpeak, SSmax, El,E2, E3, E4, and E5 in benign breast tumors were 284±26 s, 1.6±0.7×102, 272±78%, 134±38%, 203±67%, 251±74%, 258±75%, and 270±51%, respectively. The mean values of Tpeak, SSmax, El, E2, E3, E4, and E5 in malignant breast tumors were 159±64 s, 2.8±1.2×102, 268±71%, 188±64%, 254±70%, 247±68%, 241±62%, and 236±53%, respectively. There were obvious differences (P<0.05), in the changes of Tpeak, SSmax, El, E2, and E5 between benign and malignant tumors and the changes of SSmax and El were the largest (Figure 6).Figure 6
Parameters detected by MRI (∗ indicates that the difference is statistically significant versus benign lesions, P<0.05).


## 3.3. Comparison of Tumors between Two Groups before and after Chemotherapy

Before chemotherapy, the maximum diameter of tumors in the control group and the experimental group were38.2±12.3 mm and 41.5±11.8 mm, respectively (P>0.05). After chemotherapy, the maximum diameter of the tumor in the control and the experimental groups were 26.4±11.6 mm and 20.3±13.5 mm, respectively, and the difference was statistically significant (P<0.05) as shown in Figure 7. The △D % of the experimental group was 53±26, which was significantly higher than the 22±21 of the control group (P<0.05). The ROC curve was performed with △D % as the evaluation parameter and pathological reactivity grouping as the gold standard. The area under the curve was 0.91, P<0.05, so △D % was considered to be of diagnostic value. The cut-off point was 33.6%, the sensitivity was 100%, and the specificity was 73.3% as shown in Figure 8.Figure 7
Comparison of the maximum diameter of tumor between the two groups after chemotherapy (∗ indicates that the difference is statistically significant in contrast with the control group, P<0.05).Figure 8
ROC curve of maximum tumor diameter detection.


## 3.4. Comparison of ADC Values before and after Chemotherapy

Before chemotherapy, there was no apparent distinction in ADC value between the experimental group and the control group (P>0.05). After chemotherapy, the ADC value of the experimental group was 12.74±2.08, which was superior to that of the control group (9.7±1.88) (P<0.05) as shown in Figure 9. The △ADC % in the experimental group was 33±18, which was significantly higher than 12±7 in the control group (P<0.05). The ROC curve was performed with △ADC % as the evaluation parameter and the pathological reactivity grouping as the gold standard. The area under the curve was 0.82 (P<0.05), and the cut-off point was 25.4%. The sensitivity of the evaluation was 64.8%, and the specificity was 79.2% as illustrated in Figure 10.Figure 9
Comparison of ADC values between the two groups before and after chemotherapy (∗ indicates that the difference is statistically significant versus the control group, P<0.05).Figure 10
ROC curve of ADC value detection.


## 3.5. Comparison of Semiquantitative Parameters of Time-Signal Curve before and after Chemotherapy

Before chemotherapy, there was no significant difference in SSmax, SImax, pH, and SSmaxR between the two groups (P>0.05). After chemotherapy, SSmax was 2.63±0.88×102 and 3.08±0.63×102 in the control group and the experimental group, respectively (P<0.05); after chemotherapy, SImax and pH were 2,322.7±208.4 and 1,592.1±462.4 in the control group and 1,983.5±166.3 and 1,227.6±322.5 in the experimental group, respectively (P<0.05). The SSmaxR value was 7.82±6.24 in the experimental group was significantly lower than 10.08±6.25 in the control group, but there was no significant difference (P>0.05) as shown in Figure 11.Figure 11
Comparison of semiquantitative parameters of time-signal curve between the two groups before and after chemotherapy (∗ indicates that the distinction is statistically meaningful versus the control group, P<0.05).In the experimental group, △SImax % and △pH % were20.8±17.6 and 2.9±2.3. Similarly in the control group, △SImax % and △pH % were 4.2±2.3 and 0.6±0.4; the data of the experimental group were significantly higher than those of the control group (P<0.05). The results are shown in Figure 12. The ROC curve was performed with △SImax % and △pH % as the evaluation parameters, and the pathological reactivity grouping as the gold standard. The area under the curve was 0.77 and 0.79 (P<0.05). Therefore, it was considered that △SImax % and △pH % had diagnostic values. The △SImax % of the cut-off point was 17.4%, and the sensitivity and specificity were 73.6% and 79.8%, respectively. The △pH % of the cut-off point was 24.72%, and the sensitivity and specificity were 82.7% and 81.3% (Figure 13).Figure 12
Comparison of SImax and pH change rate between the two groups before and after chemotherapy (∗ indicates that the difference is statistically significant compared with the control group, P<0.05).Figure 13
ROC curve of the SImax and pH change rate.


## 4. Discussion

Studies have shown that early detection of BC is helpful to reduce the mortality of BC and improve the 5-year survival rate of patients. Thanks to the continuous development of computer-aided detection and diagnosis system for BC, the cost of women receiving early detection of BC is declining, which greatly improves the popularity of early detection of BC [26]. In previous studies, the early detection of BC is usually carried out based on mammography images. However, with the development of breast MRI technology, breast MRI detection has higher sensitivity and cancer detection rate than X-ray screening, which makes the early detection and diagnosis of BC reach a new height [27]. MRI was used to diagnose suspected BC patients. The results showed that the RVD, VOE, ASSD, RMS, and MSSD evaluated by manual segmentation were 4.63%, 6.36%, 0.98 mm, 1.81 mm, and 1.85 mm. The results of machine automatic segmentation showed that RVD, VOE, ASSD, RMS, and MSSD were 6.82%, 10.27%, 1.47 mm, 1.28 mm, and 3.88 mm, respectively. The distinction between the two groups was statistically meaningful (P<0.05), compared with manual segmentation, the accuracy of machine automatic segmentation was higher.The main manifestations of BC masses are an irregular shape, ill-defined borders, internal echoes, incomplete surrounding tissues, and lymphadenopathy [28]. The results of MRI showed that 264 of 858 patients had benign lesions and 594 had malignant lesions confirmed by pathology. The results of MRI showed that in MRI sequence detection, T1WI, T2WI, and T2WI-FS plain scan of the lesion detection rate was low and the effect was not good. However, MRI dynamic contrast-enhanced could not only detect the lesions in 100% but also clearly show the size, morphological characteristics, marginal structure, anatomical location, and hemodynamic characteristics of the lesions. In addition, the TIC of breast MRI scan was also one of the most important diagnostic indicators [29]. The results of this test showed that the mean values of Tpeak, SSmax, El, E2, E3, E4, and E5 in benign breast tumors were 284±26 s, 1.6±0.7×102, 272±78%, 134±38%, 203±67%, 251±74%, 258±75%, and 270±51%; the mean values of Tpeak, SSmax, El, E2, E3, E4, and E5 in malignant breast tumors were 159±64 s, 2.8±1.2×102, 268±71%, 188±64%, 254±70%, 247±68%, 241±62%, and 236±53%. There were significant differences in the changes of Tpeak, SSmax, El, E2, and E5 between benign and malignant tumors (P<0.05), with the greatest changes of SSmax and El, indicating that MRI had good sensitivity in the detection of BC.After chemotherapy, SSmax was2.63±0.88×102 and 3.08±0.63×102 in the control group and experimental group, and there was a significant difference between the two groups (P<0.05); after chemotherapy, SImax and pH were 2,322.7±208.4 and 1,592.1±462.4 in the control group and 1,983.5±166.3 and 1,227.6±322.5 in the experimental group, respectively. The values in the experimental group were significantly lower than those in the control group (P<0.05); △SImax % and △pH % were 20.8±17.6 and 2.9±2.3 in the experimental group and 4.2±2.3 and 0.6±0.4 in the control group (P<0.05). Studies have shown that the decrease of tumor vascular density after chemotherapy leads to the decrease of contrast agent penetration, resulting in the decrease of SImax, pH, and SSmax values. In addition, the results suggested that although there was a certain difference in SSmax changes between the two groups after chemotherapy, as well as before and after chemotherapy, there was no statistically significant difference, and the analysis may be that this parameter was greatly affected by subjective factors and could not objectively reflect the hemodynamic characteristics of the tumor.In assessing efficacy, changes in the size of cancer foci during chemotherapy are the simplest and most intuitive assessment method [30]. The results showed that before chemotherapy, the maximum diameter of cancer foci in the control group and the experimental group was 38.2±12.3 mm and 41.5±11.8 mm, and there was no significant difference between the two groups (P>0.05); after chemotherapy, the maximum diameter of cancer foci in the control group and the experimental group was 26.4±11.6 mm and 20.3±13.5 mm, and there was an obvious difference between the two groups (P<0.05). It revealed that EGFR-targeted therapy had better efficacy compared with conventional chemotherapy. Studies have shown that the change of ADC value of cancer foci during chemotherapy should precede the reduction of the maximum diameter line, and the change of ADC value of cancer foci may become one of the indicators to predict the efficacy of chemotherapy for BC [31]. The results of this exploration showed that before chemotherapy, there was no significant difference in ADC value between the experimental group and the control group (P>0.05). After chemotherapy, the ADC value of the experimental group (12.74±2.08) was significantly higher than that (9.7±1.88) of the control group (P<0.05). This shows that the change and rate of change of tumor ADC value are correlated with the efficacy of chemotherapy, and the increase of ADC value is a manifestation of effective chemotherapy.


## 5. Conclusions

Due to its high sensitivity, magnetic resonance (MR) mammography is increasingly used as an adjunct to mammography and ultrasound for the diagnosis of BC. In this study, the MRI technique was adopted to diagnose suspected cases of BC, and the results were compared with the pathological examination results. A total of 858 patients diagnosed with BC in Jincheng People’s Hospital from 2019 to 2021 were included and randomly divided into an experimental group and a control group. The experimental group was treated with epidermal growth factor receptor- (EGFR-) targeted therapy, and the control group was treated with conventional chemotherapy according to subsequent treatment modalities. Then, MRI was used to evaluate the efficacy of transepidermal factor receptor-targeted therapy in patients. The results showed that MRI had higher sensitivity and accuracy in the diagnosis of BC, improving the detection rate of lesions. Compared with conventional chemotherapy, epidermal factor receptor-targeted therapy had a better therapeutic effect, with significant changes in cancer foci, which has the value of clinical promotion. However, limited by time and personal ability, there are still many shortcomings of computer-aided detection and diagnostic system for BC. In addition, the results of individual parameters are susceptible to subjective factors, and there are some differences in the data results, which need to be further confirmed.



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*Source: 3127058-2022-06-14.xml*