# Intelligent Algorithm-Based Ultrasound Images in Evaluation of Therapeutic Effects of Radiofrequency Ablation for Liver Tumor and Analysis on Risk Factors of Postoperative Infection

Authors: Lou Kexin; Chen Ning; Li Zhihong; Xiao Shuo; Wu Rong
Journal: Contrast Media & Molecular Imaging
 (2022)

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## Abstract

This research aimed to explore the therapeutic effects of radiofrequency ablation (RFA) for liver tumors and to investigate the postoperative infection factors. Specifically, 80 patients with liver tumors undergoing ultrasound-guided FRA were selected as research subjects. They were diagnosed in the hospital. An intelligent fitting (IF) algorithm was compared with a genetic algorithm (GA) and applied to the RFA of the 80 patients. It was found that the running time of the IF algorithm was about 0.2 times than that of the GA, demonstrating better global searching capabilities. The mean diameter of single liver tumors was (3.45 ± 1.24) cm, and the complete ablation rate of tumors with diameters less than 3 cm was 87.88%, that of tumors with diameters of 3–5 cm was 72.92%, and that of tumors with a diameter of more than 5 cm was 63.33%. Posttreatment, the AST level decreased significantly and the ALB level increased significantly, and the difference was notable (P<0.05P<); the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L) and the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), showing a notable difference (P<0.05P<). The diameter, whether there was great vessel invasion, and TNM staging were associated with infection after RFA, and the difference was notable. The ultrasound images can effectively evaluate the therapeutic effects of RFA and the degree of inactivation of liver tumors. In addition, the tumor stage was an independent risk factor for postoperative infection.


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## Body

## 1. Introduction

Hepatocellular carcinoma (HCC) is a primary malignant tumor with high incidence, which often occurs in patients with chronic liver disease, and it may be or may not be accompanied by cirrhosis [1]. According to research reports, the 5-year survival rate for liver cancer is only 14.1%, and only about one-eighth of patients can survive more than 5 years [2]. The data released by National Cancer Center in January 2019 reveal that liver cancer is one of the most common malignant tumors in China, where the number of new cases and deaths from liver cancer each year is close to 400,000, accounting for approximately half of the number in the world. Above all, it ranks second in the mortality rate of malignant tumors and fourth in the incidence of malignant tumors in China [3].Radiofrequency ablation (RFA) refers to accurately inserting the ablation electrode into the tumor site by percutaneous puncture under the guidance of ultrasound images. Then, the radiofrequency waves heat the tumor tissue to 95°C, resulting in complete necrosis [4]. RFA is characterized by small trauma, high cure rate, low cost, and few complications. Hence, it is the first choice for standard therapy for liver tumors [5]. Although the clinical efficacy of liver tumors has been improved by RFA, the survival rate of liver cancer patients is still very low, thanks to the frequent recurrence posttreatment [6]. The incidence of HCC varies in different regions which have specific risk factors, including chronic hepatitis B or C virus infection, aflatoxin exposure, alcoholism, and smoking [7, 8]. Modeling subalgorithms are mainly used to process ultrasonic echo parameters and signals. As compared with the traditional threshold method, the detection of an oscillation-starting point based on an ultrasonic signal model can obtain the actual transit time with a higher detection accuracy [9]. However, its detection reliability is not satisfactory. In addition, the current ultrasonic time detection method is difficult to ensure that it can meet the application requirements in terms of reliability, precision, and rapidity. In view of the abovementioned problems, this research proposed an ultrasonic shape parameter determination method based on the intelligent fitting (IF) algorithm to use the powerful global search ability to reliably obtain the time value of the vibration point of the ultrasonic shape, eliminate the wave jumping in the detection of the vibration point, and perform the reliability detection of the ultrasonic shape. The simulation experiment design is used to test the performance of the vibration point based on the intelligent fitting algorithm [10], which improves the detection accuracy of the vibration point. The fitting model takes into account the complex instantaneous frequency characteristics in the ultrasonic received signal, avoids the detection error of the vibration point based on a single fixed frequency, and significantly improves the solution accuracy.In this research, 80 patients with liver tumors were selected as research subjects and accepted for ultrasound-guided RFA. The postoperative local recurrence rate was analyzed, and the statistically significant risk factors were analyzed by logistic linear regression. It was used to analyze the therapeutic effects of ultrasound-guided RFA on patients with liver tumors and to explore the related risk factors of infection after the surgery, expected to provide a theoretical basis for the clinical use of ultrasound-guided RFA to treat liver tumors.


## 2. Experimental Methods

### 2.1. Subjects and Grouping

Eighty patients with liver tumors, diagnosed in the hospital from February 2018 to October 2020, were selected as the research subjects. They all underwent ultrasound-guided RFA, including 55 men and 25 women, with an average age of (52.47 ± 13.25) years. The research subjects agreed to sign informed consent forms with the consent of their family members. This research had been approved by the ethics committee of the hospital.The inclusion criteria were as follows: patients who were diagnosed with liver tumors, with liver function classification of Child-Pugh A or Child-Pugh B; patients with no extrahepatic metastasis, no invasion of adjacent organs, and no vascular tumor thrombus; patients with no serious coagulation abnormality and no obvious abnormal blood picture; patients with solitary cancer and no less than three in number; and patients who did not want to undergo surgical resection due to their own reasons.The exclusion criteria were as follows: patients with hepatic vein carcinoma or portal vein tumor thrombus, patients with poor liver function, still not reaching Child-Pugh class A or Child-Pugh class B after treatment, patients who dropped out due to personal reasons during the follow-up process, and patients who had incomplete clinical data.


### 2.2. Ultrasound-Guided RFA

The patient was required to fast for 12 hours before surgery and had skin tests for penicillin and iodine. Preoperative education was provided to eliminate mental worries. Then, he was shaved for skin preparation. For those who were nervous, 10 mg of diazepam was injected intramuscularly 30 minutes before surgery. Additionally, the patient was trained to use urinals on the bed.The patient was laid in the right anterior oblique position. Local disinfection was performed first. After the aseptic-hole towel was spread, the patient was injected with local anesthesia. The puncture point and the needle angle and depth were determined under the guidance of conventional ultrasound and contrast ultrasound. A 16 G trocar was inserted into the proximal end of the lesion which was then connected to the RFA device. The RFA electrode was activated one by one according to the diameter of the liver tumor body. The RFA lasted for 12 minutes. Next, the cold circulation system was closed. When the temperature of the needle tip reached 90°C, the electrode was pulled out. To prevent cancer cell implantation or transfer through the needle tract, after the ablation, the needle tract ablation was performed.


### 2.3. Postoperative Processing

After RFA, an elastic bandage should be applied for 12 hours to prevent bleeding. The surgical limb should be stretched and immobilized for 10–24 hours, and strenuous activities should be avoided for 48–72 hours. Attention should be paid to prevent complications, including hematoma or bleeding at the puncture site, extubation syndrome, pericardial tamponade, and heart block, as well as symptoms such as dizziness, chest tightness, chest pain, palpitations, shortness of breath, nausea, vomiting, and cold sweat. The patient can have normal daily activities and exercises 1 month after the surgery. It should be noted that the skin at the puncture site should be kept clean and dry, and a bath was forbidden before the puncture site completely healed. In terms of diet, the patient should eat liquid or semiliquid food containing low fat, moderate protein, fruit, high vitamin, and fiber to prevent constipation.


### 2.4. The IF Algorithm

Ultrasonic shape feature parameter detection based on the IF algorithm is essentially a system parameter identification problem. The model parameters that need to be identified and the evaluation criteria are determined first [11]. Then, the IF algorithm is used to solve a set of parameters which have the best fit with the identified observation parameter data. Because of the special nature of ultrasound, the Gaussian model is used to describe the impulse response of the ultrasound transducer.(1)Tx1,s=αe−βs−δ2cos2πgs−δ+λ,where in the equation x1=α,β,g,δ,λ, α represents the signal amplitude coefficient, β represents the signal bandwidth, δ represents the transit time of the ultrasonic signal, g represents the center frequency of the signal, and λ represents the initial phase, λ=0 in this research. In broadband pulse ultrasound, an exponential model is used to describe the impulse response of the ultrasound transducer.(2)Tx2,s=B0s−δSne−s−δSsin2πgs−δ+λ·vs−δ,where in the equation x2=B0,n,S,g,δ,λ, B0 represents the signal amplitude coefficient, n and T are the parameters related to the performance of the transducer, n is between [1–4], δ represents the transit time of the ultrasonic signal, g represents the center frequency of the signal, and λ represents the initial phase, λ=0 in this research. In this research, the objective function is defined as the minimum mean and variance sum of the actual signal from the reconstructed signal, and the parameter to be sought for the objective function is the minimum variable x2, which is expressed as follows:(3)gx2=∑h=indexgindexQl−Tl,where x2 represents the parameters to be calculated, Ql represents the discrete signal obtained by signal reconstruction, Tl represents the discrete signal obtained in the actual sampling process, indexg represents the sequence number of the fitting start data point, and index represents the sequence number of the fitting end data point. The IF algorithm is generally a random search algorithm based on biological intelligence. First, the corresponding objective function value must be calculated. According to the criteria to search for the optimal, a search is performed in a specific variable area. After the accuracy is optimized, the algorithm stops and a specific value is output. A flowchart depicting the ultrasonic feature parameter detection based on the IF algorithm is shown in Figure 1.Figure 1
Flowchart depicting the ultrasonic feature parameter detection based on the IF algorithm.


### 2.5. Efficacy and the Follow-Up

The evaluation of the efficacy: the patient underwent a contrast-enhanced ultrasound examination of the liver 30 days after surgery to evaluate the ablation effects. Incomplete ablation: in the image, the internal arterial phase of the cancer was enhanced, indicating that the cancer tissue was not completely removed, and a second RFA was needed. If the cancer tissue still remained after two RFAs, it meant that the treatment failed and other treatment plans must be formulated. Complete ablation: the original tumor area showed low density, and the image was hyperechoic, with the arterial phase enhanced. Follow-up: after RFA, an ultrasound review was performed every 30 days to observe liver function and tumor markers, and it lasted for 3 months.


### 2.6. Simulation Experiment

Under normal circumstances, it is impossible to accurately obtain the time of the oscillation-starting point. In this research, the oscillation-starting point is detected indirectly based on the IF algorithm. As per the measurement principle of the ultrasonic flowmeter, the countercurrent propagation time value of the signal can be measured, and the equation is as follows:(4)T=Rc−ucosγ,where R represents the length of the propagation path of the ultrasonic signal and in this research, it is set as R=145mm, c represents the speed of sound, c=340m/s, u represents the flow velocity of the gas in the pipeline, and γ represents the installation angle between the flow velocity and the propagation path of the transducer, γ=45°. The reciprocal ofequation (4) is given as follows:(5)1T=c−ucosγR.According to formula (5), it can be found that there is a negative linear relationship between the reciprocal of the countercurrent propagation time and the flow velocity.


### 2.7. Statistical Analysis

The data were processed using SPSS19.0, the measurement data were expressed by the mean ± standard deviation (‾x ±s), and the count data were expressed by the percentage (%). Logistic linear regression analysis was used for statistically significant risk factors. P<0.05P< was the threshold for significance.


## 2.1. Subjects and Grouping

Eighty patients with liver tumors, diagnosed in the hospital from February 2018 to October 2020, were selected as the research subjects. They all underwent ultrasound-guided RFA, including 55 men and 25 women, with an average age of (52.47 ± 13.25) years. The research subjects agreed to sign informed consent forms with the consent of their family members. This research had been approved by the ethics committee of the hospital.The inclusion criteria were as follows: patients who were diagnosed with liver tumors, with liver function classification of Child-Pugh A or Child-Pugh B; patients with no extrahepatic metastasis, no invasion of adjacent organs, and no vascular tumor thrombus; patients with no serious coagulation abnormality and no obvious abnormal blood picture; patients with solitary cancer and no less than three in number; and patients who did not want to undergo surgical resection due to their own reasons.The exclusion criteria were as follows: patients with hepatic vein carcinoma or portal vein tumor thrombus, patients with poor liver function, still not reaching Child-Pugh class A or Child-Pugh class B after treatment, patients who dropped out due to personal reasons during the follow-up process, and patients who had incomplete clinical data.


## 2.2. Ultrasound-Guided RFA

The patient was required to fast for 12 hours before surgery and had skin tests for penicillin and iodine. Preoperative education was provided to eliminate mental worries. Then, he was shaved for skin preparation. For those who were nervous, 10 mg of diazepam was injected intramuscularly 30 minutes before surgery. Additionally, the patient was trained to use urinals on the bed.The patient was laid in the right anterior oblique position. Local disinfection was performed first. After the aseptic-hole towel was spread, the patient was injected with local anesthesia. The puncture point and the needle angle and depth were determined under the guidance of conventional ultrasound and contrast ultrasound. A 16 G trocar was inserted into the proximal end of the lesion which was then connected to the RFA device. The RFA electrode was activated one by one according to the diameter of the liver tumor body. The RFA lasted for 12 minutes. Next, the cold circulation system was closed. When the temperature of the needle tip reached 90°C, the electrode was pulled out. To prevent cancer cell implantation or transfer through the needle tract, after the ablation, the needle tract ablation was performed.


## 2.3. Postoperative Processing

After RFA, an elastic bandage should be applied for 12 hours to prevent bleeding. The surgical limb should be stretched and immobilized for 10–24 hours, and strenuous activities should be avoided for 48–72 hours. Attention should be paid to prevent complications, including hematoma or bleeding at the puncture site, extubation syndrome, pericardial tamponade, and heart block, as well as symptoms such as dizziness, chest tightness, chest pain, palpitations, shortness of breath, nausea, vomiting, and cold sweat. The patient can have normal daily activities and exercises 1 month after the surgery. It should be noted that the skin at the puncture site should be kept clean and dry, and a bath was forbidden before the puncture site completely healed. In terms of diet, the patient should eat liquid or semiliquid food containing low fat, moderate protein, fruit, high vitamin, and fiber to prevent constipation.


## 2.4. The IF Algorithm

Ultrasonic shape feature parameter detection based on the IF algorithm is essentially a system parameter identification problem. The model parameters that need to be identified and the evaluation criteria are determined first [11]. Then, the IF algorithm is used to solve a set of parameters which have the best fit with the identified observation parameter data. Because of the special nature of ultrasound, the Gaussian model is used to describe the impulse response of the ultrasound transducer.(1)Tx1,s=αe−βs−δ2cos2πgs−δ+λ,where in the equation x1=α,β,g,δ,λ, α represents the signal amplitude coefficient, β represents the signal bandwidth, δ represents the transit time of the ultrasonic signal, g represents the center frequency of the signal, and λ represents the initial phase, λ=0 in this research. In broadband pulse ultrasound, an exponential model is used to describe the impulse response of the ultrasound transducer.(2)Tx2,s=B0s−δSne−s−δSsin2πgs−δ+λ·vs−δ,where in the equation x2=B0,n,S,g,δ,λ, B0 represents the signal amplitude coefficient, n and T are the parameters related to the performance of the transducer, n is between [1–4], δ represents the transit time of the ultrasonic signal, g represents the center frequency of the signal, and λ represents the initial phase, λ=0 in this research. In this research, the objective function is defined as the minimum mean and variance sum of the actual signal from the reconstructed signal, and the parameter to be sought for the objective function is the minimum variable x2, which is expressed as follows:(3)gx2=∑h=indexgindexQl−Tl,where x2 represents the parameters to be calculated, Ql represents the discrete signal obtained by signal reconstruction, Tl represents the discrete signal obtained in the actual sampling process, indexg represents the sequence number of the fitting start data point, and index represents the sequence number of the fitting end data point. The IF algorithm is generally a random search algorithm based on biological intelligence. First, the corresponding objective function value must be calculated. According to the criteria to search for the optimal, a search is performed in a specific variable area. After the accuracy is optimized, the algorithm stops and a specific value is output. A flowchart depicting the ultrasonic feature parameter detection based on the IF algorithm is shown in Figure 1.Figure 1
Flowchart depicting the ultrasonic feature parameter detection based on the IF algorithm.


## 2.5. Efficacy and the Follow-Up

The evaluation of the efficacy: the patient underwent a contrast-enhanced ultrasound examination of the liver 30 days after surgery to evaluate the ablation effects. Incomplete ablation: in the image, the internal arterial phase of the cancer was enhanced, indicating that the cancer tissue was not completely removed, and a second RFA was needed. If the cancer tissue still remained after two RFAs, it meant that the treatment failed and other treatment plans must be formulated. Complete ablation: the original tumor area showed low density, and the image was hyperechoic, with the arterial phase enhanced. Follow-up: after RFA, an ultrasound review was performed every 30 days to observe liver function and tumor markers, and it lasted for 3 months.


## 2.6. Simulation Experiment

Under normal circumstances, it is impossible to accurately obtain the time of the oscillation-starting point. In this research, the oscillation-starting point is detected indirectly based on the IF algorithm. As per the measurement principle of the ultrasonic flowmeter, the countercurrent propagation time value of the signal can be measured, and the equation is as follows:(4)T=Rc−ucosγ,where R represents the length of the propagation path of the ultrasonic signal and in this research, it is set as R=145mm, c represents the speed of sound, c=340m/s, u represents the flow velocity of the gas in the pipeline, and γ represents the installation angle between the flow velocity and the propagation path of the transducer, γ=45°. The reciprocal ofequation (4) is given as follows:(5)1T=c−ucosγR.According to formula (5), it can be found that there is a negative linear relationship between the reciprocal of the countercurrent propagation time and the flow velocity.


## 2.7. Statistical Analysis

The data were processed using SPSS19.0, the measurement data were expressed by the mean ± standard deviation (‾x ±s), and the count data were expressed by the percentage (%). Logistic linear regression analysis was used for statistically significant risk factors. P<0.05P< was the threshold for significance.


## 3. Results

### 3.1. The Relationship between the Oscillation-Starting Point Performance and the Signal Period

Figure2 shows the relationship between the oscillation-starting point performance and the signal period. Based on the IF algorithm, the ultrasonic parameters were solved for each fitting period and the calculated average relative error represented the fitting performance. It was noted that as the fitting period continued to increase, the average relative error of the oscillation-starting point also increased. When the fitting period increased to 5, the number of fitting circles increased, and the average relative error did not change. Hence, this research used 5 signal fitting cycles to obtain the oscillation-starting point data.Figure 2
The relationship between the oscillation-starting point and the signal period.


### 3.2. Comparison of Global Search Capabilities

Figure3 shows the global search capabilities of the IF algorithm and GA algorithm. The GA algorithm was an evolutionary algorithm that simulates the law of survival of the fittest in nature, and it was also a neighborhood search algorithm. Its core idea was to continuously evolve in the solution space and then select the offspring with the highest fitness through the selection operator. The genetic operation was performed on the offspring with the highest fitness, and the algorithm can be stopped by a certain number of iterations or when the individual reached the required fitness value. It was mainly composed of coding, calculating the fitness value, selection, and genetic operation [12]. Under different signal-to-noise ratios, the times when the optimal represented the neighborhood of the oscillation-starting point were recorded to characterize the algorithm’s global search capability, and a greater number indicated a better global search capacity. With the continuous enhancement of the signal, errors occurred in the calculation based on the IF algorithm and the GA, and wave hopping occurred. However, compared with the GA, the IF algorithm was still able to search for the correct oscillation-starting neighborhood during the entire iteration process, demonstrating better performance than the GA under the same conditions. The running time of the GA was about 5 times than that of the IF algorithm. It may be because the search process of the genetic algorithm required multiple comparisons, which consumed a lot of time.Figure 3
Comparison of global search capabilities: (a) neighborhood times of the oscillation-starting point and (b) running time.∗ compared with the IF algorithm, P<0.05P<.
(a)(b)


### 3.3. General Information of Patients

Figure4 shows the general information of patients with liver tumors. There were 144 single foci in 80 patients with liver tumors, including 55 men (68.76%) and 25 women (31.25%), and the average age was (52.47 ± 13.25) years. There were 7 cases (8.75%) aged 18–25 years old, 12 cases (15%) aged 26–35 years old, 9 cases (11.25%) aged 36–45 years old, 14 cases (17.5%) aged 46–55 years old, and 38 cases (47.5%) between 56–65 years old.Figure 4
General information on patients with liver tumors: (a) sex ratio and (b) age distribution.
(a)(b)


### 3.4. The Therapeutic Effects of RFA

Figure5 shows the therapeutic effects of RFA on liver tumors. 80 patients with liver tumors underwent ultrasound-guided RFA, and 15 patients received RFA more than twice. The diameter of a single liver tumor is about 0.8–8.2 cm and the average diameter is (3.45 ± 1.24) cm. There were 66 foci with diameters less than 3 cm, of which 58 (87.88%) were completely ablated and 8 were incompletely ablated (12.12%); 48 with a diameter of 3–5 cm, of which 35 were completely ablated (72.92%) and 13 were incompletely ablated (27.08%); and 30 with a diameter larger than 5 cm, of which 19 (63.33%) were completely ablated and 11 cases (36.67%) were incompletely ablated.Figure 5
Therapeutic effects. (a) Complete ablation rate and incomplete ablation rate and (b) changes in liver tumor nodules. ∗ compared with a diameter <3 cm,P<0.05P<.
(a)(b)The 144 liver tumor nodules were found not to increase 1 month after the surgery. At 3 months postoperatively, 87 cases (60.42%) had reduced nodules, 42 (29.16%) had no obvious changes, and 15 had enlarged nodules (10.42%). Among them, 92 showed no enhancement in contrast-enhanced ultrasound and they were considered to be completely inactivated. The complete ablation rates and survival rates were 77.78% (112/144) and 22.22% (32/144), respectively.


### 3.5. Ultrasound Image Characteristics of Patients with Liver Tumors

Figure6 shows ultrasound-guided radiofrequency ablation in case 1 and Figure 7 compares the changes in liver hyperplasia and carcinogenesis before and after radiofrequency ablation in case 2.Figure 6
Case 1: ultrasound-guided radiofrequency ablation. The patient is a female, 61 years old. (a) Contrast-enhanced ultrasound images before radiofrequency ablation showing well-defined liver cancer boundary, (b) accurate needle approach and precise positioning during radiofrequency ablation, and (c) postoperative contrast-enhanced ultrasound showing complete inactivation of the liver tumor.
(a)(b)(c)Figure 7
Case 2: comparison before and after radiofrequency ablation for liver hyperplasia and carcinogenesis. The patient is a male, 65 years old. (a) Ultrasound to identify the location of the lesion, (b) ultrasound shows the ablation needle in the tumor, and (c) following radiofrequency ablation, the ablation zone covers the tumor.
(a)(b)(c)


### 3.6. Changes in Liver Function Indexes and Tumor Markers

Figure8 shows the liver function indexes and tumor markers prior to and posttreatment. The patients in this research were reviewed for changes in liver function indexes and tumor markers 1 month after RFA. The results showed that the AST level decreased significantly and the ALB level increased significantly, and the difference was notable (P<0.05P<). Posttreatment, the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L); the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), the difference was notable (P<0.05P<).Figure 8
Changes in liver function indexes and tumor markers before and after treatment. (a) AST, (b) ALB, and (c) TBIL and ALT.∗ compared with prior treatment, P<0.05P<.
(a)(b)(c)


### 3.7. Analysis of Risk Factors for Infection after RFA

Table1 shows the univariate analysis of infection after RFA. 8 risk factors in Table 1 were tested, and the results showed that the diameter of the liver tumor, whether there was a large blood vessel invasion, and TNM stage were related to the infection after RFA, and the difference was statistically significant (P<0.05P).Table 1
Univariate analysis of infection after RFA.
Risk factorsNumber of RFAsχ2PPGenderMen230.1530.717Women17Age<55 years old412.1470.235≥55 years old35Child-Pugh gradeGrade A100.1060.649Grade B6History of abdominal surgeryYes74.7320.0628No4DiameterDiameter <3 cm1463.415P≤0.001PDiameter between 3 and 5 cm21Diameter >5 cm32With or without great vessel invasionWith84.3780.034Without57TNM stagingTNM stage I1845.219P≤0.001PTNM stage II24TNM stage III-IV31Antimicrobial prophylaxisYes50.0001.000No7Table2 shows the multivariate analysis of infection after RFA. Logistic linear regression analysis was performed on the risk factors with statistical significance in univariate. The results showed that the TNM stage was an independent risk factor for infection after RFA.Table 2
Multivariate analysis of infection after RFA.
Risk factorsStandard errorWaldPP valueTNM staging0.4675.2640.010


### 3.8. Complications

Figure9shows the postoperative complications of RFA for liver tumors. The results showed that as the diameter increased, the incidence of adverse reactions and complications increased, such as fever and pain, and the difference was notable (P<0.05P<).Figure 9
Postoperative complications of liver tumors: (a) fever, (b) pain, (c) worsening liver damage, (d) gastrointestinal bleeding, and (e) pneumothorax.∗ compared with <3 cm, P<0.05P<.


## 3.1. The Relationship between the Oscillation-Starting Point Performance and the Signal Period

Figure2 shows the relationship between the oscillation-starting point performance and the signal period. Based on the IF algorithm, the ultrasonic parameters were solved for each fitting period and the calculated average relative error represented the fitting performance. It was noted that as the fitting period continued to increase, the average relative error of the oscillation-starting point also increased. When the fitting period increased to 5, the number of fitting circles increased, and the average relative error did not change. Hence, this research used 5 signal fitting cycles to obtain the oscillation-starting point data.Figure 2
The relationship between the oscillation-starting point and the signal period.


## 3.2. Comparison of Global Search Capabilities

Figure3 shows the global search capabilities of the IF algorithm and GA algorithm. The GA algorithm was an evolutionary algorithm that simulates the law of survival of the fittest in nature, and it was also a neighborhood search algorithm. Its core idea was to continuously evolve in the solution space and then select the offspring with the highest fitness through the selection operator. The genetic operation was performed on the offspring with the highest fitness, and the algorithm can be stopped by a certain number of iterations or when the individual reached the required fitness value. It was mainly composed of coding, calculating the fitness value, selection, and genetic operation [12]. Under different signal-to-noise ratios, the times when the optimal represented the neighborhood of the oscillation-starting point were recorded to characterize the algorithm’s global search capability, and a greater number indicated a better global search capacity. With the continuous enhancement of the signal, errors occurred in the calculation based on the IF algorithm and the GA, and wave hopping occurred. However, compared with the GA, the IF algorithm was still able to search for the correct oscillation-starting neighborhood during the entire iteration process, demonstrating better performance than the GA under the same conditions. The running time of the GA was about 5 times than that of the IF algorithm. It may be because the search process of the genetic algorithm required multiple comparisons, which consumed a lot of time.Figure 3
Comparison of global search capabilities: (a) neighborhood times of the oscillation-starting point and (b) running time.∗ compared with the IF algorithm, P<0.05P<.
(a)(b)


## 3.3. General Information of Patients

Figure4 shows the general information of patients with liver tumors. There were 144 single foci in 80 patients with liver tumors, including 55 men (68.76%) and 25 women (31.25%), and the average age was (52.47 ± 13.25) years. There were 7 cases (8.75%) aged 18–25 years old, 12 cases (15%) aged 26–35 years old, 9 cases (11.25%) aged 36–45 years old, 14 cases (17.5%) aged 46–55 years old, and 38 cases (47.5%) between 56–65 years old.Figure 4
General information on patients with liver tumors: (a) sex ratio and (b) age distribution.
(a)(b)


## 3.4. The Therapeutic Effects of RFA

Figure5 shows the therapeutic effects of RFA on liver tumors. 80 patients with liver tumors underwent ultrasound-guided RFA, and 15 patients received RFA more than twice. The diameter of a single liver tumor is about 0.8–8.2 cm and the average diameter is (3.45 ± 1.24) cm. There were 66 foci with diameters less than 3 cm, of which 58 (87.88%) were completely ablated and 8 were incompletely ablated (12.12%); 48 with a diameter of 3–5 cm, of which 35 were completely ablated (72.92%) and 13 were incompletely ablated (27.08%); and 30 with a diameter larger than 5 cm, of which 19 (63.33%) were completely ablated and 11 cases (36.67%) were incompletely ablated.Figure 5
Therapeutic effects. (a) Complete ablation rate and incomplete ablation rate and (b) changes in liver tumor nodules. ∗ compared with a diameter <3 cm,P<0.05P<.
(a)(b)The 144 liver tumor nodules were found not to increase 1 month after the surgery. At 3 months postoperatively, 87 cases (60.42%) had reduced nodules, 42 (29.16%) had no obvious changes, and 15 had enlarged nodules (10.42%). Among them, 92 showed no enhancement in contrast-enhanced ultrasound and they were considered to be completely inactivated. The complete ablation rates and survival rates were 77.78% (112/144) and 22.22% (32/144), respectively.


## 3.5. Ultrasound Image Characteristics of Patients with Liver Tumors

Figure6 shows ultrasound-guided radiofrequency ablation in case 1 and Figure 7 compares the changes in liver hyperplasia and carcinogenesis before and after radiofrequency ablation in case 2.Figure 6
Case 1: ultrasound-guided radiofrequency ablation. The patient is a female, 61 years old. (a) Contrast-enhanced ultrasound images before radiofrequency ablation showing well-defined liver cancer boundary, (b) accurate needle approach and precise positioning during radiofrequency ablation, and (c) postoperative contrast-enhanced ultrasound showing complete inactivation of the liver tumor.
(a)(b)(c)Figure 7
Case 2: comparison before and after radiofrequency ablation for liver hyperplasia and carcinogenesis. The patient is a male, 65 years old. (a) Ultrasound to identify the location of the lesion, (b) ultrasound shows the ablation needle in the tumor, and (c) following radiofrequency ablation, the ablation zone covers the tumor.
(a)(b)(c)


## 3.6. Changes in Liver Function Indexes and Tumor Markers

Figure8 shows the liver function indexes and tumor markers prior to and posttreatment. The patients in this research were reviewed for changes in liver function indexes and tumor markers 1 month after RFA. The results showed that the AST level decreased significantly and the ALB level increased significantly, and the difference was notable (P<0.05P<). Posttreatment, the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L); the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), the difference was notable (P<0.05P<).Figure 8
Changes in liver function indexes and tumor markers before and after treatment. (a) AST, (b) ALB, and (c) TBIL and ALT.∗ compared with prior treatment, P<0.05P<.
(a)(b)(c)


## 3.7. Analysis of Risk Factors for Infection after RFA

Table1 shows the univariate analysis of infection after RFA. 8 risk factors in Table 1 were tested, and the results showed that the diameter of the liver tumor, whether there was a large blood vessel invasion, and TNM stage were related to the infection after RFA, and the difference was statistically significant (P<0.05P).Table 1
Univariate analysis of infection after RFA.
Risk factorsNumber of RFAsχ2PPGenderMen230.1530.717Women17Age<55 years old412.1470.235≥55 years old35Child-Pugh gradeGrade A100.1060.649Grade B6History of abdominal surgeryYes74.7320.0628No4DiameterDiameter <3 cm1463.415P≤0.001PDiameter between 3 and 5 cm21Diameter >5 cm32With or without great vessel invasionWith84.3780.034Without57TNM stagingTNM stage I1845.219P≤0.001PTNM stage II24TNM stage III-IV31Antimicrobial prophylaxisYes50.0001.000No7Table2 shows the multivariate analysis of infection after RFA. Logistic linear regression analysis was performed on the risk factors with statistical significance in univariate. The results showed that the TNM stage was an independent risk factor for infection after RFA.Table 2
Multivariate analysis of infection after RFA.
Risk factorsStandard errorWaldPP valueTNM staging0.4675.2640.010


## 3.8. Complications

Figure9shows the postoperative complications of RFA for liver tumors. The results showed that as the diameter increased, the incidence of adverse reactions and complications increased, such as fever and pain, and the difference was notable (P<0.05P<).Figure 9
Postoperative complications of liver tumors: (a) fever, (b) pain, (c) worsening liver damage, (d) gastrointestinal bleeding, and (e) pneumothorax.∗ compared with <3 cm, P<0.05P<.


## 4. Discussion

In this research, RFA was performed on patients with liver tumors under the guidance of IF-based ultrasound. RFA has been widely used in the treatment of liver tumors. It is safe and reliable, and especially suitable for liver tumors with a diameter of less than 3 cm [13]. The results of this research found that there were 66 foci of a diameter <3 cm, with a complete ablation rate of 87.88%; 48 foci of a diameter of 3–5 cm, with a complete ablation rate of 72.92%; and 30 lesions of a diameter of >5 cm, with a complete ablation rate of 63.33% [14]. Statistics revealed that RFA demonstrated different therapeutic effects for liver tumors with distinct diameters. Larger liver tumors resulted in a lower complete ablation rate, and the difference was notable (P<0.05P<). This was consistent with the results of Yuan et al. [15].80 patients were reexamined for the changes in liver function indexes and tumor markers 1 month after RFA [16]. If there were remaining cancer tissues and recurrence occurred, a second RFA was needed, or other remedial treatment measures had to be formulated. The results showed that the AST level decreased, while the ALB level increased, and the difference was notable (P<0.05P<). Posttreatment, the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L), and the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), and the difference was notable (P<0.05P<) [17]. It suggested that, after the patient underwent multiple RFAs, the liver function indexes and tumor marker levels changed, improving the patient's quality of life to a certain extent, and prolonging the survival time [18].As ultrasound imaging technology marches forward continuously, it is more accurate in identifying the diameter and boundaries of tumors, providing the necessary technical support for the RFA [19]. The ultrasound imaging demonstrated 91.56% accuracy for 144 foci in the research, which aligned with the results of Yi et al. [20]. There were 96 completely inactivated foci, of which 92 had no obvious enhancement in the arterial phase and portal vein phase, indicating that the blood vessels in the foci were completely destroyed and the tissue was completely inactivated [21]. It suggested that ultrasound imaging assisted in accurately positioning the lesion for RFA and reflected the blood supply of the lesion site. Tumors of different diameters needed distinct times of RFAs, suggesting that this factor was related to recurrence after surgery. There were 3 patients with vascular tumor thrombi, and the recurrence rate after RFA was as high as 100%, suggesting that this factor was related to recurrence after surgery [22]. In this research, the univariate analysis was performed on four risk factors of age, liver tumor diameter, great blood vessel invasion, and TNM staging. Then, statistically significant risk factors were analyzed by multivariate analysis. The results showed that TNM staging was an independent risk factor for liver tumors.


## 5. Conclusion

This research analyzed the clinical diagnostic efficacy of ultrasound-guided RFA of liver tumors based on an intelligent fitting algorithm and explored the related risk factors for postoperative infection. The results showed that the global search ability of the fitting algorithm was good; ultrasound images could effectively evaluate the diagnostic efficacy of RFA and the degree of inactivation of liver tumors; the TNM stage was an independent risk factor for liver tumors. The disadvantage of this research was that the sample size was small, which could be affected by a selection bias. In the later stage, the sample size had to be expanded for further in-depth research. In conclusion, the radiofrequency ablation system based on the intelligent fitting algorithm based on the ultrasound image constructed in this research showed high clinical diagnostic application value for liver tumor patients.



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