Comparison of the detection performance of [18F]FDG PET/CT with CT on bone metastases: randomized controlled clinical trial

Comparison of the detection performance of [18F]FDG PET/CT with CT on bone metastases: randomized controlled clinical trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of the detection performance of [18F]FDG PET/CT with CT on bone metastases: randomized controlled clinical trial Yujie Chang, Yifeng Gu, Shunyi Ruan, Shengyu Xu, Jing Sun, Zhiyuan Jiang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4969944/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Cancer Imaging → Version 1 posted 9 You are reading this latest preprint version Abstract Background Bone biopsy is the gold standard for diagnosing bone metastases. However, there is no clinical consensus regarding the optimal imaging test for locating the puncture site. Methods We compared the performance of [ 18 F]FDG PET/CT with CT in detecting bone metastases to achieve the highest biopsy efficiency. This registered prospective study enrolled 273 patients with bone lesions who were treated between January 2020 and March 2021. Patients were randomly assigned to undergo [ 18 F]FDG PET/CT or CT to locate the puncture site before bone biopsy. The accuracy, sensitivity, specificity, second biopsy rate, diagnostic time and cost-effectiveness of the two imaging tests were compared. Results The accuracy and sensitivity of [ 18 F]FDG PET/CT group in the diagnosis of bone metastases were significantly higher than CT group(97.08% vs. 90.44%, 98.76% vs. 92.22%, P <0.05). The second biopsy rate was significantly lower in the PET/CT group (2.19% vs. 5.15%; P < 0.05). The diagnostic time of PET/CT was 18.33 ± 2.08 days, which was significantly shorter than 21.28 ± 1.25 days in CT group ( P 6.3 combined with ALP > 103 U/L showed a tendency for tumor metastases with an AUC of 0.901 (95%CI 0.839 to 0.946, P<0.001). Conclusion [ 18 F]FDG PET/CT is more effective and less costly than CT in locating optimal bone biopsy site. Thus, [ 18 F]FDG PET/CT should be considered the optimal imaging test for locating the optimal puncture site for bone biopsy. Trial registration The prospective study was registered on 20180410, and the registration number is ChiCTR1800015540. Bone Metastasis Diagnostic Test Imaging Positron Emission Tomography Computed Tomograph Clinical Trial Results Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Bone is a common site of metastatic solid tumors[1]. More than 50% of all cancers develop bone metastases[2]. Bone is the third most common site of tumor metastasis, with only the lungs and liver having a higher metastatic rate[3]. Bone metastases often cause skeletal complications known as skeletal-related events (SREs), including pathological fractures, radiotherapy, bone surgery, spinal cord compression, and hypercalcemia. SREs can cause loss of mobility and social functioning, further reducing the quality of life (QoL), increasing healthcare expenditure, and worsening survival[4]. Early evaluation and diagnosis of bone metastases are important to ensure effective treatment. Pathological examination, which mainly involves bone biopsy, is the gold standard for the diagnosis of malignant bone metastasis. Biopsy of bone lesions has a diagnostic accuracy ranging from 66 to 98%, which is a significant difference[5]. Complications of bone biopsy include pain, osteomyelitis, and hematoma. If the metastatic site is adjacent to the lungs or spinal cord, puncture may cause pneumothorax and nerve root irritation[6, 7]. Owing to the risks of invasive examinations, conducting imaging tests before biopsies to locate optimal puncture sites can improve the success rate of diagnosis and minimize potential complications. Imaging tests, including bone scintigraphy, computed tomography (CT) and [ 18 F] fluorodeoxyglucose positron emission tomography/computed tomography ([ 18 F]FDG PET/CT) are commonly used for screening bone metastases. Bone scintigraphy is highly sensitive but usually has low specificity[8]. The sensitivity and specificity of CT in detecting malignant bone metastases are not superior to those of other traditional imaging tools (including bone scintigraphy)[9]. [ 18 F]FDG PET/CT is a promising tool that combines metabolic index standardized uptake values (SUV) with traditional imaging tools. Metabolically active bone lesions on [ 18 F]FDG PET/CT can result from primary or metastatic malignant tumors or benign bone diseases[10]. SUVmax has been proven to be a metabolic parameter in oncology[11]. It can be used as a reliable semi-quantitative indicator to differentiate metastatic bone lesions from normal tissues. Although [ 18 F]FDG PET/CT has several clinical advantages, it is not covered by basic medical insurance in China; therefore, undergoing [ 18 F]FDG PET/CT can increase the financial burden of patients. Therefore, CT remains the primary choice for pre-biopsy imaging to determine optimal puncture sites. However, secondary punctures for CT localization may also increase diagnostic time and patient economic burden due to the poor accuracy of CT-localized biopsies. Malignant tumor metastasis mainly involves bone remodeling, including bone resorption and formation. Serum calcium, phosphorus, alkaline phosphatase (ALP), and other indicators can indicate bone turnover and evaluate the progression of bone lesion progress[1]. Serological examinations are widely used in clinical practice to inspect bone metastases because of the convenience of noninvasive detection[12]. These indicators must be complemented by imaging test[13]. Consequently, this prospective study aimed to determine whether [ 18 F]FDG PET/CT could locate a puncture site more accurately than CT to improve the diagnostic rate of biopsy. Moreover, we attempted to determine the best cutoff value of clinical indicators for differentiating malignant bone metastases using a noninvasive examination and [ 18 F]FDG PET/CT. Methods Study design and participants This prospective, single-center, comparative imaging study was approved by the Ethics Committee of the Shanghai Sixth People’s Hospital and registered in the Chinese Clinical Trial Registry (ChiCTR1800015540). The principles of the Declaration of Helsinki were adhered to. Written informed consent was obtained from all patients. The inclusion criteria were: 1) referral for diagnostic workup for bone disease, 2) age older than 18 years, and 3) Karnofsky performance status of at least 60. The exclusion criteria were: 1) contraindications for biopsies (such as infection at the puncture sites), 2) severe bleeding metastases (such as severe hemophilia or severe disseminated intramuscular coagulopathy), and 3) random blood glucose > 11.1 mmol/L. Procedures After being included according to the abpve criteria, all patients were randomly assigned to the CT or [ 18 F]FDG PET/CT group at a ratio of 1:1. The two groups underwent the corresponding imaging tests separately to locate the optimal puncture sites before diagnostic biopsies. The demographic characteristics, SUVmax of [ 18 F]FDG PET/CT, and serological test results (including serum ALP, calcium, and phosphorus) of the two groups were collected from our center and analyzed using a de-identification method. Procedure of [ 18 F]FDG PET/CT and CT All patients were required to fast for at least 6h and undergo a peripheral blood sugar test to avoid hyperglycemia. Approximately 1 h after the intravenous injection of [ 18 F]FDG [333-5 18 MBq (9-14mCi)], imaging was performed using an integrated PET/CT system (Discovery VCT; GE Medical Systems) from the head to the lower limbs with the following settings: CT scan, 120 V and 80 mA, 64 slices, with a slice thickness of 3.75 mm. PET scans were performed with 2.5 min per bed position. Finally, CT and PET images were reconstructed using ordered subset expectation maximization. Attenuation correction was performed using unenhanced CT. A senior nuclear medicine doctor evaluated all combined [ 18 F]FDG PET/CT scans. The region of interest (ROI) around the bone lesions was drawn on [ 18 F]FDG PET/CT images of each transaxial slice. SUVmax was defined as the peak value of the pixel with the highest count within the ROI. CT was performed regularly. The [ 18 F]FDG PET/CT and CT images are shown in Fig. 1 . All imaging data were anonymized and randomized. The CT and [ 18 F]-FDG PET/CT examinations were read in consensus by three radiologists with 20 years of radiological experience. Performance and costs The performance of [ 18 F]FDG PET/CT and CT was assessed in terms of the accuracy of detecting bone lesions, which were determined for detecting the malignant and benign lesions in the two groups by comparing the imaging test result with the reference standard. If the imaging test showed an indicator of malignant metastases and the biopsy was unsatisfactory owing to the lack of tumor cells, patients underwent another invasive biopsy to exclude a possible malignant diagnosis. In the [ 18 F]FDG PET/CT group, in which the imaging test itself could cover the entire body, patients underwent another biopsy via other hypermetabolic puncture sites. Due to the limited visual field of CT, patients in the CT group may need to undergo anothor CT test to detect possible metabolic concentration foci, and then may undergo a seconf biopsy via the puncture site determined by new CT results. We consider a successful puncture as one in which we obtain the tissue for pathologic analysis after imaging localization. If the first puncture fails due to inaccurate imaging localization, a second puncture is required, which is usually successful. The cost of the second puncture was 8,266.34 RMB more than the cost of the first puncture. We registered the diagnostic time as the time gap between the date of the first imaging test ([ 18 F]FDG PET/CT or CT) and the date of the final accurate diagnosis. All intervals were calculated in calendar days including weekends and holidays. All data were obtained from the patients’ medical records.. Reference standard All included patients underwent bone biopsy. A biopsy was performed by an interventional radiologist under CT guidance using the standard procedure of our radiology department. The puncture site was selected based on the presence of hypermetabolic bone lesions, represented by SUVmax, in the [18F]FDG PET/CT group. In the CT group, the puncture site was selected by an interventional radiologist based on the CT scan (Fig. 1 ). Pathologists first decalcified and evaluated bone specimens during routine work at our hospital. Only one tissue sample was obtained from each patient after a biopsy, and all biopsies yielded sufficient tissue to perform a pathological test. The reference standard was the pathological result of bone biopsy. Outcomes The primary objective was to compare the accuracy, sensitivity, and specificity of [ 18 F]FDG PET/CT and CT in diagnosing bone metastasis. The secondary outcome was the comparison of the second biopsy rate and cost-effectiveness. The experimental outcome was the application of metabolic indicators of PET/CT and bone turnover markers to differentiate malignant metastatic bone lesions from benign lesions and to improve the diagnostic efficacy of [ 18 F]FDG PET/CT. Statistical analysis The characteristics of the included patients were compared using the Fisher's exact test for binary data and the Wilcoxon rank-sum test for non-normally distributed continuous data. All tests were two-sided, and P-values less than 0.05 were considered statistically significant. The statistical analyses were performed using STATA/IC version 15.1 (Stata Corp., LLC). Receiver Operating Characteristic (ROC) curves were drawn using MedCalc version 19.0.4 (MedCalc Software). The ROC curve was constructed to obtain the cutoff value of SUVmax and ALP in diagnosing bone metastases. Logistic regression analysis was performed to identify independent factors for the diagnosis of bone metastases. Variables with P < 0.05 in multivariate analysis were independent diagnostic factors. The area under the curve (AUC) was calculated separately, along with 95% confidence intervals (CI). The cutoff value was determined using the best Youden index on the ROC curves analyzed using MedCalc version 19.0.4. All diagnostic outcomes were based on patient-based analysis. The cost-effectiveness analysis was conducted using TreeAge Pro® 2021, R2 software[14]. A simplifed model is shown in Fig. 2 and the full model diagram can be assessed in the supplementary. Results Baseline characteristics of patients Between January 2020 and March 2021, 273 patients were enrolled in this prospective cohort study; 137 patients were randomly assigned to the [ 18 F]FDG PET/CT group and 136 patients were randomly assigned to the CT group. All patients underwent bone biopsy at a site located by 18 [F]FDG PET/CT or CT (Fig. 3 ). The characteristics of the enrolled patients are summarized in Table 1 . Sex, age, bone metastases character, and KPS score in the two groups have no statistical differences between the two groups ( P > 0.05). Table 1 The characteristic of enrolled patients Patients Characteristics 18F-FDG PET-CT (n = 137) CT (n = 136) P -value Gender Male, n (%) 90 (65.7) 75 (55.1) 0.075 Female, n (%) 47 (34.3) 61 (44.9) Age (years) 0.4226 Mean ± SD 57.11 ± 14.96 58.39 ± 11.11 Bone metastases character, n (%) 0.0695 Lytic, n (%) 76 (33.6) 59 (43.4) Blastic, n (%) 24 (17.5) 23 (16.9) Mixed, n (%) 37 (48.9) 54 (39.7) KPS score 0.1019 ≥ 80, n (%) 63 (46.0) 76 (55.9) 60–70, n (%) 74 (54.0) 60 (44.1) Imaging test result Bone metastases, n (%) 83 (60.5) 89 (65.5) 0.4059 Benign bone lesions, n(%) 54 (39.5) 47 (34.5) Final diagnosis 0.2285 Bone metastases, n (%) 81 (59.1) 90 (66.2) Benign bone lesions, n(%) 56 (40.9) 46 (33.8) All bone biopsies were successfully performed. The final pathological findings for each group are presented in Supplementary Table 1. No significant differences in baseline were observed ( P > 0.05). Diagnostic performance 81 patients had malignant bone metastases and 56 patients had benign bone lesions in [ 18 F]FDG PET/CT group (Table S1 ). [ 18 F]FDG PET/CT detected 80 out of 81 actual malignant metastases, and misinterpreted 3 benign lesions as malignant bone metastases. This resulted in a sensitivity of 98.8% (95%CI 93.3–99.9%) and a specificity of 94.6% (95%CI 85.1–98.9%).Of the 136 biopsies in the CT group, 89 were positive for malignant bone metastases and 47 were benign bone lesions. CT detected 83 out of 90 actual malignant metastases and misinterpreted 6 benign lesions as malignant bone metastases. This resulted in a sensitivity of 92.2% (95%CI 84.6–96.8%) and specificity of 86.9% (95%CI 73.7–95.1%)(Table 2 ). Table 2 Comparison of the diagnostic performance and cost between the PET/CT and CT groups Imaging test 18F-FDG PET/CT group CT group P valule Diagnostic accuracy (n, %) 97.08 (133/137) 90.44 (123/136) 0.0232* Diagnostic sensitivity (n, %) 98.76 (80/81) 92.22 (83/90) 0.0394* Diagnostic specificity (n, %) 94.64 (53/56) 86.96 (40/46) 0.1134* Second biospsy rate (n, %) 2.19 (3/137) 5.15 (7/136) 0.031* Diagnostic time (mean ± SD, day) 18.33 ± 2.08 21.28 ± 1.25 0.021 # * χ 2 test, # Independent-samples t-test [ 18 F]FDG PET/CT had a significantly higher sensitivity than CT for detecting malignant metastases ( P = 0.0394), and the specificity of the two groups showed no difference ( P = 0.1134). The accuracy of diagnosing bone lesions via [ 18 F]FDG PET/CT was 97.1%, compared to 90.4% via CT, and [ 18 F]FDG PET/CT was significantly superior to CT in terms of bone lesion diagnosis performance ( P = 0.0232) (Table 2 ). Cost-effectiveness In the [ 18 F]FDG PET/CT group, the rate of a second biopsy resulting from an unsatisfactory biopsy was 2.19%, which was significantly lower than that of the CT group (5.15%; P = 0.031). The diagnostic time caused by the second biopsy was 18.33 ± 2.08 days in PET/CT group, which significantly shorter than the 21.28 ± 1.25 days in CT group ( P = 0.021)(Table 2 ). The average daily cost of treatment during the diagnostic time was 859.34 RMB per day. We compared the cost-effectiveness analysis of the two imaging modalities in terms of diagnostic time and secondary puncture, and the results of Treeage's decision-tree modeling resulted in a superior cost-effectiveness of PETCT compared to CT (Table 3 , Fig. 4 ).The cost of [18F] FDG PETCT is 11428.35 yuan, and the cost of CT is 13287.52 yuan; the incremental cost is 1859.17 yuan. Table 3 Cost-effectiveness report of [18F] FDG PETCT and CT Strategy Dominance Cost (rmb) Incremental Cost (rmb) Effectiveness [18F] FDG PETCT undominated 11428.35 1 CT abs. dominated 13287.52 1859.166766 1 As summarized, age (median 63 vs. 54.5, P = 0.0308), ALP (median 126 vs. 87, P = 0.0011), and SUVmax (median 9.4 vs 4.25, P <0.0001) were significantly higher in patients with malignant metastases than in those with benign lesions (Table S2). Univariate logistic regression analysis showed that age, ALP level, and SUVmax were significantly correlated with malignant lesions. Subsequent multivariate logistic regression analysis showed that ALP levels and SUVmax significantly correlated with metastatic malignancies (Table 4 ). Table 4 Patient characteristics of [ 18 F]FDG PET/CT group and the univariate& multivariate logistic regression analysis results on bone metastases and benign lesions Characteristics Univariate Analysis OR (95% CI) P value Multivariate Analysis OR (95% CI) P value Age(year) 1.029 (1.005,1.054) 0.017 1.008 (0.974,1.044) 0.65 ALP (U/L) 1.038 (1.022,1.055) < 0.001 1.035 (1.014,1.056) 0.001 Calcium (mmol/L) 1.833 (0.306,10.996) 0.507 0.653(0.062,6.851) 0.723 Phosphorus (mmol/L) 0.892 (0.340, 2.343) 0.817 1.546(0.409,5.833) 0.521 SUVmax 1.66(1.392,1.979) < 0.001 1.515(1.263,1.818) 6.3 could indicate malignant tumor metastases with an AUC of 0.873 (95% CI 0.806–0.924, P 103 U/L also showed a tendency for tumor metastases with an AUC of 0.793 (95%CI 0.716–0.858, P <0.001). With the two factors combined, when SUVmax of the lesions reached 6.3 and blood ALP are above 103 U/L, there was a possibility of 0.901 that the patient had a metastatic bone tumor (95%CI 0.839 to 0.946, P <0.001) (Fig. 5 , Table 5 ). Table 5 Diagnostic characteristics of SUVmax, serum ALP and combined metrics Diagnostic outcomes SUVmax ALP Combined SUVmax + ALP Cutoff value 6.3 103 Sensitivity 82.72 67.90 76.54 Specificity 85.71 82.14 96.64 AUC 0.873 0.793 0.901 95% CI 0.806 to 0.924 0.716 to 0.858 0.839 to 0.946 PETCT can also be used to differentiate bone metabolic lesions due to bone metastases and inflammatory diseases. In [ 18 F]FDG PET/CT group, we further subgrouped patients with bone metastases and inflammatory bone lesions. Univariate logistic regression analysis showed that age, ALP level, and SUVmax were significantly correlated with bone metastases. Subsequent multivariate logistic regression analysis showed that ALP levels and SUVmax significantly correlated with bone metastases (Table 6 ). Table 6 Patient characteristics of [ 18 F]FDG PET/CT group and the univariate& multivariate logistic regression analysis results on bone metastases and inflammatory bone lesions Characteristics Univariate Analysis OR (95% CI) P value Multivariate Analysis OR (95% CI) P value Age(year) 1.027(1.001,1.054) 0.042 1.007(0.972,1.044) 0.697 ALP (U/L) 1.025(1.011,1.040) 0.000434 1.025 (1.007.1.043) 0.007 Calcium (mmol/L) 1.999(0.271,14.722) 0.497 1.470(0.087,24.719) 0.789 Phosphorus (mmol/L) 0.747(0.256,2.180) 0.593 1.657(0.345,7.959) 0.528 SUVmax 1.641(1.361,1.979) 0.00000021519 1.600991(1.320,1.941) 0.000002 ROC curve analysis showed that SUVmax > 6.2 could indicate bone metastases with an AUC of 0.871 (95% CI 0.799 to 0.924, P 87 U/L also showed a tendency for bone metastases with an AUC of 0.738 (95%CI 0.651 to 0.813, P <0.001). With the two factors combined, there was a possibility of 0.898 that the patient had bone metastases (95%CI 0.837 to 0.958, P <0.001) (Fig. 6 , Table 7 ). Table 7 Diagnostic characteristics of SUVmax, serum ALP and combined metrics on bone metastases and inflammatory bone lesions Diagnostic outcomes SUVmax ALP Combined SUVmax + ALP Cutoff value 6.2 87 Sensitivity 83.95 72.84 76.54 Specificity 83.72 74.42 96.64 AUC 0.871 0.738 0.898 95% CI 0.799 to 0.924 0.651 to 0.813 0.837 to 0.958 Discussion Bone metastasis is an indicator of advanced lesions, and timely diagnosis is very important. The most widely used techniques include [ 18 F]FDG PET/CT and CT. Bone metastases, which are immeasurable lesions, can occur in the entire body of patients with tumors. Most bone metastases occur in the axial skeleton, particularly in the spine, ribs, pelvis, femur, and skull, more than the appendicular skeleton[15]. Both have proven vital for detecting bone metastases[8]. However, no consensus has been reached regarding the best imaging test for bone metastasis detection, making it difficult for clinicians to treat [16]. Our study was a prospective study based on a medical center specializing in various bone diseases that examined patients with possible bone metastases using [ 18 F]FDG PET/CT and CT, and performed percutaneous biopsy under CT guidance. The main finding of our study is that [ 18 F]FDG PET/CT, compared to CT, is optimal for bone metastasis detection. CT-guided biopsy is often recommended for the initial diagnosis of skeletal lesions and has been accepted as safe, minimally invasive, and cost-effective for diagnostic confirmation, thereby preventing the need for riskier and more invasive open surgical biopsy procedures in most patients. Previous studies have also described the success rate of CT-guided biopsy in identifying morphologically clear bone lesions to be in the range of 69–90%; however, the success rate of biopsies may be unexpectedly lower for lesions that are characterized by their metabolic information rather than by anatomic structure[7, 17]. The success rate of CT-guided biopsy is unsatisfactory. [ 18 F]FDG PET/CT can not only target morphologically clear lesions but also metabolically active areas. PET/CT inherent quantitative nature enables accurate, reproducible measurements of radiopharmaceutical uptake in the tumour during diagnostic work-up[18]. PET/CT is also recommended as a workup for potential bone metastases (evidence-level category 2 B) by the National Comprehensive Cancer Network (NCCN) guidelines version 3.2023. [ 18 F]FDG PET/CT and CT, which is the optimal imaging test for bone metastases? Many studies have compared the detection efficiencies of these two imaging tests, but none of them have been prospective. In a retrospective study by Guo et al., [ 18 F]FDG PET/CT yielded a high diagnostic success rate for evaluating bone lesions in patients[9]. The first-time diagnostic success rate of biopsy was 96.1%, which was inferior to the diagnostic success rate of the PET/CT group in our study. A retrospective study by Wu et al. reported that the overall diagnostic yield of [ 18 F]FDG PET/CT in initial biopsies was significantly higher than that in the CT group [15]. In a study by Cornelis et al., [ 18 F]FDG PET/CT allowed high diagnostic success of percutaneous biopsies for metabolically active lesions that are difficult to see with conventional cross-sectional imaging[19]. Other retrospective analyses have shown consistent results, the superiority of PET/CT in retrospective studies is obvious[20]. Our study compared [ 18 F]FDG PET/CT with CT in a prospective randomized controlled trial for the first time and confirmed that [ 18 F]FDG PET/CT has higher diagnostic accuracy and sensitivity than CT. Moreover, PET/CT's role extends beyond initial diagnosis, as it can also be used to monitor the response to treatment, assess the progression of disease, and detect recurrence. The sensitivity and specificity of PET/CT in detecting metabolic changes make it a cornerstone in the multidisciplinary approach to cancer care, particularly for those cancers known to have a high propensity for bone metastasis, such as breast, prostate, and lung cancers[21, 22]. Our study is the first prospective study to compare the clinical indicators. Considering the high cost of [ 18 F]FDG PET/CT, CT may be more practical in this field. However, CT can lead to false-negative diagnoses and unsatisfactory pathological results that could not reach diagnoses[23]. For example, in our study, seven patients in CT group did not get diagnoses during the first pathological biopsy, compared to [ 18 F]FDG PET/CT, only three patients failed to reach diagnoses at the first biopsy. Owing to the limitations of CT devices, if the first biopsy didn’t get the appropriate tissue,a secondary puncture must be done. The diagnostic time was prolonged due to the second biopsy and the costs went up.. PET/CT has shown significant promise for reducing the cost of cancer management by improving the accuracy of both diagnosis and staging, thereby helping to avoid expensive, futile intervetions and associated side effects. Overall, PET/CT has the potential to improve the diagnoses and reduce the cost burden over time to the healthcare system. However, the potential of PET as a tool to help in the management of cancer patients has not yet been reflected in the extent of its adoption. Many studies have analysed the cost-effectiveness of PET/CT for preoperative staging of NSCLC and have demonstrated that the clinical use of PET/CT for staging of the disease leads to significant decreases in surgery and radiotherapy rates[24]. Cost-effectiveness analyses performed alongside the studies have established that PET/CT provides accurate preoperative staging of NSCLC and its use leads to cost savings. The systematic use of PET/CT has been shown to decrease the number of futile thoracotomies and lower the costs associated with lung cancer diagnosis and treatment [25, 26]. Similar results on head and neck squamous cell carcinoma follow-up are shown. Patients receiving PET/CT surveillance had equal survival probability, while unnecessary surgery and potential complications were avoided, and its use saved £1,492 per patient for the duration of the study[27].High uptake of SUVs represents an area with severe bone turnover. As a metabolic index, SUV can distinguish benign from malignant masses, viable from non-viable masses, or biologically aggressive from non-aggressive regions of malignant masses[17]. In [ 18 F]FDG PET/CT, the site of SUVmax indicates the possible malignant location, where the biopsy can obtain a meaningful sample for subsequent pathological and molecular tests. Yao et al. reported an SUVmax of 5 to predict bone metastasis, and that SUVmax could be a valuable noninvasive predictor of EGFR mutations in lung adenocarcinoma[28, 29].Our study showed that SUVmax > 6.3 indicates that the bone lesion is malignant, which is higher than that reported in the literature, and can be attributed to a minority of extreme values [30]. Our data were based on a prospective study with a large sample size that used bone pathological examination as a gold standard and ALP as an additional index. Compared with previous studies, our study had the highest AUC[28, 30]. These cutoff values could be helpful when choosing the most suitable site for bone biopsy. The goal of diagnostic imaging is to detect skeletal metastases early, whenever suspected, on the basis of clinical or laboratory findings. Bone turnover markers (BTMs) are substances released in the blood and urine that can reflect bone resorption and formation during the remodeling process and can be used to predict the risk of bone metastases. ALP, serum calcium, and phosphorus levels indicate bone osteoblast activity[12, 31]. In our prospective study, many patients did not undergo comprehensive BTM tests but only underwent routine blood tests, which merely covered the blood ALP tests and serum calcium and phosphorus levels. Univariate and multivariate logistic regression analyses were performed to determine whether these markers were associated with malignant bone metastases. Serum calcium and phosphorus levels are strongly affected by disease and diet; therefore, they are not stable indicators that can be used to diagnose bone metastases. In contrast, ALP indicates relatively stable bone metabolism. This study has some limitations. The prospective study is single-center and the sample size was limited. Only one metabolic index and one serum index were statistically significant in assisting in diagnosing bone lesion malignancy. More significant indicators can be used for prediction. Conclusion [ 18 F]FDG PET/CT has better performance and cost-effectiveness than CT for bone metastasis detection. SUVmax and ALP levels can further improve the detection efficiency of [ 18 F]FDG PET/CT. Abbreviations CT : computed tomography [18F]FDG PET/CT : [18F] fluorodeoxyglucose positron emission tomography/computed tomography AUC : area under ROC curve ALP : alkaline phosphatase) SREs : skeletal-related events QoL : quality of life SUV : standardized uptake values ROI : region of interest ROC : receiver operating characteristic CI : confidence intervals NCCN : National Comprehensive Cancer Network BTM : Bone turnover markers Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of the Shanghai Sixth People’s Hospital. The principles of the Declaration of Helsinki were adhered to. Written informed consent was obtained from all patients. Consent for publication The consent for publication was obtained from all patients involved. Availability of data and materials The datasets generated and analysed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request. Competing interests The authors declare no potential conflicts of interest. Funding Not applicable Authors' contributions YC : Formal analysis, writing - original draft. YG : Methodology , writing - review & editing. SR : validation. SX : Methodology. JS : Investigation. ZJ : data curation. GY: conceptualization. ZW : Project administration. HZ : Project administration , supervision. All authors read and approved the final manuscript. Acknowledgements Not applicable References Coleman R, Hadji P, Body J-J, Santini D, Chow E, Terpos E, et al. Bone health in cancer: ESMO Clinical Practice Guidelines. Annals of Oncology. 2020;31:1650–63. Ardakani AHG, Faimali M, Nystrom L, Mesko N, Mughal M, Ware H, et al. 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TreeAge Software, Williamstown, MA; software available at http://www.treeage.com. Wu M-H, Xiao L-F, Liu H-W, Yang Z-Q, Liang X-X, Chen Y, et al. PET/CT-guided versus CT-guided percutaneous core biopsies in the diagnosis of bone tumors and tumor-like lesions: which is the better choice? Cancer Imaging. 2019;19:69. Yang H-L, Liu T, Wang X-M, Xu Y, Deng S-M. Diagnosis of bone metastases: a meta-analysis comparing 18 FDG PET, CT, MRI and bone scintigraphy. Eur Radiol. 2011;21:2604–17. Hoffman JM, Gambhir SS. Molecular imaging: the vision and opportunity for radiology in the future. Radiology. 2007;244:39–47. Fischer BM, Siegel BA, Weber WA, von Bremen K, Beyer T, Kalemis A. PET/CT is a cost-effective tool against cancer: synergy supersedes singularity. Eur J Nucl Med Mol Imaging. 2016;43:1749–52. Cornelis F, Silk M, Schoder H, Takaki H, Durack JC, Erinjeri JP, et al. Performance of intra-procedural 18-fluorodeoxyglucose PET/CT-guided biopsies for lesions suspected of malignancy but poorly visualized with other modalities. Eur J Nucl Med Mol Imaging. 2014;41:2265–72. Rodrigues M, Stark H, Rendl G, Rettenbacher L, Datz L, Studnicka M, et al. Diagnostic performance of [18F] FDG PET-CT compared to bone scintigraphy for the detection of bone metastases in lung cancer patients. Q J Nucl Med Mol Imaging. 2016;60:62–8. Vijayakumar S, Yang J, Nittala MR, Velazquez AE, Huddleston BL, Rugnath NA, et al. Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy. Cureus. 14:e32840. Hadebe B, Harry L, Ebrahim T, Pillay V, Vorster M. The Role of PET/CT in Breast Cancer. Diagnostics (Basel). 2023;13:597. Hunink MGM, Gazelle GS. CT screening: a trade-off of risks, benefits, and costs. J Clin Invest. 2003;111:1612–9. Redistribution of health care costs after the adoption of positron emission tomography among medicare beneficiaries with non-small-cell lung cancer, 1998-2005 - PubMed. https://pubmed.ncbi.nlm.nih.gov/24736074/. Accessed 24 Aug 2024. Verboom P, van Tinteren H, Hoekstra OS, Smit EF, van den Bergh JHAM, Schreurs AJM, et al. Cost-effectiveness of FDG-PET in staging non-small cell lung cancer: the PLUS study. Eur J Nucl Med Mol Imaging. 2003;30:1444–9. Søgaard R, Fischer BMB, Mortensen J, Højgaard L, Lassen U. Preoperative staging of lung cancer with PET/CT: cost-effectiveness evaluation alongside a randomized controlled trial. Eur J Nucl Med Mol Imaging. 2011;38:802–9. Mehanna H, Wong W-L, McConkey CC, Rahman JK, Robinson M, Hartley AGJ, et al. PET-CT Surveillance versus Neck Dissection in Advanced Head and Neck Cancer. N Engl J Med. 2016;374:1444–54. Yao G, Zhou Y, Gu Y, Wang Z, Yang M, Sun J, et al. A Retrospective Study of predicting risk of Metastasis among FDG-avid Bone Lesions in 18 F-FDG PET/CT. J Cancer. 2020;11:4989–95. Yao G, Zhou Y, Gu Y, Wang Z, Yang M, Sun J, et al. Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis. J Cancer. 2020;11:5511–7. Gomi D, Fukushima T, Kobayashi T, Sekiguchi N, Koizumi T, Oguchi K. Fluorine‐18‐fluorodeoxyglucose‐positron emission tomography evaluation in metastatic bone lesions in lung cancer: Possible prediction of pain and skeletal‐related events. Thorac Cancer. 2019;10:980–7. Coleman R, Brown J, Terpos E, Lipton A, Smith MR, Cook R, et al. Bone markers and their prognostic value in metastatic bone disease: clinical evidence and future directions. Cancer Treat Rev. 2008;34:629–39. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2024 Read the published version in Cancer Imaging → Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviews received at journal 09 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviewers agreed at journal 29 Aug, 2024 Reviewers invited by journal 29 Aug, 2024 Editor assigned by journal 27 Aug, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 24 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4969944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356786613,"identity":"21a8ffb8-814d-4314-afc3-15e27440f94b","order_by":0,"name":"Yujie Chang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Chang","suffix":""},{"id":356786614,"identity":"fee8be2c-1e12-44e6-b85e-cf58e4e6106c","order_by":1,"name":"Yifeng Gu","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Gu","suffix":""},{"id":356786615,"identity":"de280a34-e583-49d3-859e-64d30ceaa421","order_by":2,"name":"Shunyi Ruan","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shunyi","middleName":"","lastName":"Ruan","suffix":""},{"id":356786616,"identity":"d83dc0ad-629a-40e1-a9ba-bd78440b36d2","order_by":3,"name":"Shengyu Xu","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Xu","suffix":""},{"id":356786617,"identity":"ab3ce0e5-14ae-4640-9b6b-cdffb761331b","order_by":4,"name":"Jing Sun","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Sun","suffix":""},{"id":356786618,"identity":"9343ff22-d0a5-432e-ae5c-fee58b769f1c","order_by":5,"name":"Zhiyuan Jiang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Jiang","suffix":""},{"id":356786619,"identity":"53120810-d0fc-41b6-828d-406790b4526f","order_by":6,"name":"Guangyu Yao","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangyu","middleName":"","lastName":"Yao","suffix":""},{"id":356786620,"identity":"72dc093f-e019-4e15-a164-3917618995cf","order_by":7,"name":"Zhiyu Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiyu","middleName":"","lastName":"Wang","suffix":""},{"id":356786621,"identity":"cc229d09-0dd1-44e8-9f60-afd195b52dd3","order_by":8,"name":"Hui Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACCSBmbGDg4WdgSGCAsInVItnAkNhAkhYGgwMQ1YS1yM9ufviAccdhGePzB54/5mGwkd1wgPnZA3xaGOccMzZgPHOYx+xGQmIzD0Oa8YYDbOYG+LQwSySYSf9tA2lhAGk5nLjhAA+bBD4tbBLp3yQYgVqM+w+AtPwnrIVHIscMrMWAAeywA4S1SEjkFBswtqXzSAD9MnOOQbLxzMNsZni1yM9I3/iAsc3anr//TMKHNxV2sn3Hm5/h1YLsxgRg7ABpZiLVAwH7AeLVjoJRMApGwYgCAM5RRU8++qqUAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Jiao Tong University Affiliated Sixth People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-08-24 15:57:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4969944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4969944/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40644-024-00804-6","type":"published","date":"2024-11-24T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66863190,"identity":"10d80205-3579-425b-9bda-4d568708b391","added_by":"auto","created_at":"2024-10-17 08:43:31","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61221,"visible":true,"origin":"","legend":"\u003cp\u003e18F-FDG PET/CT, CT and bone biospy images of bone metastases are shown. A 63-year-old man who represented with lytic metastatic lesions of spine with lung adenocarcinoma (A-C), the possible bone metastasis showed in CT and 18F-FDG PET/CT. The HE staining confirmed tumor metastasis. A 67-year-old manwith no abnormal lesions was seen in CT (D), while a hypermetabolic bone lesion was showed in 18F-FDG PET/CT (E). Bone biospy of hypermetabolic site confirmed the metastatic lung tumor (F). A 79-year-old man with possible metastatic bone tumor was detected by CT(G), while the corresponding site showed no increased bone metabolism(H), the biopsy proved malignant. A 70-year-old man who represented with heterogenous density of the bone, the 18F-FDG PET/CT showed no abnormal lesions(J-K), pathological result showed ‘the tissue obtained was mainly bone marrow and fatty tissue and tumor cells were not seen(L).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/84c48a5f84fe56a5cbfe19f1.jpeg"},{"id":66863788,"identity":"46eeba98-bb85-471b-a870-8057e4eb5043","added_by":"auto","created_at":"2024-10-17 08:51:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22144,"visible":true,"origin":"","legend":"\u003cp\u003eDecision tree illustrating the compared imaging test for bone metastases.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/d78c77b770647e38420a0511.png"},{"id":66863195,"identity":"31fe68d9-86ba-430c-b744-7411f341b803","added_by":"auto","created_at":"2024-10-17 08:43:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149324,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the prospective study\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/a20599a969348994ba185cfd.png"},{"id":66863791,"identity":"579826b0-ad28-439e-bca8-8f2e9c9072fb","added_by":"auto","created_at":"2024-10-17 08:51:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25703,"visible":true,"origin":"","legend":"\u003cp\u003eThe cost-effectiveness analysis of [18F]FDG PET/CT and CT.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/9d2555bce780283dedd7b3c3.png"},{"id":66863192,"identity":"cf07c06d-bb97-4381-9e06-db2e1c62d60c","added_by":"auto","created_at":"2024-10-17 08:43:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16133,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve analysis of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/5e0de05735d8ca2cabe69b68.png"},{"id":66863196,"identity":"8273af42-4890-447c-b7be-2087bcd3842a","added_by":"auto","created_at":"2024-10-17 08:43:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17673,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve analysis of subgroup in [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/44631e5d0ed197fbec063a5e.png"},{"id":69834862,"identity":"9dad6391-ac39-4f6a-961f-2c30ac34a56c","added_by":"auto","created_at":"2024-11-25 16:09:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/b9775a5f-14e0-4099-af7a-9e249564f2ef.pdf"},{"id":66865073,"identity":"6302028f-311f-427f-9bad-cfd3a673767f","added_by":"auto","created_at":"2024-10-17 08:59:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":54993,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4969944/v1/a245e75651dcc179af527205.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the detection performance of [18F]FDG PET/CT with CT on bone metastases: randomized controlled clinical trial","fulltext":[{"header":"Background","content":"\u003cp\u003eBone is a common site of metastatic solid tumors[1]. More than 50% of all cancers develop bone metastases[2]. Bone is the third most common site of tumor metastasis, with only the lungs and liver having a higher metastatic rate[3]. Bone metastases often cause skeletal complications known as skeletal-related events (SREs), including pathological fractures, radiotherapy, bone surgery, spinal cord compression, and hypercalcemia. SREs can cause loss of mobility and social functioning, further reducing the quality of life (QoL), increasing healthcare expenditure, and worsening survival[4]. Early evaluation and diagnosis of bone metastases are important to ensure effective treatment.\u003c/p\u003e \u003cp\u003ePathological examination, which mainly involves bone biopsy, is the gold standard for the diagnosis of malignant bone metastasis. Biopsy of bone lesions has a diagnostic accuracy ranging from 66 to 98%, which is a significant difference[5]. Complications of bone biopsy include pain, osteomyelitis, and hematoma. If the metastatic site is adjacent to the lungs or spinal cord, puncture may cause pneumothorax and nerve root irritation[6, 7]. Owing to the risks of invasive examinations, conducting imaging tests before biopsies to locate optimal puncture sites can improve the success rate of diagnosis and minimize potential complications.\u003c/p\u003e \u003cp\u003eImaging tests, including bone scintigraphy, computed tomography (CT) and [\u003csup\u003e18\u003c/sup\u003eF] fluorodeoxyglucose positron emission tomography/computed tomography ([\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT) are commonly used for screening bone metastases. Bone scintigraphy is highly sensitive but usually has low specificity[8]. The sensitivity and specificity of CT in detecting malignant bone metastases are not superior to those of other traditional imaging tools (including bone scintigraphy)[9]. [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT is a promising tool that combines metabolic index standardized uptake values (SUV) with traditional imaging tools. Metabolically active bone lesions on [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT can result from primary or metastatic malignant tumors or benign bone diseases[10]. SUVmax has been proven to be a metabolic parameter in oncology[11]. It can be used as a reliable semi-quantitative indicator to differentiate metastatic bone lesions from normal tissues. Although [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT has several clinical advantages, it is not covered by basic medical insurance in China; therefore, undergoing [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT can increase the financial burden of patients. Therefore, CT remains the primary choice for pre-biopsy imaging to determine optimal puncture sites. However, secondary punctures for CT localization may also increase diagnostic time and patient economic burden due to the poor accuracy of CT-localized biopsies.\u003c/p\u003e \u003cp\u003eMalignant tumor metastasis mainly involves bone remodeling, including bone resorption and formation. Serum calcium, phosphorus, alkaline phosphatase (ALP), and other indicators can indicate bone turnover and evaluate the progression of bone lesion progress[1]. Serological examinations are widely used in clinical practice to inspect bone metastases because of the convenience of noninvasive detection[12]. These indicators must be complemented by imaging test[13].\u003c/p\u003e \u003cp\u003eConsequently, this prospective study aimed to determine whether [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT could locate a puncture site more accurately than CT to improve the diagnostic rate of biopsy. Moreover, we attempted to determine the best cutoff value of clinical indicators for differentiating malignant bone metastases using a noninvasive examination and [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e This prospective, single-center, comparative imaging study was approved by the Ethics Committee of the Shanghai Sixth People\u0026rsquo;s Hospital and registered in the Chinese Clinical Trial Registry (ChiCTR1800015540). The principles of the Declaration of Helsinki were adhered to. Written informed consent was obtained from all patients.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were: 1) referral for diagnostic workup for bone disease, 2) age older than 18 years, and 3) Karnofsky performance status of at least 60.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were: 1) contraindications for biopsies (such as infection at the puncture sites), 2) severe bleeding metastases (such as severe hemophilia or severe disseminated intramuscular coagulopathy), and 3) random blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;11.1 mmol/L.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProcedures\u003c/h2\u003e \u003cp\u003eAfter being included according to the abpve criteria, all patients were randomly assigned to the CT or [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group at a ratio of 1:1. The two groups underwent the corresponding imaging tests separately to locate the optimal puncture sites before diagnostic biopsies. The demographic characteristics, SUVmax of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT, and serological test results (including serum ALP, calcium, and phosphorus) of the two groups were collected from our center and analyzed using a de-identification method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProcedure of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT\u003c/h2\u003e \u003cp\u003eAll patients were required to fast for at least 6h and undergo a peripheral blood sugar test to avoid hyperglycemia. Approximately 1 h after the intravenous injection of [\u003csup\u003e18\u003c/sup\u003eF]FDG [333-5\u003csup\u003e18\u003c/sup\u003eMBq (9-14mCi)], imaging was performed using an integrated PET/CT system (Discovery VCT; GE Medical Systems) from the head to the lower limbs with the following settings: CT scan, 120 V and 80 mA, 64 slices, with a slice thickness of 3.75 mm. PET scans were performed with 2.5 min per bed position. Finally, CT and PET images were reconstructed using ordered subset expectation maximization. Attenuation correction was performed using unenhanced CT. A senior nuclear medicine doctor evaluated all combined [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT scans. The region of interest (ROI) around the bone lesions was drawn on [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT images of each transaxial slice. SUVmax was defined as the peak value of the pixel with the highest count within the ROI. CT was performed regularly. The [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT images are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All imaging data were anonymized and randomized. The CT and [\u003csup\u003e18\u003c/sup\u003eF]-FDG PET/CT examinations were read in consensus by three radiologists with 20 years of radiological experience.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003ePerformance and costs\u003c/h2\u003e \u003cp\u003eThe performance of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT was assessed in terms of the accuracy of detecting bone lesions, which were determined for detecting the malignant and benign lesions in the two groups by comparing the imaging test result with the reference standard.\u003c/p\u003e \u003cp\u003eIf the imaging test showed an indicator of malignant metastases and the biopsy was unsatisfactory owing to the lack of tumor cells, patients underwent another invasive biopsy to exclude a possible malignant diagnosis. In the [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group, in which the imaging test itself could cover the entire body, patients underwent another biopsy via other hypermetabolic puncture sites. Due to the limited visual field of CT, patients in the CT group may need to undergo anothor CT test to detect possible metabolic concentration foci, and then may undergo a seconf biopsy via the puncture site determined by new CT results.\u003c/p\u003e \u003cp\u003eWe consider a successful puncture as one in which we obtain the tissue for pathologic analysis after imaging localization. If the first puncture fails due to inaccurate imaging localization, a second puncture is required, which is usually successful. The cost of the second puncture was 8,266.34 RMB more than the cost of the first puncture.\u003c/p\u003e \u003cp\u003eWe registered the diagnostic time as the time gap between the date of the first imaging test ([\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT or CT) and the date of the final accurate diagnosis. All intervals were calculated in calendar days including weekends and holidays. All data were obtained from the patients\u0026rsquo; medical records..\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference standard\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAll included patients underwent bone biopsy. A biopsy was performed by an interventional radiologist under CT guidance using the standard procedure of our radiology department. The puncture site was selected based on the presence of hypermetabolic bone lesions, represented by SUVmax, in the [18F]FDG PET/CT group. In the CT group, the puncture site was selected by an interventional radiologist based on the CT scan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Pathologists first decalcified and evaluated bone specimens during routine work at our hospital. Only one tissue sample was obtained from each patient after a biopsy, and all biopsies yielded sufficient tissue to perform a pathological test. The reference standard was the pathological result of bone biopsy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary objective was to compare the accuracy, sensitivity, and specificity of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT in diagnosing bone metastasis. The secondary outcome was the comparison of the second biopsy rate and cost-effectiveness. The experimental outcome was the application of metabolic indicators of PET/CT and bone turnover markers to differentiate malignant metastatic bone lesions from benign lesions and to improve the diagnostic efficacy of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe characteristics of the included patients were compared using the Fisher's exact test for binary data and the Wilcoxon rank-sum test for non-normally distributed continuous data. All tests were two-sided, and P-values less than 0.05 were considered statistically significant. The statistical analyses were performed using STATA/IC version 15.1 (Stata Corp., LLC). Receiver Operating Characteristic (ROC) curves were drawn using MedCalc version 19.0.4 (MedCalc Software). The ROC curve was constructed to obtain the cutoff value of SUVmax and ALP in diagnosing bone metastases. Logistic regression analysis was performed to identify independent factors for the diagnosis of bone metastases. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multivariate analysis were independent diagnostic factors. The area under the curve (AUC) was calculated separately, along with 95% confidence intervals (CI). The cutoff value was determined using the best Youden index on the ROC curves analyzed using MedCalc version 19.0.4. All diagnostic outcomes were based on patient-based analysis. The cost-effectiveness analysis was conducted using TreeAge Pro\u0026reg; 2021, R2 software[14]. A simplifed model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the full model diagram can be assessed in the supplementary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e \u003cp\u003eBetween January 2020 and March 2021, 273 patients were enrolled in this prospective cohort study; 137 patients were randomly assigned to the [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group and 136 patients were randomly assigned to the CT group. All patients underwent bone biopsy at a site located by \u003csup\u003e18\u003c/sup\u003e[F]FDG PET/CT or CT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The characteristics of the enrolled patients are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Sex, age, bone metastases character, and KPS score in the two groups have no statistical differences between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristic of enrolled patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18F-FDG\u003c/p\u003e \u003cp\u003ePET-CT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;137)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.11\u0026thinsp;\u0026plusmn;\u0026thinsp;14.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone metastases character, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLytic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlastic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKPS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;70, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImaging test result\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone metastases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign bone lesions, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone metastases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign bone lesions, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll bone biopsies were successfully performed. The final pathological findings for each group are presented in Supplementary Table\u0026nbsp;1. No significant differences in baseline were observed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance\u003c/h2\u003e \u003cp\u003e81 patients had malignant bone metastases and 56 patients had benign bone lesions in [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT detected 80 out of 81 actual malignant metastases, and misinterpreted 3 benign lesions as malignant bone metastases. This resulted in a sensitivity of 98.8% (95%CI 93.3\u0026ndash;99.9%) and a specificity of 94.6% (95%CI 85.1\u0026ndash;98.9%).Of the 136 biopsies in the CT group, 89 were positive for malignant bone metastases and 47 were benign bone lesions. CT detected 83 out of 90 actual malignant metastases and misinterpreted 6 benign lesions as malignant bone metastases. This resulted in a sensitivity of 92.2% (95%CI 84.6\u0026ndash;96.8%) and specificity of 86.9% (95%CI 73.7\u0026ndash;95.1%)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the diagnostic performance and cost between the PET/CT and CT groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eImaging test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18F-FDG PET/CT group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCT group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e valule\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic accuracy (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.08\u003c/p\u003e \u003cp\u003e(133/137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.44\u003c/p\u003e \u003cp\u003e(123/136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0232*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic sensitivity (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.76\u003c/p\u003e \u003cp\u003e(80/81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.22\u003c/p\u003e \u003cp\u003e(83/90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0394*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic specificity (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.64\u003c/p\u003e \u003cp\u003e(53/56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.96\u003c/p\u003e \u003cp\u003e(40/46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1134*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond biospsy rate (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003cp\u003e(3/137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003cp\u003e(7/136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic time (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003eχ\u003csup\u003e2\u003c/sup\u003e test, \u003csup\u003e#\u003c/sup\u003e Independent-samples t-test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT had a significantly higher sensitivity than CT for detecting malignant metastases (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0394), and the specificity of the two groups showed no difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1134). The accuracy of diagnosing bone lesions via [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT was 97.1%, compared to 90.4% via CT, and [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT was significantly superior to CT in terms of bone lesion diagnosis performance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0232) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCost-effectiveness\u003c/h2\u003e \u003cp\u003eIn the [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group, the rate of a second biopsy resulting from an unsatisfactory biopsy was 2.19%, which was significantly lower than that of the CT group (5.15%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). The diagnostic time caused by the second biopsy was 18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08 days in PET/CT group, which significantly shorter than the 21.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 days in CT group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average daily cost of treatment during the diagnostic time was 859.34 RMB per day. We compared the cost-effectiveness analysis of the two imaging modalities in terms of diagnostic time and secondary puncture, and the results of Treeage's decision-tree modeling resulted in a superior cost-effectiveness of PETCT compared to CT (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).The cost of [18F] FDG PETCT is 11428.35 yuan, and the cost of CT is 13287.52 yuan; the incremental cost is 1859.17 yuan.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost-effectiveness report of [18F] FDG PETCT and CT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost (rmb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncremental Cost (rmb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffectiveness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[18F] FDG PETCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eundominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11428.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabs. dominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13287.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1859.166766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs summarized, age (median 63 vs. 54.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0308), ALP (median 126 vs. 87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011), and SUVmax (median 9.4 vs 4.25, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) were significantly higher in patients with malignant metastases than in those with benign lesions (Table S2). Univariate logistic regression analysis showed that age, ALP level, and SUVmax were significantly correlated with malignant lesions. Subsequent multivariate logistic regression analysis showed that ALP levels and SUVmax significantly correlated with metastatic malignancies (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group and the univariate\u0026amp; multivariate logistic regression analysis results on bone metastases and benign lesions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate Analysis OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate Analysis OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003cp\u003e(1.005,1.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003cp\u003e(0.974,1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003cp\u003e(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003cp\u003e(1.022,1.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003cp\u003e(1.014,1.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.833\u003c/p\u003e \u003cp\u003e(0.306,10.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653(0.062,6.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003cp\u003e(0.340, 2.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.546(0.409,5.833)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66(1.392,1.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.515(1.263,1.818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis showed that SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;6.3 could indicate malignant tumor metastases with an AUC of 0.873 (95% CI 0.806\u0026ndash;0.924, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), could indicate malignant tumor metastases, and ALP\u0026thinsp;\u0026gt;\u0026thinsp;103 U/L also showed a tendency for tumor metastases with an AUC of 0.793 (95%CI 0.716\u0026ndash;0.858, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). With the two factors combined, when SUVmax of the lesions reached 6.3 and blood ALP are above 103 U/L, there was a possibility of 0.901 that the patient had a metastatic bone tumor (95%CI 0.839 to 0.946, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic characteristics of SUVmax, serum ALP and combined metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic\u003c/p\u003e \u003cp\u003eoutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombined SUVmax\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutoff value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.806 to 0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716 to 0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839 to 0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePETCT can also be used to differentiate bone metabolic lesions due to bone metastases and inflammatory diseases. In [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group, we further subgrouped patients with bone metastases and inflammatory bone lesions. Univariate logistic regression analysis showed that age, ALP level, and SUVmax were significantly correlated with bone metastases. Subsequent multivariate logistic regression analysis showed that ALP levels and SUVmax significantly correlated with bone metastases (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group and the univariate\u0026amp; multivariate logistic regression analysis results on bone metastases and inflammatory bone lesions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate Analysis OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate Analysis OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.027(1.001,1.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007(0.972,1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003cp\u003e(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.025(1.011,1.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003cp\u003e(1.007.1.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.999(0.271,14.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.470(0.087,24.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.747(0.256,2.180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.657(0.345,7.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.641(1.361,1.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00000021519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.600991(1.320,1.941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eROC curve analysis showed that SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;6.2 could indicate bone metastases with an AUC of 0.871 (95% CI 0.799 to 0.924, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and ALP\u0026thinsp;\u0026gt;\u0026thinsp;87 U/L also showed a tendency for bone metastases with an AUC of 0.738 (95%CI 0.651 to 0.813, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). With the two factors combined, there was a possibility of 0.898 that the patient had bone metastases (95%CI 0.837 to 0.958, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic characteristics of SUVmax, serum ALP and combined metrics on bone metastases and inflammatory bone lesions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnostic\u003c/p\u003e \u003cp\u003eoutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUVmax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombined SUVmax\u0026thinsp;+\u0026thinsp;ALP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCutoff value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.799 to 0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.651 to 0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837 to 0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBone metastasis is an indicator of advanced lesions, and timely diagnosis is very important. The most widely used techniques include [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT. Bone metastases, which are immeasurable lesions, can occur in the entire body of patients with tumors. Most bone metastases occur in the axial skeleton, particularly in the spine, ribs, pelvis, femur, and skull, more than the appendicular skeleton[15]. Both have proven vital for detecting bone metastases[8]. However, no consensus has been reached regarding the best imaging test for bone metastasis detection, making it difficult for clinicians to treat [16]. Our study was a prospective study based on a medical center specializing in various bone diseases that examined patients with possible bone metastases using [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT, and performed percutaneous biopsy under CT guidance. The main finding of our study is that [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT, compared to CT, is optimal for bone metastasis detection.\u003c/p\u003e \u003cp\u003eCT-guided biopsy is often recommended for the initial diagnosis of skeletal lesions and has been accepted as safe, minimally invasive, and cost-effective for diagnostic confirmation, thereby preventing the need for riskier and more invasive open surgical biopsy procedures in most patients. Previous studies have also described the success rate of CT-guided biopsy in identifying morphologically clear bone lesions to be in the range of 69\u0026ndash;90%; however, the success rate of biopsies may be unexpectedly lower for lesions that are characterized by their metabolic information rather than by anatomic structure[7, 17]. The success rate of CT-guided biopsy is unsatisfactory. [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT can not only target morphologically clear lesions but also metabolically active areas. PET/CT inherent quantitative nature enables accurate, reproducible measurements of radiopharmaceutical uptake in the tumour during diagnostic work-up[18]. PET/CT is also recommended as a workup for potential bone metastases (evidence-level category 2 B) by the National Comprehensive Cancer Network (NCCN) guidelines version 3.2023.\u003c/p\u003e \u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT and CT, which is the optimal imaging test for bone metastases? Many studies have compared the detection efficiencies of these two imaging tests, but none of them have been prospective. In a retrospective study by Guo et al., [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT yielded a high diagnostic success rate for evaluating bone lesions in patients[9]. The first-time diagnostic success rate of biopsy was 96.1%, which was inferior to the diagnostic success rate of the PET/CT group in our study. A retrospective study by Wu et al. reported that the overall diagnostic yield of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT in initial biopsies was significantly higher than that in the CT group [15]. In a study by Cornelis et al., [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT allowed high diagnostic success of percutaneous biopsies for metabolically active lesions that are difficult to see with conventional cross-sectional imaging[19]. Other retrospective analyses have shown consistent results, the superiority of PET/CT in retrospective studies is obvious[20]. Our study compared [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT with CT in a prospective randomized controlled trial for the first time and confirmed that [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT has higher diagnostic accuracy and sensitivity than CT. Moreover, PET/CT's role extends beyond initial diagnosis, as it can also be used to monitor the response to treatment, assess the progression of disease, and detect recurrence. The sensitivity and specificity of PET/CT in detecting metabolic changes make it a cornerstone in the multidisciplinary approach to cancer care, particularly for those cancers known to have a high propensity for bone metastasis, such as breast, prostate, and lung cancers[21, 22].\u003c/p\u003e \u003cp\u003eOur study is the first prospective study to compare the clinical indicators. Considering the high cost of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT, CT may be more practical in this field. However, CT can lead to false-negative diagnoses and unsatisfactory pathological results that could not reach diagnoses[23]. For example, in our study, seven patients in CT group did not get diagnoses during the first pathological biopsy, compared to [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT, only three patients failed to reach diagnoses at the first biopsy. Owing to the limitations of CT devices, if the first biopsy didn\u0026rsquo;t get the appropriate tissue,a secondary puncture must be done. The diagnostic time was prolonged due to the second biopsy and the costs went up.. PET/CT has shown significant promise for reducing the cost of cancer management by improving the accuracy of both diagnosis and staging, thereby helping to avoid expensive, futile intervetions and associated side effects. Overall, PET/CT has the potential to improve the diagnoses and reduce the cost burden over time to the healthcare system. However, the potential of PET as a tool to help in the management of cancer patients has not yet been reflected in the extent of its adoption.\u003c/p\u003e \u003cp\u003eMany studies have analysed the cost-effectiveness of PET/CT for preoperative staging of NSCLC and have demonstrated that the clinical use of PET/CT for staging of the disease leads to significant decreases in surgery and radiotherapy rates[24]. Cost-effectiveness analyses performed alongside the studies have established that PET/CT provides accurate preoperative staging of NSCLC and its use leads to cost savings. The systematic use of PET/CT has been shown to decrease the number of futile thoracotomies and lower the costs associated with lung cancer diagnosis and treatment [25, 26]. Similar results on head and neck squamous cell carcinoma follow-up are shown. Patients receiving PET/CT surveillance had equal survival probability, while unnecessary surgery and potential complications were avoided, and its use saved \u0026pound;1,492 per patient for the duration of the study[27].High uptake of SUVs represents an area with severe bone turnover. As a metabolic index, SUV can distinguish benign from malignant masses, viable from non-viable masses, or biologically aggressive from non-aggressive regions of malignant masses[17]. In [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT, the site of SUVmax indicates the possible malignant location, where the biopsy can obtain a meaningful sample for subsequent pathological and molecular tests. Yao et al. reported an SUVmax of 5 to predict bone metastasis, and that SUVmax could be a valuable noninvasive predictor of EGFR mutations in lung adenocarcinoma[28, 29].Our study showed that SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;6.3 indicates that the bone lesion is malignant, which is higher than that reported in the literature, and can be attributed to a minority of extreme values [30]. Our data were based on a prospective study with a large sample size that used bone pathological examination as a gold standard and ALP as an additional index. Compared with previous studies, our study had the highest AUC[28, 30]. These cutoff values could be helpful when choosing the most suitable site for bone biopsy.\u003c/p\u003e \u003cp\u003eThe goal of diagnostic imaging is to detect skeletal metastases early, whenever suspected, on the basis of clinical or laboratory findings. Bone turnover markers (BTMs) are substances released in the blood and urine that can reflect bone resorption and formation during the remodeling process and can be used to predict the risk of bone metastases. ALP, serum calcium, and phosphorus levels indicate bone osteoblast activity[12, 31]. In our prospective study, many patients did not undergo comprehensive BTM tests but only underwent routine blood tests, which merely covered the blood ALP tests and serum calcium and phosphorus levels. Univariate and multivariate logistic regression analyses were performed to determine whether these markers were associated with malignant bone metastases. Serum calcium and phosphorus levels are strongly affected by disease and diet; therefore, they are not stable indicators that can be used to diagnose bone metastases. In contrast, ALP indicates relatively stable bone metabolism.\u003c/p\u003e \u003cp\u003eThis study has some limitations. The prospective study is single-center and the sample size was limited. Only one metabolic index and one serum index were statistically significant in assisting in diagnosing bone lesion malignancy. More significant indicators can be used for prediction.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT has better performance and cost-effectiveness than CT for bone metastasis detection. SUVmax and ALP levels can further improve the detection efficiency of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCT : computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[18F]FDG PET/CT : [18F] fluorodeoxyglucose positron emission tomography/computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC : area under ROC curve\u003c/p\u003e\n\u003cp\u003eALP : alkaline phosphatase)\u003c/p\u003e\n\u003cp\u003eSREs : skeletal-related events\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQoL : quality of life\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSUV : standardized uptake values\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROI : region of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC : receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCI : confidence intervals\u003c/p\u003e\n\u003cp\u003eNCCN : National Comprehensive Cancer Network\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBTM : Bone turnover markers\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Shanghai Sixth People\u0026rsquo;s Hospital. The principles of the Declaration of Helsinki were adhered to. Written informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eThe consent for publication was obtained from all patients involved.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due to privacy or ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eYC : Formal analysis, writing - original draft. YG : Methodology , writing - review \u0026amp; editing. SR : validation. SX : Methodology. JS : Investigation. ZJ : data curation. GY: conceptualization. ZW : Project administration. HZ : Project administration , supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eColeman R, Hadji P, Body J-J, Santini D, Chow E, Terpos E, et al. Bone health in cancer: ESMO Clinical Practice Guidelines. Annals of Oncology. 2020;31:1650\u0026ndash;63.\u003c/li\u003e\n\u003cli\u003eArdakani AHG, Faimali M, Nystrom L, Mesko N, Mughal M, Ware H, et al. Metastatic bone disease: Early referral for multidisciplinary care. Cleve Clin J Med. 2022;89:393\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ePiccioli A, Maccauro G, Spinelli MS, Biagini R, Rossi B. Bone metastases of unknown origin: epidemiology and principles of management. J Orthop Traumatol. 2015;16:81\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eCl\u0026eacute;zardin P, Coleman R, Puppo M, Ottewell P, Bonnelye E, Paycha F, et al. Bone metastasis: mechanisms, therapies, and biomarkers. Physiol Rev. 2021;101:797\u0026ndash;855.\u003c/li\u003e\n\u003cli\u003eFilippiadis DK, Charalampopoulos G, Mazioti A, Keramida K, Kelekis A. Bone and Soft-Tissue Biopsies: What You Need to Know. Semin Intervent Radiol. 2018;35:215\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eEspinosa LA, Jamadar DA, Jacobson JA, DeMaeseneer MO, Ebrahim FS, Sabb BJ, et al. CT-guided biopsy of bone: a radiologist\u0026rsquo;s perspective. AJR Am J Roentgenol. 2008;190:W283-289.\u003c/li\u003e\n\u003cli\u003eSaifuddin A, Palloni V, du Preez H, Junaid SE. Review article: the current status of CT-guided needle biopsy of the spine. Skeletal Radiol. 2021;50:281\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Sullivan GJ, Carty FL, Cronin CG. Imaging of bone metastasis: An update. World J Radiol. 2015;7:202\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eGuo W, Hao B, Chen H-J, Zhao L, Luo Z-M, Wu H, et al. PET/CT-guided percutaneous biopsy of FDG-avid metastatic bone lesions in patients with advanced lung cancer: a safe and effective technique. Eur J Nucl Med Mol Imaging. 2017;44:25\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eLemans JVC, Hobbelink MGG, IJpma FFA, Plate JDJ, van den Kieboom J, Bosch P, et al. The diagnostic accuracy of 18F-FDG PET/CT in diagnosing fracture-related infections. Eur J Nucl Med Mol Imaging. 2019;46:999\u0026ndash;1008.\u003c/li\u003e\n\u003cli\u003eLodge MA. Repeatability of SUV in Oncologic 18F-FDG PET. J Nucl Med. 2017;58:523\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eBrown JE, Cook RJ, Major P, Lipton A, Saad F, Smith M, et al. Bone turnover markers as predictors of skeletal complications in prostate cancer, lung cancer, and other solid tumors. J Natl Cancer Inst. 2005;97:59\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003eSelvaggi G, Scagliotti GV. Management of bone metastases in cancer: a review. Crit Rev Oncol Hematol. 2005;56:365\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eTreeAge Pro 2021, R1. TreeAge Software, Williamstown, MA; software available at http://www.treeage.com.\u003c/li\u003e\n\u003cli\u003eWu M-H, Xiao L-F, Liu H-W, Yang Z-Q, Liang X-X, Chen Y, et al. PET/CT-guided versus CT-guided percutaneous core biopsies in the diagnosis of bone tumors and tumor-like lesions: which is the better choice? Cancer Imaging. 2019;19:69.\u003c/li\u003e\n\u003cli\u003eYang H-L, Liu T, Wang X-M, Xu Y, Deng S-M. Diagnosis of bone metastases: a meta-analysis comparing \u003csup\u003e18\u003c/sup\u003eFDG PET, CT, MRI and bone scintigraphy. Eur Radiol. 2011;21:2604\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eHoffman JM, Gambhir SS. Molecular imaging: the vision and opportunity for radiology in the future. Radiology. 2007;244:39\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eFischer BM, Siegel BA, Weber WA, von Bremen K, Beyer T, Kalemis A. PET/CT is a cost-effective tool against cancer: synergy supersedes singularity. Eur J Nucl Med Mol Imaging. 2016;43:1749\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eCornelis F, Silk M, Schoder H, Takaki H, Durack JC, Erinjeri JP, et al. Performance of intra-procedural 18-fluorodeoxyglucose PET/CT-guided biopsies for lesions suspected of malignancy but poorly visualized with other modalities. Eur J Nucl Med Mol Imaging. 2014;41:2265\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eRodrigues M, Stark H, Rendl G, Rettenbacher L, Datz L, Studnicka M, et al. Diagnostic performance of [18F] FDG PET-CT compared to bone scintigraphy for the detection of bone metastases in lung cancer patients. Q J Nucl Med Mol Imaging. 2016;60:62\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eVijayakumar S, Yang J, Nittala MR, Velazquez AE, Huddleston BL, Rugnath NA, et al. Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy. Cureus. 14:e32840.\u003c/li\u003e\n\u003cli\u003eHadebe B, Harry L, Ebrahim T, Pillay V, Vorster M. The Role of PET/CT in Breast Cancer. Diagnostics (Basel). 2023;13:597.\u003c/li\u003e\n\u003cli\u003eHunink MGM, Gazelle GS. CT screening: a trade-off of risks, benefits, and costs. J Clin Invest. 2003;111:1612\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eRedistribution of health care costs after the adoption of positron emission tomography among medicare beneficiaries with non-small-cell lung cancer, 1998-2005 - PubMed. https://pubmed.ncbi.nlm.nih.gov/24736074/. Accessed 24 Aug 2024.\u003c/li\u003e\n\u003cli\u003eVerboom P, van Tinteren H, Hoekstra OS, Smit EF, van den Bergh JHAM, Schreurs AJM, et al. Cost-effectiveness of FDG-PET in staging non-small cell lung cancer: the PLUS study. Eur J Nucl Med Mol Imaging. 2003;30:1444\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eS\u0026oslash;gaard R, Fischer BMB, Mortensen J, H\u0026oslash;jgaard L, Lassen U. Preoperative staging of lung cancer with PET/CT: cost-effectiveness evaluation alongside a randomized controlled trial. Eur J Nucl Med Mol Imaging. 2011;38:802\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eMehanna H, Wong W-L, McConkey CC, Rahman JK, Robinson M, Hartley AGJ, et al. PET-CT Surveillance versus Neck Dissection in Advanced Head and Neck Cancer. N Engl J Med. 2016;374:1444\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eYao G, Zhou Y, Gu Y, Wang Z, Yang M, Sun J, et al. A Retrospective Study of predicting risk of Metastasis among FDG-avid Bone Lesions in \u003csup\u003e18\u003c/sup\u003e F-FDG PET/CT. J Cancer. 2020;11:4989\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eYao G, Zhou Y, Gu Y, Wang Z, Yang M, Sun J, et al. Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis. J Cancer. 2020;11:5511\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eGomi D, Fukushima T, Kobayashi T, Sekiguchi N, Koizumi T, Oguchi K. Fluorine‐18‐fluorodeoxyglucose‐positron emission tomography evaluation in metastatic bone lesions in lung cancer: Possible prediction of pain and skeletal‐related events. Thorac Cancer. 2019;10:980\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eColeman R, Brown J, Terpos E, Lipton A, Smith MR, Cook R, et al. Bone markers and their prognostic value in metastatic bone disease: clinical evidence and future directions. Cancer Treat Rev. 2008;34:629\u0026ndash;39.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bone Metastasis, Diagnostic Test, Imaging, Positron Emission Tomography, Computed Tomograph, Clinical Trial Results","lastPublishedDoi":"10.21203/rs.3.rs-4969944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4969944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBone biopsy is the gold standard for diagnosing bone metastases. However, there is no clinical consensus regarding the optimal imaging test for locating the puncture site.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe compared the performance of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT with CT in detecting bone metastases to achieve the highest biopsy efficiency. This registered prospective study enrolled 273 patients with bone lesions who were treated between January 2020 and March 2021. Patients were randomly assigned to undergo [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT or CT to locate the puncture site before bone biopsy. The accuracy, sensitivity, specificity, second biopsy rate, diagnostic time and cost-effectiveness of the two imaging tests were compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe accuracy and sensitivity of [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT group in the diagnosis of bone metastases were significantly higher than CT group(97.08% vs. 90.44%, 98.76% vs. 92.22%, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The second biopsy rate was significantly lower in the PET/CT group (2.19% vs. 5.15%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The diagnostic time of PET/CT was 18.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08 days, which was significantly shorter than 21.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25 days in CT group ( \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). The cost of [18F] FDG PETCT is 11428.35 yuan, and the cost of CT is 13287.52 yuan; the incremental cost is 1859.17 yuan. SUVmax\u0026thinsp;\u0026gt;\u0026thinsp;6.3 combined with ALP\u0026thinsp;\u0026gt;\u0026thinsp;103 U/L showed a tendency for tumor metastases with an AUC of 0.901 (95%CI 0.839 to 0.946, P\u0026lt;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e[\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT is more effective and less costly than CT in locating optimal bone biopsy site. Thus, [\u003csup\u003e18\u003c/sup\u003eF]FDG PET/CT should be considered the optimal imaging test for locating the optimal puncture site for bone biopsy.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eThe prospective study was registered on 20180410, and the registration number is ChiCTR1800015540.\u003c/p\u003e","manuscriptTitle":"Comparison of the detection performance of [18F]FDG PET/CT with CT on bone metastases: randomized controlled clinical trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 08:43:27","doi":"10.21203/rs.3.rs-4969944/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-20T10:53:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T06:55:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T19:51:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127566060783320247627840315869257519819","date":"2024-08-31T14:41:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130445111792910214013082817513747937872","date":"2024-08-29T06:26:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-29T06:13:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-27T13:28:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-27T09:37:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Imaging","date":"2024-08-24T15:56:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caig","sideBox":"Learn more about [Cancer Imaging](https://cancerimagingjournal.biomedcentral.com/)","snPcode":"40644","submissionUrl":"https://submission.nature.com/new-submission/40644/3","title":"Cancer Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20507dc6-5191-4887-b68b-9ca49e12b950","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-4969944","link":"https://doi.org/10.1186/s40644-024-00804-6","journal":{"identity":"cancer-imaging","isVorOnly":false,"title":"Cancer Imaging"},"publishedOn":"2024-11-24 15:57:26","publishedOnDateReadable":"November 24th, 2024"},"versionCreatedAt":"2024-10-17 08:43:27","video":"","vorDoi":"10.1186/s40644-024-00804-6","vorDoiUrl":"https://doi.org/10.1186/s40644-024-00804-6","workflowStages":[]},"version":"v1","identity":"rs-4969944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4969944","identity":"rs-4969944","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}