Diagnostic Performance of Quantitative Ga-SPECT/CT for Patients with Lower-limb Osteomyelitis

Diagnostic Performance of Quantitative Ga-SPECT/CT for Patients with Lower-limb Osteomyelitis | 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 Diagnostic Performance of Quantitative Ga-SPECT/CT for Patients with Lower-limb Osteomyelitis Yoshito Nishikawa, Yoshimitsu Fukushima, Sonoko Kirinoki, Gen Takagi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1835166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Patients with lower-limb osteomyelitis (LLOM) may experience major adverse events, such as lower-leg amputations or death; therefore, early diagnosis and risk stratification are essential to improve outcomes. Ga-scintigraphy is commonly used for diagnosing inflammatory diseases. Until fairly recently, conventional imaging and SPECT were the most common; however, the diagnostic performance of planar and SPECT imaging for localized lesions is limited. While localized imaging using Ga-SPECT/CT is an emerging approach to improve diagnoses, its diagnostic performance has not been sufficiently evaluated to date. Therefore, this study aimed to evaluate the diagnostic performance of Ga-SPECT/CT with quantitative analyses for patients with LLOM. Methods A total of 103 consecutive patients suspected of LLOM between April 2012 and October 2016 were analyzed. All patients underwent Ga-scintigraphy with SPECT/CT imaging. Findings were assessed visually, with higher than background accumulation considered positive, and quantitatively, using Ga-SPECT/CT images to calculate the inflammation-to-background ratio (IBR), the maximum standardized uptake value (SUVmax), and total lesion uptake (TLU). Diagnoses were confirmed using pathological examinations and patient outcomes, and diagnostic performances of planar, SPECT, and SPECT/CT images were compared. To evaluate prognostic performance, all patients were observed for 5 years for occurrences of major adverse events (MAE), defined as recurrence of osteomyelitis, major leg amputation, or fatal event. Multivariate Cox regression was performed to evaluate outcome factors. Results The overall diagnoses indicated that 54 out of 103 patients had LLOM. IBR, SUVmax, and TLU were significantly higher in patients with LLOM (12.23 vs. 1.00, 4.85 vs. 1.34, and 68.77 vs. 8.63, respectively; p < 0.001). Sensitivity and specificity were 91% and 96% for SPECT/CT with IBR, 89% and 94% for SPECT/CT with SUVmax, and 91% and 92% for SPECT/CT with TLU, respectively. MAE occurred in 23 of 54 LLOM patients (43%). TLU was found to be an independent prognostic factor (p = 0.047). Conclusions Ga-SPECT/CT using quantitative parameters, namely, IBR and TLU, had better diagnostic and prognostic performances for patients with LLOM compared to conventional imaging. The results suggest that Ga-SPECT/CT is a good alternative for diagnosing LLOM in countries where FDG-PET/CT is not commonly available. SPECT/CT quantitative analysis osteomyelitis diagnostic performance prognostic value Figures Figure 1 Figure 2 Figure 3 Introduction Normal bone tissue has high resistance to infection; however, some incidents can cause osteomyelitis, such as large-volume inoculations, trauma, and the presence of foreign bodies [ 1 , 2 ]. Osteomyelitis has been observed since the earliest recorded history, with its symptoms described in the oldest medical texts, such as the Edwin Smith papyrus from the seventeenth century BC, and identified in hominid fossils [ 3 ]. In the past 30 years, the incidence of osteomyelitis has nearly tripled among older adults, primarily caused by a drastic increase in diabetes mellitus [ 4 ]. Osteomyelitis is a difficult-to-diagnose refractory disease characterized by progressive inflammatory destruction and new bone deposition [ 5 , 6 ]. Osteomyelitis significantly impacts quality of life and can be fatal [ 7 ]. Early diagnosis and treatment are crucial for a favorable prognosis [ 8 ]. Various imaging modalities have been used to diagnose osteomyelitis. X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) are widely used for initial diagnosis [ 9 , 10 ]. While CT only shows advanced osteomyelitis with osteolysis due to limited contrast resolution, it produces osteo-images with high spatial resolution within moments [ 11 ]. However, there is a substantial loss of image resolution when metal is present in or near the area of osteomyelitis [ 12 ]. MRI images have a lower spatial resolution than CT images; however, MRI images possess a high contrast resolution, providing excellent anatomic delineation of the infected area and surrounding soft tissues [ 12 , 13 ]. Unfortunately, MRI specificity for diagnosing LLOM is relatively low due largely to the inability to differentiate between osteomyelitis and bone marrow edema [ 14 ]. In addition, distinguishing bone marrow abnormalities related to LLOM from soft tissue inflammation is important for diagnostic accuracy [ 13 ]. Radionuclide imaging is crucial for diagnosing LLOM [ 9 ]. 67 Ga-citrate (Ga) scintigraphy has long been one of the standard diagnostic imaging methods, recognized as being capable of identifying active inflammatory lesions [ 15 ]. Ga injected into the bloodstream accumulates in LLOM lesions through increased inflammatory cell uptake and increased receptor density [ 16 , 17 ]. Ga-scintigraphy includes planar imaging and single-photon emission computed tomography combined with CT (SPECT/CT) imaging. The spatial resolution and contrast resolution of planar images are low, making it difficult to differentiate LLOM from soft tissue infections, such as cellulitis, resulting in weak or moderate diagnostic performance [ 18 , 19 ]. SPECT/CT, which simultaneously acquires functional (scintigraphic uptake) and anatomical (low-dose X-ray CT) definition [ 20 – 22 ], has resulted in significant improvements to diagnostic confidence [ 21 ]. SPECT/CT using white blood cell (WBC) tracers (WBC-SPECT/CT) can identify active inflammatory lesions with higher spatial resolution and specificity than Ga-SPECT/CT [ 19 , 21 ]. 18 F-Fluorodeoxyglucose (FDG) positron emission computed tomography combined with CT (PET/CT) can identify active inflammatory lesions with remarkably higher spatial resolution than Ga-SPECT/CT [ 23 ]. However, WBC-SPECT/CT and FDG-PET/CT generally cannot be used in Japan due to technical and insurance limitations. As such, Ga-SPECT/CT is the best available nuclear medicine modality for identifying LLOM inflammation activity in Japan to date. PET/CT utilizing quantitative analyses with standardized uptake value (SUV) has been widespread, while the development of quantitative analysis software, such as GI-BONE (AZE Co., Ltd, Kawasaki, Japan), has enabled the use of SUV for analyses with SPECT/CT [ 24 , 25 ]. However, quantitative analyses using SPECT/CT data have not been applied to evaluate inflammatory activity in LLOM to date. This study aimed to evaluate the diagnostic accuracy and prognostic value of quantitative Ga-SPECT/CT for patients with LLOM. Materials And Methods Study design Patient selection This study examined 111 consecutive patients suspected of LLOM who underwent Ga-scintigraphy between April 2012 and October 2016. Six patients were excluded due to incomplete observation resulting from hospital transfer. Two patients were excluded due to incomplete treatment resulting from the exacerbation of comorbidities. Consequently, 103 patients suspected of LLOM who underwent Ga-scintigraphy (76 men and 27 women, 67 [55–74] years) were analyzed as shown in Fig. 1 . Ga-scintigraphy imaging procedures All patients underwent Ga scintigraphy. SPECT/CT images were obtained 48 hours after the injection of 148 MBq of Ga. In addition, conventional planar images were simultaneously obtained for 38 patients. Acquisitions were performed using a SPECT/CT system that contains a dual-head gamma camera with a two-row multi-section CT scanner, Symbia T2 (Siemens Healthcare Japan, Tokyo, Japan). SPECT images were acquired over 15 minutes per bed position (30 projections over an orbit of 180°, 6° per step, and 30 seconds per projection). Acquisition range was limited to 2 bed positions from the toes, including the entirety of the lower legs and feet. An MEGP collimator was used for acquisitions with a matrix size of 128 × 128 pixels. SPECT images were reconstructed using an iterative image reconstruction algorithm, Flash3D. Reconstruction parameters for the number of subsets and iterations were 6 and 8, respectively. Non-contrast-enhanced CT scans (tube voltage, 110 kVp; tube current-time product, 10–40 mA; detector configuration, 2 × 4 mm; matrix, 512 × 512 pixels; reconstruction thickness, 5 mm for entire leg and 3mm for foot) were also performed to obtain morphological data. CT attenuation correction was used to create SPECT/CT images. Definitive clinical diagnoses were established by the primary physicians using a combination of physical examinations, medical tests, including diagnostic imaging, and clinical outcomes. Patients diagnosed as positive were considered positive for LLOM (LLOM group), while patients diagnosed as negative were considered negative for LLOM (non-LLOM group). In addition, LLOM with cellulitis (CE) can interfere with diagnosis, as it may be difficult to distinguish pure CE and LLOM with CE. While LLOM without CE does not present this problem, the presence of both LLOM and CE is not rare [ 26 ]. Therefore, patients in both the LLOM and non-LLOM groups were additionally classified based on clinical diagnoses of CE, resulting in four groups: LLOM and CE positive (LLOM-CE), LLOM positive and CE negative (LLOM only), LLOM negative and CE positive (CE only), and LLOM negative and CE negative (LLOM-CE negative). Data analysis Planar, SPECT, and CT images were independently assessed visually and quantitatively by two radiologists in order to identify LLOM. Planar images were visually analyzed to compare lesion accumulation with background accumulation in the unaffected side leg. Lesions with higher accumulation than background on planar and SPECT images were classified as positive, while those with lower and similar accumulation were classified as negative for LLOM. Lesions on SPECT and SPECT/CT images were compared to unaffected muscle tissue, and lesions with higher accumulation to background were classified as positive, while those with lower and similar accumulation were classified as negative for LLOM. Osteolytic and sclerotic lesions on CT images were classified as positive for LLOM, and anatomical data obtained from CT images were utilized to identify the precise location of lesions in SPECT/CT images. Following standard procedures, cases that were identified as positive on CT images and planar or SPECT images were considered SPECT/CT positive for LLOM as presented in Table 1. In addition, CE was evaluated for differentiation from LLOM visually using planar, SPECT, CT, and SPECT/CT images. Soft tissue density and accumulation in subdermal sites were analyzed using SPECT, CT, and SPECT/CT images. Higher than regular fat density and abnormal accumulation were considered positive for CE. Quantitative analyses were performed on lesions suspected to be LLOM-related and CE-related using SPECT/CT data. Inflammation-to-background ratio (IBR) was calculated by dividing maximal count in each lesion accumulation by the mean count of accumulation in the bone marrow of both distal femurs. Furthermore, standardized uptake values (SUV) and total lesion uptake (TLU) were calculated using the quantitative analysis software, GI-BONE. Volume of interest (VOI) threshold was set at 50% of peak value, and maximum SUV (SUVmax) and TLU were calculated. IBR, SUVmax, and TLU cut-off values were determined using a receiver operation curve (ROC) analysis based on definitive diagnoses by primary physicians described above. Diagnostic values, prognostic values, and visual and quantitative results were compared between the groups. Evaluation of prognosis All patients were observed for five years after their initial lower-limb Ga-scintigraphy for the occurrence of major adverse events (MAE), which were defined as recurrence of LLOM, major amputation, or all-cause mortality. The endpoint for this study was defined as either the occurrence of MAE or end of the observation period. The correlation between the occurrence of MAE and various clinical parameters, including age, results of blood tests, risk factors, and comorbidities, was also analyzed. Statistical analyses Normally distributed continuous variables were expressed as means ± SD and non-normally distributed variables as medians with 25th and 75th percentiles. Categorical variables were presented as percentages and counts. Non-normally distributed continuous variables, such as age, serum CRP levels, and IBR, were compared using the Mann-Whitney U-test. Categorical variables were compared using Fisher’s exact probability test for bivariate data. In order to examine the correlation with future occurrence of MAE, all variables were checked using a univariate Cox regression analysis. Variables with p < 0.05 were considered statistically significant, and the stepwise method was used to select variables for analysis. Multivariate Cox regression was performed on the top four significant variables to identify independently associated factors. All statistical analyses were performed using StatMate IV software version 4.01 (Advanced Technology for Medicine and Science, Tokyo, Japan) and BellCurve for Excel software version 2.13 (Social Survey Research Information, Tokyo, Japan). Results Clinical characteristics A total of 103 patients (67 [55–74] years, 76 men and 27 women) underwent lower-limb perfusion scintigraphy with quantitative SPECT/CT. Conventional planar images were obtained for 38 patients (68 [58–73] years, 29 men and 9 women). Patient characteristics, including medical histories, comorbidities, and blood examination results, are presented in Table 2 . Of the total, 54 patients were clinically diagnosed as having LLOM (LLOM group), while 49 were clinically diagnosed as not having LLOM (non-LLOM group). Table 3 shows patient characteristics for the two groups. Out of 54 patients in the LLOM group, 49 were clinically identified as having CE (LLOM-CE group) and 5 patients were clinically identified as having only LLOM (LLOM only group). In addition, 32 patients were clinically diagnosed with CE only (CE only group) and 17 patients were clinically diagnosed as negative for both LLOM and CE (negative group) (Table 4). -Visual assessment using Ga-scintigraphy Based on a visual assessment using planar images, 12 patients (71%) from the LLOM group and 14 patients (67%) from the non-LLOM group were rated as positive for LLOM. SPECT images identified 43 patients (79%) from the LLOM group and 32 patients (65%) from the non-LLOM group as positive for LLOM. CT images categorized 37 patients (69%) from the LLOM group and 4 patients (8%) from the non-LLOM group as positive for LLOM. SPECT/CT images identified 44 patients (81%) from the LLOM group and 4 patients (8%) from the non-LLOM group as positive for LLOM. Out of the LLOM-CE group, 45 patients (92%), 34 patients (69%) and 39 patients (80%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively (Table 4). Out of the LLOM only group, 4 patients (80%), 3 patients (60%) and 4 patients (80%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively. Out of the CE only group, 32 patients (100%), 3 patients (9%) and 26 patients (81%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively. Out of the negative group, 8 patients (47%), 1 patient (6%) and 6 patients (35%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively. -Quantitative assessment using Ga-scintigraphy Based on clinical diagnosis, IBR, SUVmax, and TLU for the LLOM group were 12.23 (7.38–17.94), 4.85 (3.45–8.31), and 68.77 (22.90–96.63), respectively, and 1.00 (1.00–1.47), 1.34 (1.14–1.62), and 8.63 (1.15–2.33), respectively, for the non-LLOM group (Table 3). The cut-off values for diagnosing LLOM were 1.99 for IBR, 1.74 for SUVmax, and 7.29 for TLU. The IBR, SUVmax, and TLU in the LLOM-CE group were 14.86 (8.91–17.40), 6.36 (3.45–8.35), and 69.80 (22.60–96.99), respectively. The IBR, SUVmax, and TLU in the LLOM-only group were 9.13 (5.40–8.87), 4.88 (2.02–4.87), and 58.66 (35.02–76.34), respectively. The IBR, SUVmax, and TLU in the CE-only group were 2.24 (1.00–1.01), 1.56 (1.14–1.58), and 8.51 (1.31–2.36), respectively. The IBR, SUVmax, and TLU in the negative group were 1.86 (1.00–1.01), 1.58 (1.14–1.49), and 8.87 (1.09–2.30), respectively. The results demonstrated statistically significant differences in IBR, SUVmax, and TLU between the LLOM-CE and CE-only groups (p < 0.001 for all three quantitative parameters). Accuracy of imaging methods As shown in Table 5, the sensitivity and specificity of the planar images were 71% and 33%, respectively. The sensitivity and specificity of the SPECT images were 80% and 35%, respectively. The sensitivity and specificity of the CT images were 69% and 92%, respectively. The sensitivity and specificity of SPECT/CT without quantitative analysis were 81% and 92%, respectively. The sensitivity and specificity of SPECT/CT with IBR were 91% and 96%, respectively. The sensitivity and specificity of SPECT/CT with SUVmax were 89% and 94%, respectively. The sensitivity and specificity of SPECT/CT with TLU were 91% and 92%, respectively. The areas under the ROC curves for the presence of LLOM were 0.957 using IBR, 0.921 using SUVmax, and 0.926 using TLU. Patient prognoses MAE occurred in 23 patients with LLOM (43%). The area under the ROC curve for MAE occurrences was 0.680 for TLU, and the cut-off values for prognosis prediction were 38.35 for TLU. The prevalence of diabetes mellitus and chronic kidney disease as well as WBC, IBR, and TLU were statistically significantly higher among patients who experienced an MAE (Table 6 ). The results of the Cox proportional hazards regression analyses are presented in Table 7. The univariate analysis revealed significant correlations for WBC (p = 0.002), diabetes mellitus (p = 0.012), TLU (p = 0.020), IBR (p = 0.030), and chronic kidney disease (p = 0.049). A multivariate analysis was performed for the top four parameters and demonstrated a statistically significant positive correlation between WBC and MAE (p = 0.003) as well as TLU and MAE (p = 0.047), while IBR showed no statistical significance (p = 0.175). Case studies Figure 2 shows a case of a patient with low TLU. This 68-year-old man developed a fever and increased inflammatory markers after treatment for severe leg trauma. Pretreatment Ga-scintigraphy was conducted, and planar images showed no clear signs of accumulation in the left toes. However, SPECT/CT images revealed increased subcutaneous density and accumulation, indicating CE, around the 4th distal phalanx of the left foot and destruction and mild accumulation in the bone, indicating LLOM. Quantitative analyses were performed using GI-BONE and showed a low SUVmax of 3.25, low IBR of 5.40, and low TLU of 35.02. Recovery from fever and inflammation was smooth, not requiring surgical treatment. This patient did not experience an MAE within the 3-year observation period. Figure 3 shows a case of a patient with high TLU. This 68-year-old man was treated for diabetic gangrene and underwent Ga-scintigraphy to confirm the diagnosis. SPECT/CT images revealed increased subcutaneous density and accumulation indicative of CE near the right 1st proximal phalanx and metatarsal, with bone destruction and distinct accumulation indicative of LLOM. Quantitative analyses using GI-BONE found a low SUVmax of 3.45, high IBR of 12.0, and high TLU of 133.76. Minor amputation was performed, and sequestrum in the affected areas during operation confirmed the diagnosis of LLOM. These findings indicated that the lesion had active chronic inflammation. Thirty-nine days after the initial Ga-scintigraphy, the patient experienced a fatal event. Discussion This study evaluated the diagnostic accuracy and prognostic value of quantitative Ga-SPECT/CT for patients with LLOM by comparing it with other methods and clinical diagnoses. Comparison of Ga-SPECT/CT and other imaging modalities An accurate diagnosis of LLOM is crucial for a favorable outcome. However, providing an accurate diagnosis remains a challenge for imaging modalities [ 27 ]. Utilizing Ga accumulation in inflammatory cells, this study demonstrated that diagnoses with Ga-SPECT/CT using IBR achieved a diagnostic sensitivity and specificity of 91% and 96%, respectively. These results are an improvement over previous attempts lacking quantitative evaluation, which achieved 88% and 94% [ 28 ]. On the other hand, diagnoses using SUVmax achieved a diagnostic sensitivity and specificity of 89% and 94%, respectively, showing no superiority over previous studies that only used visual evaluation. SPECT was superior to CT in sensitivity (80% and 69%, respectively), while CT was superior to SPECT in specificity (92% and 35%, respectively). However, SPECT/CT was superior to both SPECT and CT in both dimensions, which may be due to the improvement of the contrast resolution of SPECT images through CT attenuation correction [ 29 ]. In addition, the synergistic effect of the fusion of SPECT and CT combines anatomical data obtained from CT with functional data obtained from SPECT [ 21 ]. Currently, MRI is the most commonly used diagnostic modality for LLOM; however, its sensitivity and specificity for LLOM caused by diabetes mellitus were 93% and 75%, respectively [ 11 ]. As such, MRI findings are not always sufficient to confidently diagnose LLOM. Noninfectious inflammatory and metabolic conditions of osseous tissue, bone contusions, stress fractures, healing fractures, osteonecrosis, and tumors can all produce signal alterations in some sequences similar to those seen in osteomyelitis, as MRI cannot differentiate edema from inflammation [ 14 , 30 ]. Therefore, Ga-SPECT/CT appears to be more suitable for detecting LLOM and the associated inflammatory activity. FDG-PET/CT has been found to have a sensitivity and specificity for detecting LLOM of 89% and 92%, respectively [ 14 ], which is inferior to the performance of Ga-SPECT/CT with IBR observed in the present study. However, analyses utilizing FDG-PET/CT have not incorporated IBR to date. As such, future research on the possibility of using IBR with FDG-PET/CT may shed more light on this issue. Comparison of quantitative evaluation methods The results demonstrated the diagnostic significance of IBR, SUVmax, and TLU for LLOM; however, while TLU was positively correlated with prognosis, IBR and SUVmax were not statistically significant. This was likely due to the fact that SUVmax was based on a calculated distribution value including tissue concentration, injected dose, and body weight, resulting in data related to inflammation other than LLOM [ 31 ]. On the other hand, IBR was based on a comparison of the affected tissue and mean count in the bone marrow of both distal femurs, avoiding confusion with other sites. Unlike SUV and TLU, IBR-based calculations were similar to a radiologists’ visual interpretation. However, as measurement location, including background, was determined manually, IBR-based calculations could be vulnerable to error. On the other hand, TLU was a more objective measure and more accurate assessment of local inflammatory activity. Prognostic value of Ga-SPECT/CT with quantitative parameters As mentioned, Ga-SPECT/CT is generally not the preferred modality for LLOM in most countries; therefore, its prognostic value has not been investigated to date. The use of Ga-SPECT/CT in the literature has been limited. For instance, Aslangul et al. reported that combined diagnosis with Ga-SPECT/CT and percutaneous bone puncture improved the 1-year outcome of patients with LLOM (4 improved and 15 cured out of 55 patients) [ 28 ]. However, the present study revealed the efficacy of Ga-SPECT/CT as a prognostic tool. The multivariate analysis revealed TLU to be an independent prognostic factor (p = 0.047). The results demonstrated that prognosis was significantly poorer in patients with high TLU than those with low TLU. Similarly, the prognostic value of WBC-SPECT/CT is not well understood, with studies focusing on it as a diagnostic tool. For instance, Vouillarmet et al. reported that patients with positive WBC-SPECT/CT who underwent 12 weeks of medical therapy experienced high prevalence of LLOM relapse during the 1-year observation period (6 out of 13 patients) [ 32 ]. Likewise, the prognostic value of FDG-PET/CT for LLOM has not been determined, as the modality is relatively new. However, FDG-PET/CT has been reported to improve LLOM diagnosis and therapeutic monitoring and effects [ 33 ]. Furthermore, surgery based on FDG-PET/CT images using SUV cut-off values of 2.00–8.00 has a higher potential for procedural success [ 31 ]. This suggests that FDG-PET/CT is likely to have a good prognostic value. However, as mentioned, FDG-PET/CT and WBC-SPECT/CT generally cannot be used in Japan due to technical and insurance limitations. Therefore, Ga-SPECT/CT presents the best available method with a potential for high prognostic value. Chronic osteomyelitis entails a major financial burden and a substantial impact on the quality of life, including both mental and physical aspects [ 4 , 7 ]. Providing an accurate prognosis would allow early intervention and mitigate some of the mental, physical, and financial burdens. This study indicated that quantitative assessment is more precise than visual assessment and enables prognosis stratification. The results provided strong evidence for recommending the utilization of Ga-SPECT/CT for patients with LLOM, at least in countries where FDG-PET/CT is not available or feasible. Future research should investigate this method across Japan and in other countries in order to lend further validity to these results. Possible interference of CE LLOM and CE are frequently difficult to distinguish, as both diseases are caused by infection and the inflammatory sites are in proximity to each other. Due to the possible interference of CE, an analysis was conducted to evaluate differences in quantitative parameters based on the presence of CE. The results demonstrated significant differences in IBR, SUVmax, and TLU between LLOM-CE and CE only. There were no significant differences in IBR, SUVmax, or TLU between the subgroups with and without CE in the non-LLOM group. These results suggest that there is no effect of the presence or absence of CE on quantitative analyses of LLOM. Study limitations This study had some limitations. First, due to the retrospective design of the study, there may be a bias in case selection caused by the initial focus on the indication for surgery. Moreover, for the same reason, clinical examinations may not have been optimized for LLOM, such as injection-to-scan acquisition times. Future research should conduct multicenter randomized controlled trials in order to eliminate this potential bias. Second, Ga-SPECT/CT was chosen over FDG-PET/CT, as Japan’s national health insurance system only covers Ga-scintigraphy for patients with LLOM; therefore, FDG-PET/CT data could not be acquired. As FDG-PET/CT has higher spatial resolution and sensitivity, a lower radiation burden, and a significantly shorter acquisition time compared with Ga-SPECT/CT, it may produce superior results [ 22 , 23 ]. Further research should explore this hypothesis. Furthermore, the sample size of the present study was relatively small. Future studies should aim to include a wider range of participants. Conclusions This study evaluated inflammatory activity in patients with LLOM using quantitative Ga-SPECT/CT. The results indicated that Ga-SPECT/CT using quantitative parameters, namely, IBR, SUVmax, and TLU, had a better diagnostic performance for patients with LLOM compared to planar imaging. In addition, this study found that TLU values were positively correlated with MAE, demonstrating the prognostic assessment potential of Ga-SPECT/CT with TLU, including the ability to stratify the prognosis of patients with LLOM. The results suggest that Ga-SPECT/CT is a good alternative for diagnosing LLOM in countries where FDG-PET/CT is not commonly available. Future studies should conduct further analyses across a range of populations. It should be noted that although Ga-SPECT/CT is an acceptable alternative, most physicians agree that FDG-PET/CT is superior [ 25 ]. Therefore, future policies should strive to allow the implementation of FDG-PET/CT for LLOM whenever possible. Declarations Ethics approval and consent to participate This paper is a single-center retrospective study on LLOM patients from one university hospital (Nippon Medical School Hospital, Tokyo, Japan). Written informed consent was obtained from all participants prior to performing the scan (all participants are legal adults). The study protocol was approved by the institutional ethics committee and classified as a non-interventional study. All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committees and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Nippon Medical School Hospital (approval no. B-2019-061). Consent for publication We followed the retrospective observational research information disclosure procedure (opt-out) of Nippon Medical School when obtaining informed consent from research participants, including permissions to publish research results at conferences and in academic journals. The use of opt-out consent is approved by the Ethics Committee of Nippon Medical School Hospital. The option to opt-out is detailed on the hospital’s website. Availability of data and materials No datasets were generated or analyzed during the current study. Competing interests The authors declare no competing interests. Funding No funding was required during the current study; therefore the authors declare no sources of funding for this research. Authors’ Contributions YN, YF, SKu, GT, and SKi were involved in study design and data interpretation. YN, YF, MS, and TM were involved in the data analysis. All authors critically revised the report, commented on drafts of the manuscript, and approved the final report. Acknowledgements We would like to extend our gratitude to radiology technologists Kyoji Asano and Shinjiro Yoshida for their work with the administration of Ga-SPECT/CT. We would also like to thank the primary physicians for providing care and obtaining the data for this study. 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Eur J Nucl Med Mol Imaging 44:1393–1407 Kagna O, Srour S, Melamed E, Militianu D, Keidar Z (2012) FDG-PET/CT imaging in the diagnosis of osteomyelitis in the diabetic foot. Eur J Nucl Med Mol Imaging 39:1545–1550 Ogura I, Kobayashi E, Nakahara K, Igarashi K, Haga-Tsujimura M, Toshima H (2019) Quantitative SPECT/CT imaging for medication-related osteonecrosis of the jaw: A preliminary study using volume-based parameters, comparison with chronic osteomyelitis. Ann Nucl Med 33:776–782 Hata H, Kitao T, Sato J et al (2020) Monitoring indices of bone inflammatory activity of the jaw using SPECT bone scintigraphy: a study of ARONJ patients. Sci Rep 10:1–9 Klein DA, Lee BH, Bezhani H, Droukas DD, Stoffels G (2020) The Clinical Utility of MRI in Evaluating for Osteomyelitis in Patients Presenting with Uncomplicated Cellulitis. J Foot Ankle Surg 59(2):323–329 Berendt AR, Peters EJG, Bakker K et al (2008) Diabetic foot osteomyelitis: A progress report on diagnosis and a systematic review of treatment. Diab/Metab Res Rev 24:S145–S161 Aslangul E, M'Bemba J, Caillat-Vigneron N et al (2013) Diagnosing diabetic foot osteomyelitis in patients without signs of soft tissue infection by coupling hybrid 67 Ga-SPECT/CT with bedside percutaneous bone puncture. Diabetes Care 36:2203–2210 Seo Y, Wong KH, Sun M, Franc BL, Hawkins RA, Hasegawa BH (2005) Correction of photon attenuation and collimator response for a body-contouring SPECT/CT imaging system. J Nucl Med 46:868–877 Fujii M, Armsrong DG, Terashi H (2013) Efficacy of magnetic resonance imaging in diagnosing diabetic foot osteomyelitis in the presence of ischemia. J Foot Ankle Surg 52:717–723 Takaki M, Takenaka N, Mori K et al (2020) Comparison of histopathology and preoperative 18 F-FDG-PET/CT of osteomyelitis aiming for image guided surgery: A preliminary trial. Injury 51:871–877 Vouillarmet J, Moret M, Morelec I, Michon P, Dubreuil J (2017) Application of white blood cell SPECT/CT to predict remission after a 6 or 12 week course of antibiotic treatment for diabetic foot osteomyelitis. Diabetologia 60:2486–2494 Chatziioannou S, Papamichos O, Gamaletsou MN, Georgakopoulos A, Kostomitsopoulos NG, Tseleni-Balafouta S et al (2015) 18-Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography scan for monitoring the therapeutic response in experimental Staphylococcus aureus foreign-body osteomyelitis. J Orthop Surg Res 10:132 Tables Table 1 Procedure to identify LLOM positive cases using visual assessments Modality Image findings CT Osteolytic and sclerotic lesions Planar imaging Higher accumulation than background SPECT Higher accumulation than background muscle tissue SPECT/CT Osteolytic and sclerotic lesions with higher accumulation than background muscle tissue Table 2 Patient characteristics Number of patients 103 Age (years) 67 (55–74) Male (%) 76 (74%) Blood exam WBC (/μl) 6200 (5150–7750) CRP (mg/l) 1.12 (0.28–3.58) Risk factor Diabetes mellitus (%) 77 (75%) Peripheral artery disease (%) 64 (62%) Cellulitis (%) 81 (79%) Comorbidity Hypertension (%) 52 (50%) Chronic kidney disease (%) 49 (48%) Coronary artery disease (%) 24 (23%) WBC = white blood cell; CRP = C-reactive protein Table 3 Comparison of clinical profiles between LLOM and non-LLOM groups LLOM (n = 54) Non-LLOM (n = 49) P value Age (years) 68 (61–75) 64 (55–74) 0.037 Male (%) 37 (69%) 39 (80%) 0.263 Blood exam WBC (/μl) 6250 (5100–7575) 6200 (5400–8000) 0.731 CRP (mg/l) 1.12 (0.24–3.37) 1.62 (0.28–3.72) 0.907 Risk factor/comorbidity Diabetes mellitus (%) 37 (69%) 40 (82%) 0.173 Peripheral artery disease (%) 31 (57%) 33 (67%) 0.317 Cellulitis (%) 49 (91%) 32 (65%) 0.003 Hypertension (%) 27 (50%) 25 (51%) 0.925 Chronic kidney disease (%) 21 (39%) 28 (57%) 0.098 Coronary artery disease (%) 10 (19%) 14 (29%) 0.331 Imaging findings Positive in planar imaging 12 (71%; n=17) 14 (67%; n=21) 0.796 Positive in SPECT/CT 44 (81%) 4 (8%) <0.001 IBR 12.23 (7.38–17.94) 1.00 (1.00–1.47) <0.001 SUVmax 4.85 (3.45–8.31) 1.34 (1.14–1.62) <0.001 TLU 68.77 (22.90–96.63) 8.63 (1.15–2.33) <0.001 LLOM = lower-limb osteomyelitis; SPECT/CT = single photon emission computed tomography/computed tomography; SUV = standardized uptake value; IBR = inflammation-to-background ratio; TLU = total lesion uptake Table 4 Results based on the presence of LLOM and CE LLOM-CE (n = 49) LLOM only (n = 5) CE only (n = 32) LLOM-CE negative (n = 17) Visual assessment CT positive 34 (69%) 3 (60%) 3 (9%) 1 (6%) SPECT positive 39 (80%) 4 (80%) 26 (81%) 6 (35%) Quantitative assessment IBR 14.86 (8.91–17.40) 9.13 (5.40–8.87) 2.24 (1.00–1.01) 1.86 (1.00–1.01) SUVmax 6.36 (3.45–8.35) 4.88 (2.02–4.87) 1.56 (1.14–1.58) 1.58 (1.14–1.49) TLU 69.80 (22.60–96.99) 58.66 (35.02–76.34) 8.51 (1.31–2.36) 8.87 (1.09–2.30) CE = cellulitis. Table 5 Diagnostic accuracy of imaging modalities Sensitivity (%) Specificity (%) Accuracy (%) Visual assessment Planar imaging 71 33 50 SPECT imaging 80 35 58 CT imaging 69 92 80 SPECT/CT imaging 81 92 86 Quantitative assessment IBR 91 96 94 SUVmax 89 94 92 TLU 91 92 92 Table 6 Clinical profiles of patients with LLOM divided by MAE occurence MAE (n = 23) No MAE (n = 31) P value Age (years) 66 (58–69) 68 (61–76) 0.593 Male (%) 16 (70%) 21 (68%) 0.887 Blood exam WBC (/μl) 6800 (5900–8800) 5600 (4800–6550) 0.008 CRP (mg/l) 2.66 (0.30–3.88) 0.79 (0.26–2.41) 0.07 Risk factor/comorbidity Diabetes mellitus (%) 21 (91%) 16 (52%) 0.001 Peripheral artery disease (%) 14 (61%) 17 (55%) 0.658 Cellulitis (%) 22 (96%) 27 (87%) 0.284 Hypertension (%) 13 (57%) 14 (45%) 0.409 Chronic kidney disease (%) 12 (52%) 9 (29%) 0.084 Coronary artery disease (%) 6 (26%) 7 (23%) 0.766 Imaging findings IBR 18.39 (9.88–17.40) 11.31 (1.00–17.11) 0.017 SUVmax 6.75 (3.45–12.87) 5.83 (2.97–7.78) 0.479 TLU 89.83 (48.00–136.84) 35.02 (19.28–78.53) 0.025 Event-free survival (days) 19 (5.5–57) NA NA MAE = major adverse event Table 7 Univariate and multivariate Cox regression for MAE occurrence Univariate Multivariate HR 95% CI P value HR 95% CI P value Age 0.986 0.955–1.018 0.399 Male 1.010 0.415–2.455 0.983 Blood exam WBC 1.000 1.001–1.001 0.002 1.000 1.000–1.001 0.003 CRP 1.075 0.988–1.169 0.093 Risk factor/comorbidity Diabetes mellitus 6.448 1.508–27.577 0.012 4.081 0.921–18.071 0.064 Peripheral artery disease 1.193 0.516–2.759 0.680 Cellulitis 2.717 0.366–20.166 0.329 Hypertension 1.391 0.610–3.173 0.433 Chronic kidney disease 2.283 1.004–5.191 0.049 Coronary artery disease 1.182 0.465–3.005 0.725 Imaging findings IBR 1.041 1.004–1.080 0.030 1.030 0.987–1.075 0.175 SUVmax 1.047 0.956–1.146 0.325 TLU 1.008 1.001–1.016 0.020 1.006 1.000–1.013 0.047 HR = hazard ratio; CI = confidential interval Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 Jul, 2022 Reviewers invited by journal 12 Jul, 2022 Editor assigned by journal 12 Jul, 2022 Reviewer # 2 agreed at journal 11 Jul, 2022 Reviewer # 1 agreed at journal 11 Jul, 2022 Submission checks completed at journal 11 Jul, 2022 Editor invited by journal 11 Jul, 2022 First submitted to journal 07 Jul, 2022 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. 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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-1835166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":120436246,"identity":"7c1015a7-0d8b-49e8-b6d1-663ca9fc2c28","order_by":0,"name":"Yoshito Nishikawa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLCCBAYGHn4Yh7GBWC2SMJXEaQEBgwNEq7zde0ziwZ/DMsa3u5M/MNTYMTDPJmCNwZ1zaRKJbYd5zO6c3SbBcCyZgXEOAfsMbuSYSSQ2ALXcyN3GwMB2gIFxRgIRWhL+HOYxnpG7+QPDP6K1sB3mMZDI3SDB2EaEFskbOcYWiW3pPBJAh0kk9iXzEPQL340cw5s//ljb84Mc9uGbnZwhoRBTOMDAIsHA0AzhAZ3EYzgDvw4G+QYG5g8MDHVIIhIEtIyCUTAKRsGIAwAm10ZQAgummwAAAABJRU5ErkJggg==","orcid":"","institution":"Nippon Medical School: Nihon Ika Daigaku","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yoshito","middleName":"","lastName":"Nishikawa","suffix":""},{"id":120436247,"identity":"32f8acdb-6fd6-466d-b6cc-371f3896f748","order_by":1,"name":"Yoshimitsu Fukushima","email":"","orcid":"https://orcid.org/0000-0001-6224-4914","institution":"Nippon Medical School Department of Radiology: Nihon Ika Daigaku Rinsho Igaku Hoshasen Igaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yoshimitsu","middleName":"","lastName":"Fukushima","suffix":""},{"id":120436248,"identity":"9bdf600d-2946-44f1-9543-2070f9f2792e","order_by":2,"name":"Sonoko Kirinoki","email":"","orcid":"","institution":"Nippon Medical School: Nihon Ika Daigaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sonoko","middleName":"","lastName":"Kirinoki","suffix":""},{"id":120436249,"identity":"754c4405-3b89-445c-9ec5-5a7138683696","order_by":3,"name":"Gen Takagi","email":"","orcid":"","institution":"Nippon Medical School: Nihon Ika Daigaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gen","middleName":"","lastName":"Takagi","suffix":""},{"id":120436250,"identity":"5beaf096-0058-420b-9a9c-cac54df9bd58","order_by":4,"name":"Masaya Suda","email":"","orcid":"","institution":"Nippon Medical School Hospital: Nihon Ika Daigaku Fuzoku Byoin","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Masaya","middleName":"","lastName":"Suda","suffix":""},{"id":120436251,"identity":"78d70845-4b53-4825-966d-77df30223571","order_by":5,"name":"Toshio Maki","email":"","orcid":"","institution":"Nippon Medical School Hospital: Nihon Ika Daigaku Fuzoku Byoin","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Toshio","middleName":"","lastName":"Maki","suffix":""},{"id":120436252,"identity":"0ea076ec-0d71-47e2-b3e1-c668e9fede2e","order_by":6,"name":"Shinichiro Kumita","email":"","orcid":"","institution":"Nippon Medical School Department of Radiology: Nihon Ika Daigaku Rinsho Igaku Hoshasen Igaku","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shinichiro","middleName":"","lastName":"Kumita","suffix":""}],"badges":[],"createdAt":"2022-07-07 12:12:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1835166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1835166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24055814,"identity":"961e950b-9f9a-4f9b-80ee-b8dd31a786ac","added_by":"auto","created_at":"2022-07-19 18:55:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47992,"visible":true,"origin":"","legend":"\u003cp\u003ePatient selection flowchart\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1835166/v1/3805a3a2755cb85c175871e9.png"},{"id":24056085,"identity":"1e2e854b-0dd5-4af4-90d1-d39270337683","added_by":"auto","created_at":"2022-07-19 19:00:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":515261,"visible":true,"origin":"","legend":"\u003cp\u003eThese images represent a 68-year-old man with LLOM due to leg trauma and dyslipidemia, stable angina, and atherosclerosis obliterans with no MAE. (a) shows Ga-scintigraphy, whole-body, planar images indicating a defect in the left toes with no clear signs of accumulation. (b) shows CT images indicating a resection of the left toes and cellulitis near the left 4th distal phalanx with irregular bone destruction. (c) shows fused SPECT/CT images indicating distinct accumulation in the left 4th distal metatarsal and proximal phalanx with low IBR, SUVmax, and TLU.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1835166/v1/22d73e173136dad2ba6a3ac1.png"},{"id":24055816,"identity":"4c732981-0dbb-4cd6-9c81-17d96e9860b6","added_by":"auto","created_at":"2022-07-19 18:55:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":334863,"visible":true,"origin":"","legend":"\u003cp\u003eThese images represent an 68-year-old man with LLOM due to diabetic gangrene, hypertension, and atherosclerosis obliterans. (a) shows CT images indicating increased subcutaneous density and accumulation indicative of cellulitis near the right 1st proximal phalanx and metatarsal bone with irregular bone destruction. (b) shows fused SPECT/CT images indicating distinct accumulation in the right 1st proximal phalanx and metatarsal bone with high IBR and TLU and low SUVmax. Thirty-nine days after the scanning, the patient experienced a fatal event.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1835166/v1/d2c3194f05a1c605e02f1b52.png"},{"id":24056086,"identity":"7ed978ff-f902-4e9e-bad0-bdd18b564378","added_by":"auto","created_at":"2022-07-19 19:00:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1523142,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1835166/v1/ffa81aba-4d7c-494b-a407-39e8baa02b34.pdf"}],"financialInterests":"","formattedTitle":"Diagnostic Performance of Quantitative Ga-SPECT/CT for Patients with Lower-limb Osteomyelitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNormal bone tissue has high resistance to infection; however, some incidents can cause osteomyelitis, such as large-volume inoculations, trauma, and the presence of foreign bodies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Osteomyelitis has been observed since the earliest recorded history, with its symptoms described in the oldest medical texts, such as the Edwin Smith papyrus from the seventeenth century BC, and identified in hominid fossils [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the past 30 years, the incidence of osteomyelitis has nearly tripled among older adults, primarily caused by a drastic increase in diabetes mellitus [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Osteomyelitis is a difficult-to-diagnose refractory disease characterized by progressive inflammatory destruction and new bone deposition [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Osteomyelitis significantly impacts quality of life and can be fatal [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Early diagnosis and treatment are crucial for a favorable prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious imaging modalities have been used to diagnose osteomyelitis. X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) are widely used for initial diagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While CT only shows advanced osteomyelitis with osteolysis due to limited contrast resolution, it produces osteo-images with high spatial resolution within moments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, there is a substantial loss of image resolution when metal is present in or near the area of osteomyelitis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. MRI images have a lower spatial resolution than CT images; however, MRI images possess a high contrast resolution, providing excellent anatomic delineation of the infected area and surrounding soft tissues [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Unfortunately, MRI specificity for diagnosing LLOM is relatively low due largely to the inability to differentiate between osteomyelitis and bone marrow edema [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, distinguishing bone marrow abnormalities related to LLOM from soft tissue inflammation is important for diagnostic accuracy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRadionuclide imaging is crucial for diagnosing LLOM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003csup\u003e67\u003c/sup\u003eGa-citrate (Ga) scintigraphy has long been one of the standard diagnostic imaging methods, recognized as being capable of identifying active inflammatory lesions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Ga injected into the bloodstream accumulates in LLOM lesions through increased inflammatory cell uptake and increased receptor density [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Ga-scintigraphy includes planar imaging and single-photon emission computed tomography combined with CT (SPECT/CT) imaging. The spatial resolution and contrast resolution of planar images are low, making it difficult to differentiate LLOM from soft tissue infections, such as cellulitis, resulting in weak or moderate diagnostic performance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSPECT/CT, which simultaneously acquires functional (scintigraphic uptake) and anatomical (low-dose X-ray CT) definition [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], has resulted in significant improvements to diagnostic confidence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. SPECT/CT using white blood cell (WBC) tracers (WBC-SPECT/CT) can identify active inflammatory lesions with higher spatial resolution and specificity than Ga-SPECT/CT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003csup\u003e18\u003c/sup\u003eF-Fluorodeoxyglucose (FDG) positron emission computed tomography combined with CT (PET/CT) can identify active inflammatory lesions with remarkably higher spatial resolution than Ga-SPECT/CT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, WBC-SPECT/CT and FDG-PET/CT generally cannot be used in Japan due to technical and insurance limitations. As such, Ga-SPECT/CT is the best available nuclear medicine modality for identifying LLOM inflammation activity in Japan to date.\u003c/p\u003e \u003cp\u003ePET/CT utilizing quantitative analyses with standardized uptake value (SUV) has been widespread, while the development of quantitative analysis software, such as GI-BONE (AZE Co., Ltd, Kawasaki, Japan), has enabled the use of SUV for analyses with SPECT/CT [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, quantitative analyses using SPECT/CT data have not been applied to evaluate inflammatory activity in LLOM to date.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the diagnostic accuracy and prognostic value of quantitative Ga-SPECT/CT for patients with LLOM.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eThis study examined 111 consecutive patients suspected of LLOM who underwent Ga-scintigraphy between April 2012 and October 2016. Six patients were excluded due to incomplete observation resulting from hospital transfer. Two patients were excluded due to incomplete treatment resulting from the exacerbation of comorbidities. Consequently, 103 patients suspected of LLOM who underwent Ga-scintigraphy (76 men and 27 women, 67 [55\u0026ndash;74] years) were analyzed as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGa-scintigraphy imaging procedures\u003c/h2\u003e \u003cp\u003eAll patients underwent Ga scintigraphy. SPECT/CT images were obtained 48 hours after the injection of 148 MBq of Ga. In addition, conventional planar images were simultaneously obtained for 38 patients. Acquisitions were performed using a SPECT/CT system that contains a dual-head gamma camera with a two-row multi-section CT scanner, Symbia T2 (Siemens Healthcare Japan, Tokyo, Japan). SPECT images were acquired over 15 minutes per bed position (30 projections over an orbit of 180\u0026deg;, 6\u0026deg; per step, and 30 seconds per projection). Acquisition range was limited to 2 bed positions from the toes, including the entirety of the lower legs and feet. An MEGP collimator was used for acquisitions with a matrix size of 128 \u0026times; 128 pixels. SPECT images were reconstructed using an iterative image reconstruction algorithm, Flash3D. Reconstruction parameters for the number of subsets and iterations were 6 and 8, respectively. Non-contrast-enhanced CT scans (tube voltage, 110 kVp; tube current-time product, 10\u0026ndash;40 mA; detector configuration, 2 \u0026times; 4 mm; matrix, 512 \u0026times; 512 pixels; reconstruction thickness, 5 mm for entire leg and 3mm for foot) were also performed to obtain morphological data. CT attenuation correction was used to create SPECT/CT images.\u003c/p\u003e \u003cp\u003eDefinitive clinical diagnoses were established by the primary physicians using a combination of physical examinations, medical tests, including diagnostic imaging, and clinical outcomes. Patients diagnosed as positive were considered positive for LLOM (LLOM group), while patients diagnosed as negative were considered negative for LLOM (non-LLOM group).\u003c/p\u003e \u003cp\u003eIn addition, LLOM with cellulitis (CE) can interfere with diagnosis, as it may be difficult to distinguish pure CE and LLOM with CE. While LLOM without CE does not present this problem, the presence of both LLOM and CE is not rare [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, patients in both the LLOM and non-LLOM groups were additionally classified based on clinical diagnoses of CE, resulting in four groups: LLOM and CE positive (LLOM-CE), LLOM positive and CE negative (LLOM only), LLOM negative and CE positive (CE only), and LLOM negative and CE negative (LLOM-CE negative).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003ePlanar, SPECT, and CT images were independently assessed visually and quantitatively by two radiologists in order to identify LLOM. Planar images were visually analyzed to compare lesion accumulation with background accumulation in the unaffected side leg. Lesions with higher accumulation than background on planar and SPECT images were classified as positive, while those with lower and similar accumulation were classified as negative for LLOM.\u003c/p\u003e \u003cp\u003eLesions on SPECT and SPECT/CT images were compared to unaffected muscle tissue, and lesions with higher accumulation to background were classified as positive, while those with lower and similar accumulation were classified as negative for LLOM. Osteolytic and sclerotic lesions on CT images were classified as positive for LLOM, and anatomical data obtained from CT images were utilized to identify the precise location of lesions in SPECT/CT images. Following standard procedures, cases that were identified as positive on CT images and planar or SPECT images were considered SPECT/CT positive for LLOM as presented in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eIn addition, CE was evaluated for differentiation from LLOM visually using planar, SPECT, CT, and SPECT/CT images. Soft tissue density and accumulation in subdermal sites were analyzed using SPECT, CT, and SPECT/CT images. Higher than regular fat density and abnormal accumulation were considered positive for CE.\u003c/p\u003e \u003cp\u003eQuantitative analyses were performed on lesions suspected to be LLOM-related and CE-related using SPECT/CT data. Inflammation-to-background ratio (IBR) was calculated by dividing maximal count in each lesion accumulation by the mean count of accumulation in the bone marrow of both distal femurs. Furthermore, standardized uptake values (SUV) and total lesion uptake (TLU) were calculated using the quantitative analysis software, GI-BONE. Volume of interest (VOI) threshold was set at 50% of peak value, and maximum SUV (SUVmax) and TLU were calculated. IBR, SUVmax, and TLU cut-off values were determined using a receiver operation curve (ROC) analysis based on definitive diagnoses by primary physicians described above.\u003c/p\u003e \u003cp\u003eDiagnostic values, prognostic values, and visual and quantitative results were compared between the groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of prognosis\u003c/h2\u003e \u003cp\u003eAll patients were observed for five years after their initial lower-limb Ga-scintigraphy for the occurrence of major adverse events (MAE), which were defined as recurrence of LLOM, major amputation, or all-cause mortality. The endpoint for this study was defined as either the occurrence of MAE or end of the observation period. The correlation between the occurrence of MAE and various clinical parameters, including age, results of blood tests, risk factors, and comorbidities, was also analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eNormally distributed continuous variables were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and non-normally distributed variables as medians with 25th and 75th percentiles. Categorical variables were presented as percentages and counts. Non-normally distributed continuous variables, such as age, serum CRP levels, and IBR, were compared using the Mann-Whitney U-test.\u003c/p\u003e \u003cp\u003eCategorical variables were compared using Fisher\u0026rsquo;s exact probability test for bivariate data. In order to examine the correlation with future occurrence of MAE, all variables were checked using a univariate Cox regression analysis. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant, and the stepwise method was used to select variables for analysis. Multivariate Cox regression was performed on the top four significant variables to identify independently associated factors.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using StatMate IV software version 4.01 (Advanced Technology for Medicine and Science, Tokyo, Japan) and BellCurve for Excel software version 2.13 (Social Survey Research Information, Tokyo, Japan).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eClinical characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 103 patients (67 [55\u0026ndash;74] years, 76 men and 27 women) underwent lower-limb perfusion scintigraphy with quantitative SPECT/CT. Conventional planar images were obtained for 38 patients (68 [58\u0026ndash;73] years, 29 men and 9 women). Patient characteristics, including medical histories, comorbidities, and blood examination results, are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eOf the total, 54 patients were clinically diagnosed as having LLOM (LLOM group), while 49 were clinically diagnosed as not having LLOM (non-LLOM group). Table\u0026nbsp;3 shows patient characteristics for the two groups. Out of 54 patients in the LLOM group, 49 were clinically identified as having CE (LLOM-CE group) and 5 patients were clinically identified as having only LLOM (LLOM only group). In addition, 32 patients were clinically diagnosed with CE only (CE only group) and 17 patients were clinically diagnosed as negative for both LLOM and CE (negative group) (Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e-Visual assessment using Ga-scintigraphy\u003c/h2\u003e\n\u003cp\u003eBased on a visual assessment using planar images, 12 patients (71%) from the LLOM group and 14 patients (67%) from the non-LLOM group were rated as positive for LLOM. SPECT images identified 43 patients (79%) from the LLOM group and 32 patients (65%) from the non-LLOM group as positive for LLOM. CT images categorized 37 patients (69%) from the LLOM group and 4 patients (8%) from the non-LLOM group as positive for LLOM. SPECT/CT images identified 44 patients (81%) from the LLOM group and 4 patients (8%) from the non-LLOM group as positive for LLOM.\u003c/p\u003e\n\u003cp\u003eOut of the LLOM-CE group, 45 patients (92%), 34 patients (69%) and 39 patients (80%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively (Table\u0026nbsp;4). Out of the LLOM only group, 4 patients (80%), 3 patients (60%) and 4 patients (80%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively. Out of the CE only group, 32 patients (100%), 3 patients (9%) and 26 patients (81%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively. Out of the negative group, 8 patients (47%), 1 patient (6%) and 6 patients (35%) were rated as positive for LLOM based on planar, CT and SPECT images, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e-Quantitative assessment using Ga-scintigraphy\u003c/h2\u003e\n\u003cp\u003eBased on clinical diagnosis, IBR, SUVmax, and TLU for the LLOM group were 12.23 (7.38\u0026ndash;17.94), 4.85 (3.45\u0026ndash;8.31), and 68.77 (22.90\u0026ndash;96.63), respectively, and 1.00 (1.00\u0026ndash;1.47), 1.34 (1.14\u0026ndash;1.62), and 8.63 (1.15\u0026ndash;2.33), respectively, for the non-LLOM group (Table\u0026nbsp;3). The cut-off values for diagnosing LLOM were 1.99 for IBR, 1.74 for SUVmax, and 7.29 for TLU.\u003c/p\u003e\n\u003cp\u003eThe IBR, SUVmax, and TLU in the LLOM-CE group were 14.86 (8.91\u0026ndash;17.40), 6.36 (3.45\u0026ndash;8.35), and 69.80 (22.60\u0026ndash;96.99), respectively. The IBR, SUVmax, and TLU in the LLOM-only group were 9.13 (5.40\u0026ndash;8.87), 4.88 (2.02\u0026ndash;4.87), and 58.66 (35.02\u0026ndash;76.34), respectively. The IBR, SUVmax, and TLU in the CE-only group were 2.24 (1.00\u0026ndash;1.01), 1.56 (1.14\u0026ndash;1.58), and 8.51 (1.31\u0026ndash;2.36), respectively. The IBR, SUVmax, and TLU in the negative group were 1.86 (1.00\u0026ndash;1.01), 1.58 (1.14\u0026ndash;1.49), and 8.87 (1.09\u0026ndash;2.30), respectively. The results demonstrated statistically significant differences in IBR, SUVmax, and TLU between the LLOM-CE and CE-only groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all three quantitative parameters).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eAccuracy of imaging methods\u003c/h2\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;5, the sensitivity and specificity of the planar images were 71% and 33%, respectively. The sensitivity and specificity of the SPECT images were 80% and 35%, respectively. The sensitivity and specificity of the CT images were 69% and 92%, respectively. The sensitivity and specificity of SPECT/CT without quantitative analysis were 81% and 92%, respectively.\u003c/p\u003e\n\u003cp\u003eThe sensitivity and specificity of SPECT/CT with IBR were 91% and 96%, respectively. The sensitivity and specificity of SPECT/CT with SUVmax were 89% and 94%, respectively. The sensitivity and specificity of SPECT/CT with TLU were 91% and 92%, respectively. The areas under the ROC curves for the presence of LLOM were 0.957 using IBR, 0.921 using SUVmax, and 0.926 using TLU.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003ePatient prognoses\u003c/h2\u003e\n\u003cp\u003eMAE occurred in 23 patients with LLOM (43%). The area under the ROC curve for MAE occurrences was 0.680 for TLU, and the cut-off values for prognosis prediction were 38.35 for TLU. The prevalence of diabetes mellitus and chronic kidney disease as well as WBC, IBR, and TLU were statistically significantly higher among patients who experienced an MAE (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The results of the Cox proportional hazards regression analyses are presented in Table\u0026nbsp;7. The univariate analysis revealed significant correlations for WBC (p\u0026thinsp;=\u0026thinsp;0.002), diabetes mellitus (p\u0026thinsp;=\u0026thinsp;0.012), TLU (p\u0026thinsp;=\u0026thinsp;0.020), IBR (p\u0026thinsp;=\u0026thinsp;0.030), and chronic kidney disease (p\u0026thinsp;=\u0026thinsp;0.049). A multivariate analysis was performed for the top four parameters and demonstrated a statistically significant positive correlation between WBC and MAE (p\u0026thinsp;=\u0026thinsp;0.003) as well as TLU and MAE (p\u0026thinsp;=\u0026thinsp;0.047), while IBR showed no statistical significance (p\u0026thinsp;=\u0026thinsp;0.175).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eCase studies\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows a case of a patient with low TLU. This 68-year-old man developed a fever and increased inflammatory markers after treatment for severe leg trauma. Pretreatment Ga-scintigraphy was conducted, and planar images showed no clear signs of accumulation in the left toes. However, SPECT/CT images revealed increased subcutaneous density and accumulation, indicating CE, around the 4th distal phalanx of the left foot and destruction and mild accumulation in the bone, indicating LLOM. Quantitative analyses were performed using GI-BONE and showed a low SUVmax of 3.25, low IBR of 5.40, and low TLU of 35.02. Recovery from fever and inflammation was smooth, not requiring surgical treatment. This patient did not experience an MAE within the 3-year observation period.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows a case of a patient with high TLU. This 68-year-old man was treated for diabetic gangrene and underwent Ga-scintigraphy to confirm the diagnosis. SPECT/CT images revealed increased subcutaneous density and accumulation indicative of CE near the right 1st proximal phalanx and metatarsal, with bone destruction and distinct accumulation indicative of LLOM. Quantitative analyses using GI-BONE found a low SUVmax of 3.45, high IBR of 12.0, and high TLU of 133.76. Minor amputation was performed, and sequestrum in the affected areas during operation confirmed the diagnosis of LLOM. These findings indicated that the lesion had active chronic inflammation. Thirty-nine days after the initial Ga-scintigraphy, the patient experienced a fatal event.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the diagnostic accuracy and prognostic value of quantitative Ga-SPECT/CT for patients with LLOM by comparing it with other methods and clinical diagnoses.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Ga-SPECT/CT and other imaging modalities\u003c/h2\u003e \u003cp\u003eAn accurate diagnosis of LLOM is crucial for a favorable outcome. However, providing an accurate diagnosis remains a challenge for imaging modalities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Utilizing Ga accumulation in inflammatory cells, this study demonstrated that diagnoses with Ga-SPECT/CT using IBR achieved a diagnostic sensitivity and specificity of 91% and 96%, respectively. These results are an improvement over previous attempts lacking quantitative evaluation, which achieved 88% and 94% [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, diagnoses using SUVmax achieved a diagnostic sensitivity and specificity of 89% and 94%, respectively, showing no superiority over previous studies that only used visual evaluation.\u003c/p\u003e \u003cp\u003eSPECT was superior to CT in sensitivity (80% and 69%, respectively), while CT was superior to SPECT in specificity (92% and 35%, respectively). However, SPECT/CT was superior to both SPECT and CT in both dimensions, which may be due to the improvement of the contrast resolution of SPECT images through CT attenuation correction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, the synergistic effect of the fusion of SPECT and CT combines anatomical data obtained from CT with functional data obtained from SPECT [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, MRI is the most commonly used diagnostic modality for LLOM; however, its sensitivity and specificity for LLOM caused by diabetes mellitus were 93% and 75%, respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As such, MRI findings are not always sufficient to confidently diagnose LLOM. Noninfectious inflammatory and metabolic conditions of osseous tissue, bone contusions, stress fractures, healing fractures, osteonecrosis, and tumors can all produce signal alterations in some sequences similar to those seen in osteomyelitis, as MRI cannot differentiate edema from inflammation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, Ga-SPECT/CT appears to be more suitable for detecting LLOM and the associated inflammatory activity.\u003c/p\u003e \u003cp\u003eFDG-PET/CT has been found to have a sensitivity and specificity for detecting LLOM of 89% and 92%, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which is inferior to the performance of Ga-SPECT/CT with IBR observed in the present study. However, analyses utilizing FDG-PET/CT have not incorporated IBR to date. As such, future research on the possibility of using IBR with FDG-PET/CT may shed more light on this issue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison of quantitative evaluation methods\u003c/h2\u003e \u003cp\u003eThe results demonstrated the diagnostic significance of IBR, SUVmax, and TLU for LLOM; however, while TLU was positively correlated with prognosis, IBR and SUVmax were not statistically significant. This was likely due to the fact that SUVmax was based on a calculated distribution value including tissue concentration, injected dose, and body weight, resulting in data related to inflammation other than LLOM [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the other hand, IBR was based on a comparison of the affected tissue and mean count in the bone marrow of both distal femurs, avoiding confusion with other sites. Unlike SUV and TLU, IBR-based calculations were similar to a radiologists\u0026rsquo; visual interpretation. However, as measurement location, including background, was determined manually, IBR-based calculations could be vulnerable to error. On the other hand, TLU was a more objective measure and more accurate assessment of local inflammatory activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of Ga-SPECT/CT with quantitative parameters\u003c/h2\u003e \u003cp\u003eAs mentioned, Ga-SPECT/CT is generally not the preferred modality for LLOM in most countries; therefore, its prognostic value has not been investigated to date. The use of Ga-SPECT/CT in the literature has been limited. For instance, Aslangul et al. reported that combined diagnosis with Ga-SPECT/CT and percutaneous bone puncture improved the 1-year outcome of patients with LLOM (4 improved and 15 cured out of 55 patients) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the present study revealed the efficacy of Ga-SPECT/CT as a prognostic tool. The multivariate analysis revealed TLU to be an independent prognostic factor (p\u0026thinsp;=\u0026thinsp;0.047). The results demonstrated that prognosis was significantly poorer in patients with high TLU than those with low TLU.\u003c/p\u003e \u003cp\u003eSimilarly, the prognostic value of WBC-SPECT/CT is not well understood, with studies focusing on it as a diagnostic tool. For instance, Vouillarmet et al. reported that patients with positive WBC-SPECT/CT who underwent 12 weeks of medical therapy experienced high prevalence of LLOM relapse during the 1-year observation period (6 out of 13 patients) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Likewise, the prognostic value of FDG-PET/CT for LLOM has not been determined, as the modality is relatively new. However, FDG-PET/CT has been reported to improve LLOM diagnosis and therapeutic monitoring and effects [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, surgery based on FDG-PET/CT images using SUV cut-off values of 2.00\u0026ndash;8.00 has a higher potential for procedural success [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This suggests that FDG-PET/CT is likely to have a good prognostic value.\u003c/p\u003e \u003cp\u003eHowever, as mentioned, FDG-PET/CT and WBC-SPECT/CT generally cannot be used in Japan due to technical and insurance limitations. Therefore, Ga-SPECT/CT presents the best available method with a potential for high prognostic value. Chronic osteomyelitis entails a major financial burden and a substantial impact on the quality of life, including both mental and physical aspects [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Providing an accurate prognosis would allow early intervention and mitigate some of the mental, physical, and financial burdens. This study indicated that quantitative assessment is more precise than visual assessment and enables prognosis stratification. The results provided strong evidence for recommending the utilization of Ga-SPECT/CT for patients with LLOM, at least in countries where FDG-PET/CT is not available or feasible. Future research should investigate this method across Japan and in other countries in order to lend further validity to these results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePossible interference of CE\u003c/h2\u003e \u003cp\u003eLLOM and CE are frequently difficult to distinguish, as both diseases are caused by infection and the inflammatory sites are in proximity to each other. Due to the possible interference of CE, an analysis was conducted to evaluate differences in quantitative parameters based on the presence of CE. The results demonstrated significant differences in IBR, SUVmax, and TLU between LLOM-CE and CE only. There were no significant differences in IBR, SUVmax, or TLU between the subgroups with and without CE in the non-LLOM group. These results suggest that there is no effect of the presence or absence of CE on quantitative analyses of LLOM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eThis study had some limitations. First, due to the retrospective design of the study, there may be a bias in case selection caused by the initial focus on the indication for surgery. Moreover, for the same reason, clinical examinations may not have been optimized for LLOM, such as injection-to-scan acquisition times. Future research should conduct multicenter randomized controlled trials in order to eliminate this potential bias. Second, Ga-SPECT/CT was chosen over FDG-PET/CT, as Japan\u0026rsquo;s national health insurance system only covers Ga-scintigraphy for patients with LLOM; therefore, FDG-PET/CT data could not be acquired. As FDG-PET/CT has higher spatial resolution and sensitivity, a lower radiation burden, and a significantly shorter acquisition time compared with Ga-SPECT/CT, it may produce superior results [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Further research should explore this hypothesis. Furthermore, the sample size of the present study was relatively small. Future studies should aim to include a wider range of participants.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study evaluated inflammatory activity in patients with LLOM using quantitative Ga-SPECT/CT. The results indicated that Ga-SPECT/CT using quantitative parameters, namely, IBR, SUVmax, and TLU, had a better diagnostic performance for patients with LLOM compared to planar imaging. In addition, this study found that TLU values were positively correlated with MAE, demonstrating the prognostic assessment potential of Ga-SPECT/CT with TLU, including the ability to stratify the prognosis of patients with LLOM.\u003c/p\u003e \u003cp\u003eThe results suggest that Ga-SPECT/CT is a good alternative for diagnosing LLOM in countries where FDG-PET/CT is not commonly available. Future studies should conduct further analyses across a range of populations. It should be noted that although Ga-SPECT/CT is an acceptable alternative, most physicians agree that FDG-PET/CT is superior [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, future policies should strive to allow the implementation of FDG-PET/CT for LLOM whenever possible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis paper is a single-center retrospective study on LLOM patients from one university hospital (Nippon Medical School Hospital, Tokyo, Japan). Written informed consent was obtained from all participants prior to performing the scan (all participants are legal adults). The study protocol was approved by the institutional ethics committee and classified as a non-interventional study.\u003c/p\u003e\n\u003cp\u003eAll procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committees and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the Ethics Committee of Nippon Medical School Hospital (approval no. B-2019-061).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eWe followed the retrospective observational research information disclosure procedure (opt-out) of Nippon Medical School when obtaining informed consent from research participants, including permissions to publish research results at conferences and in academic journals. The use of opt-out consent is approved by the Ethics Committee of Nippon Medical School Hospital. The option to opt-out is detailed on the hospital\u0026rsquo;s website.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding was required during the current study; therefore the authors declare no sources of funding for this research.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; Contributions\u003c/h2\u003e\n\u003cp\u003eYN, YF, SKu, GT, and SKi were involved in study design and data interpretation. YN, YF, MS, and TM were involved in the data analysis. All authors critically revised the report, commented on drafts of the manuscript, and approved the final report.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to extend our gratitude to radiology technologists Kyoji Asano and Shinjiro Yoshida for their work with the administration of Ga-SPECT/CT. We would also like to thank the primary physicians for providing care and obtaining the data for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLew DP, Waldvogel FA, Osteomyelitis (1997) N Engl J Med 336:999\u0026ndash;1007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLew DP, Waldvogel FA (2004) Osteomyelitis The Lancet 364:369\u0026ndash;379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalter G, Kemmerer M, Kappler C, Hoffmann R (2012) Treatment algorithms for chronic osteomyelitis. Dtsch Arztebl Int 109:257\u0026ndash;264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKremers HM, Nwojo ME, Ransom JE, Wood-Wentz CM, Joseph Melton L, Huddleston PM (2014) Trends in the epidemiology of osteomyelitis a population-based study, 1969 to 2009. J Bone Jt Surg-Am 97:837\u0026ndash;845\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipsky BA, Berendt AR, Cornia PB et al (2012) 2012 infectious diseases society of America clinical practice guideline for the diagnosis and treatment of diabetic foot infections. Clin Infect Dis 54:132\u0026ndash;173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHingorani A, LaMuraglia GM, Henke P et al (2016) The management of diabetic foot: A clinical practice guideline by the society for vascular surgery in collaboration with the american podiatric medical association and the society for vascular medicine. J Vasc Surg 63:3S\u0026ndash;21S\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedras S, Vilhena E, Carvalho R, Pereira MG (2020) Quality of life following a lower limb amputation in diabetic patients: A longitudinal and multicenter study. Psychiatry 83:47\u0026ndash;57\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia Del Pozo E, Collazos J, Carton JA, Camporro D, Asensi V (2018) Factors predictive of relapse in adult bacterial osteomyelitis of long bones. BMC Infect Dis 18:635\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExpert Panel on, Musculoskeletal I, Beaman FD, von Herrmann PF et al (2017) Acr appropriateness criteria((r)) suspected osteomyelitis, septic arthritis, or soft tissue infection (excluding spine and diabetic foot). J Am Coll Radiol 14:S326\u0026ndash;S337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipsky BA, Senneville E, Abbas ZG et al (2020) Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev 36(Suppl 1S1):e3280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFayad LM, Carrino JA, Fishman EK (2007) Musculoskeletal infection: Role of CT in the emergency department. Radiographics 27:1723\u0026ndash;1736\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTermaat MF, Raijmakers PG, Scholten HJ, Bakker FC, Patka P, Haarman HJ (2005) The accuracy of diagnostic imaging for the assessment of chronic osteomyelitis: A systematic review and meta-analysis. J Bone Jt Surg-Am 87:2464\u0026ndash;2471\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwegler B, Stumpe KD, Weishaupt D et al (2008) Unsuspected osteomyelitis is frequent in persistent diabetic foot ulcer and better diagnosed by MRI than by \u003csup\u003e18\u003c/sup\u003eF-FDG PET or \u003csup\u003e99m\u003c/sup\u003eTc-MOAB. J Intern Med 263:99\u0026ndash;106\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLauri C, Tamminga M, Glaudemans AWJM, Ju\u0026aacute;rez Orozco LE, Erba PA, Jutte PC et al (2017) Detection of Osteomyelitis in the Diabetic Foot by Imaging Techniques: A Systematic Review and Meta-analysis Comparing MRI, White Blood Cell Scintigraphy, and FDG-PET. Diabetes Care 40:1111\u0026ndash;1120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker W (1999) Imaging osteomyelitis and the diabetic foot. Q J Nucl Med 43:9\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsan MF (1985) Mechanism of gallium-67 accumulation in inflammatory lesions. J Nucl Med 26:88\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffer PB, Huberty J, Khayam-Bashi H (1977) The association of Ga-67 and lactoferrin. J Nucl Med 18:713\u0026ndash;717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove C, Palestro CJ (2016) Nuclear medicine imaging of bone infections. Clin Radiol 71:632\u0026ndash;646\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelcourt A, Huglo D, Prangere T et al (2005) Comparison between leukoscan (sulesomab) and gallium-67 for the diagnosis of osteomyelitis in the diabetic foot. Diabetes Metab 31:125\u0026ndash;133\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorger M, Eschmann SM, Pfannenberg C et al (2003) The value of SPET/CT in chronic osteomyelitis. Eur J Nucl Med Mol Imaging 30:1665\u0026ndash;1673\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar-Shalom R, Yefremov N, Guralnik L et al (2005) SPECT/CT using \u003csup\u003e67\u003c/sup\u003eGa and \u003csup\u003e11\u003c/sup\u003eIn-labeled leukocyte scintigraphy for diagnosis of infection. J Nucl Med 47:587\u0026ndash;594\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGovaert GA, FF IJ, McNally M, McNally E, Reininga IH, Glaudemans AW (2017) Accuracy of diagnostic imaging modalities for peripheral post-traumatic osteomyelitis - a systematic review of the recent literature. Eur J Nucl Med Mol Imaging 44:1393\u0026ndash;1407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKagna O, Srour S, Melamed E, Militianu D, Keidar Z (2012) FDG-PET/CT imaging in the diagnosis of osteomyelitis in the diabetic foot. Eur J Nucl Med Mol Imaging 39:1545\u0026ndash;1550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgura I, Kobayashi E, Nakahara K, Igarashi K, Haga-Tsujimura M, Toshima H (2019) Quantitative SPECT/CT imaging for medication-related osteonecrosis of the jaw: A preliminary study using volume-based parameters, comparison with chronic osteomyelitis. Ann Nucl Med 33:776\u0026ndash;782\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHata H, Kitao T, Sato J et al (2020) Monitoring indices of bone inflammatory activity of the jaw using SPECT bone scintigraphy: a study of ARONJ patients. Sci Rep 10:1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein DA, Lee BH, Bezhani H, Droukas DD, Stoffels G (2020) The Clinical Utility of MRI in Evaluating for Osteomyelitis in Patients Presenting with Uncomplicated Cellulitis. J Foot Ankle Surg 59(2):323\u0026ndash;329\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerendt AR, Peters EJG, Bakker K et al (2008) Diabetic foot osteomyelitis: A progress report on diagnosis and a systematic review of treatment. Diab/Metab Res Rev 24:S145\u0026ndash;S161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAslangul E, M'Bemba J, Caillat-Vigneron N et al (2013) Diagnosing diabetic foot osteomyelitis in patients without signs of soft tissue infection by coupling hybrid \u003csup\u003e67\u003c/sup\u003eGa-SPECT/CT with bedside percutaneous bone puncture. Diabetes Care 36:2203\u0026ndash;2210\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo Y, Wong KH, Sun M, Franc BL, Hawkins RA, Hasegawa BH (2005) Correction of photon attenuation and collimator response for a body-contouring SPECT/CT imaging system. J Nucl Med 46:868\u0026ndash;877\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujii M, Armsrong DG, Terashi H (2013) Efficacy of magnetic resonance imaging in diagnosing diabetic foot osteomyelitis in the presence of ischemia. J Foot Ankle Surg 52:717\u0026ndash;723\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakaki M, Takenaka N, Mori K et al (2020) Comparison of histopathology and preoperative \u003csup\u003e18\u003c/sup\u003eF-FDG-PET/CT of osteomyelitis aiming for image guided surgery: A preliminary trial. Injury 51:871\u0026ndash;877\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVouillarmet J, Moret M, Morelec I, Michon P, Dubreuil J (2017) Application of white blood cell SPECT/CT to predict remission after a 6 or 12 week course of antibiotic treatment for diabetic foot osteomyelitis. Diabetologia 60:2486\u0026ndash;2494\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatziioannou S, Papamichos O, Gamaletsou MN, Georgakopoulos A, Kostomitsopoulos NG, Tseleni-Balafouta S et al (2015) 18-Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography scan for monitoring the therapeutic response in experimental Staphylococcus aureus foreign-body osteomyelitis. J Orthop Surg Res 10:132\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u003c/strong\u003eProcedure to identify LLOM positive cases using visual assessments\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eModality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"74.91638795986623%\"\u003e\n \u003cp\u003eImage findings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"74.91638795986623%\"\u003e\n \u003cp\u003eOsteolytic and sclerotic lesions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003ePlanar imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"74.91638795986623%\"\u003e\n \u003cp\u003eHigher accumulation than background\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eSPECT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"74.91638795986623%\"\u003e\n \u003cp\u003eHigher accumulation than background muscle tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.08361204013378%\"\u003e\n \u003cp\u003eSPECT/CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"74.91638795986623%\"\u003e\n \u003cp\u003eOsteolytic and sclerotic lesions with higher accumulation\u0026nbsp;\u003cbr\u003e\u0026nbsp;than background muscle tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u003c/strong\u003ePatient characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"93%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e67 (55\u0026ndash;74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eMale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e76 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eBlood exam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; WBC (/\u0026mu;l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e6200 (5150\u0026ndash;7750)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; CRP (mg/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e1.12 (0.28\u0026ndash;3.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eRisk factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Diabetes mellitus (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e77 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Peripheral artery disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e64 (62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Cellulitis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e81 (79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Hypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e52 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Chronic kidney disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e49 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73.73737373737374%\"\u003e\n \u003cp\u003e\u0026nbsp; Coronary artery disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\"\u003e\n \u003cp\u003e24 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWBC = white blood cell; CRP = C-reactive protein\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u003c/strong\u003eComparison of clinical profiles between LLOM and non-LLOM groups\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLLOM (n = 54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-LLOM (n = 49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e68 (61\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e64 (55\u0026ndash;74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e37 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e39 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood exam\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;WBC (/\u0026mu;l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e6250 (5100\u0026ndash;7575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e6200 (5400\u0026ndash;8000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;CRP (mg/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e1.12 (0.24\u0026ndash;3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e1.62 (0.28\u0026ndash;3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factor/comorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Diabetes mellitus (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e37 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e40 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Peripheral artery disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e31 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e33 (67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Cellulitis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e49 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e32 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Hypertension (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e27 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e25 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Chronic kidney disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e21 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e28 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Coronary artery disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e10 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e14 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003eImaging findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Positive in planar imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e12 (71%; n=17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e14 (67%; n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Positive in SPECT/CT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e44 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e4 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;IBR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e12.23 (7.38\u0026ndash;17.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;SUVmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e4.85 (3.45\u0026ndash;8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e1.34 (1.14\u0026ndash;1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.90853658536585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;TLU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.323170731707318%\"\u003e\n \u003cp\u003e68.77 (22.90\u0026ndash;96.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.23780487804878%\"\u003e\n \u003cp\u003e8.63 (1.15\u0026ndash;2.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.53048780487805%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLLOM = lower-limb osteomyelitis; SPECT/CT = single photon emission computed tomography/computed tomography; SUV = standardized uptake value; IBR = inflammation-to-background ratio; TLU = total lesion uptake\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u003c/strong\u003eResults based on the presence of LLOM and CE\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003eLLOM-CE\u003cbr\u003e\u0026nbsp;(n = 49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003eLLOM only\u003cbr\u003e\u0026nbsp;(n = 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003eCE only\u003cbr\u003e\u0026nbsp;(n = 32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003eLLOM-CE negative\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n = 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eVisual assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eCT positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e34 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e3 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e1 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eSPECT positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e39 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e4 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e26 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e6 (35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eQuantitative assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eIBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e14.86\u003cbr\u003e\u0026nbsp; (8.91\u0026ndash;17.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e9.13\u0026nbsp;\u003cbr\u003e\u0026nbsp;(5.40\u0026ndash;8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e2.24\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.00\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e1.86\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.00\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e6.36\u003cbr\u003e\u0026nbsp; (3.45\u0026ndash;8.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e4.88\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2.02\u0026ndash;4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e1.56\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.14\u0026ndash;1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e1.58\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.14\u0026ndash;1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.16984402079723%\"\u003e\n \u003cp\u003eTLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e69.80\u0026nbsp;\u003cbr\u003e\u0026nbsp;(22.60\u0026ndash;96.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.71750433275563%\"\u003e\n \u003cp\u003e58.66\u0026nbsp;\u003cbr\u003e\u0026nbsp;(35.02\u0026ndash;76.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.370883882149048%\"\u003e\n \u003cp\u003e8.51\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.31\u0026ndash;2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"18.02426343154246%\"\u003e\n \u003cp\u003e8.87\u0026nbsp;\u003cbr\u003e\u0026nbsp;(1.09\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCE = cellulitis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 \u003c/strong\u003eDiagnostic accuracy of imaging modalities\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlanar imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPECT imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPECT/CT imaging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuantitative assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIBR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUVmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.53781512605042%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.991596638655462%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.478991596638654%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u003c/strong\u003eClinical profiles of patients with LLOM divided by MAE occurence\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAE (n = 23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo MAE (n = 31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e66 (58\u0026ndash;69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e68 (61\u0026ndash;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e16 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e21 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood exam\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;WBC (/\u0026mu;l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e6800 (5900\u0026ndash;8800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e5600 (4800\u0026ndash;6550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;CRP (mg/l)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e2.66 (0.30\u0026ndash;3.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e0.79 (0.26\u0026ndash;2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk factor/comorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Diabetes mellitus (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e21 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e16 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Peripheral artery disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e14 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e17 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Cellulitis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e22 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e27 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Hypertension (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e13 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e14 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Chronic kidney disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e12 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e9 (29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Coronary artery disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e6 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e7 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eImaging findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;IBR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e18.39 (9.88\u0026ndash;17.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e11.31 (1.00\u0026ndash;17.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;SUVmax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e6.75 (3.45\u0026ndash;12.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e5.83 (2.97\u0026ndash;7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;TLU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e89.83 (48.00\u0026ndash;136.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003e35.02 (19.28\u0026ndash;78.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.52066115702479%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent-free survival (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.611570247933884%\"\u003e\n \u003cp\u003e19 (5.5\u0026ndash;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.958677685950413%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMAE = major adverse event\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7 \u003c/strong\u003eUnivariate and multivariate Cox regression for MAE occurrence\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"36.37820512820513%\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"36.37820512820513%\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.955\u0026ndash;1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.415\u0026ndash;2.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eBlood exam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;WBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.000\u0026ndash;1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.988\u0026ndash;1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eRisk factor/comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e6.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.508\u0026ndash;27.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e4.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.921\u0026ndash;18.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Peripheral artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.516\u0026ndash;2.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Cellulitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e2.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.366\u0026ndash;20.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.610\u0026ndash;3.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Chronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e2.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.004\u0026ndash;5.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;Coronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.465\u0026ndash;3.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eImaging findings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;IBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.004\u0026ndash;1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.987\u0026ndash;1.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003e\u0026nbsp;SUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e0.956\u0026ndash;1.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.134615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.576923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.243589743589745%\"\u003e\n \u003cp\u003eTLU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.134615384615385%\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e1.000\u0026ndash;1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.576923076923077%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHR = hazard ratio; CI = confidential interval\u003c/p\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":"european-journal-of-hybrid-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejhi","sideBox":"Learn more about [European Journal of Hybrid Imaging](http://ejhi.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejhi/default.aspx","title":"European Journal of Hybrid Imaging","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SPECT/CT, quantitative analysis, osteomyelitis, diagnostic performance, prognostic value","lastPublishedDoi":"10.21203/rs.3.rs-1835166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1835166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with lower-limb osteomyelitis (LLOM) may experience major adverse events, such as lower-leg amputations or death; therefore, early diagnosis and risk stratification are essential to improve outcomes. Ga-scintigraphy is commonly used for diagnosing inflammatory diseases. Until fairly recently, conventional imaging and SPECT were the most common; however, the diagnostic performance of planar and SPECT imaging for localized lesions is limited. While localized imaging using Ga-SPECT/CT is an emerging approach to improve diagnoses, its diagnostic performance has not been sufficiently evaluated to date. Therefore, this study aimed to evaluate the diagnostic performance of Ga-SPECT/CT with quantitative analyses for patients with LLOM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 103 consecutive patients suspected of LLOM between April 2012 and October 2016 were analyzed. All patients underwent Ga-scintigraphy with SPECT/CT imaging. Findings were assessed visually, with higher than background accumulation considered positive, and quantitatively, using Ga-SPECT/CT images to calculate the inflammation-to-background ratio (IBR), the maximum standardized uptake value (SUVmax), and total lesion uptake (TLU). Diagnoses were confirmed using pathological examinations and patient outcomes, and diagnostic performances of planar, SPECT, and SPECT/CT images were compared. To evaluate prognostic performance, all patients were observed for 5 years for occurrences of major adverse events (MAE), defined as recurrence of osteomyelitis, major leg amputation, or fatal event. Multivariate Cox regression was performed to evaluate outcome factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall diagnoses indicated that 54 out of 103 patients had LLOM. IBR, SUVmax, and TLU were significantly higher in patients with LLOM (12.23 vs. 1.00, 4.85 vs. 1.34, and 68.77 vs. 8.63, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sensitivity and specificity were 91% and 96% for SPECT/CT with IBR, 89% and 94% for SPECT/CT with SUVmax, and 91% and 92% for SPECT/CT with TLU, respectively. MAE occurred in 23 of 54 LLOM patients (43%). TLU was found to be an independent prognostic factor (p\u0026thinsp;=\u0026thinsp;0.047).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGa-SPECT/CT using quantitative parameters, namely, IBR and TLU, had better diagnostic and prognostic performances for patients with LLOM compared to conventional imaging. The results suggest that Ga-SPECT/CT is a good alternative for diagnosing LLOM in countries where FDG-PET/CT is not commonly available.\u003c/p\u003e","manuscriptTitle":"Diagnostic Performance of Quantitative Ga-SPECT/CT for Patients with Lower-limb Osteomyelitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-19 18:55:48","doi":"10.21203/rs.3.rs-1835166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-07-12T09:35:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-12T08:58:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-12T08:40:20+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-07-12T01:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2022-07-12T00:00:00+00:00","index":1,"fulltext":""},{"type":"checksComplete","content":"","date":"2022-07-11T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-07-11T23:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Hybrid Imaging","date":"2022-07-07T08:11:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-hybrid-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejhi","sideBox":"Learn more about [European Journal of Hybrid Imaging](http://ejhi.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejhi/default.aspx","title":"European Journal of Hybrid Imaging","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a36f329-d68f-416d-bb19-34c76c3e145f","owner":[],"postedDate":"July 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-30T08:05:35+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-19 18:55:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1835166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1835166","identity":"rs-1835166","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}